GPU-based cone beam computed tomography.

PubMed

Noël, Peter B; Walczak, Alan M; Xu, Jinhui; Corso, Jason J; Hoffmann, Kenneth R; Schafer, Sebastian

2010-06-01

The use of cone beam computed tomography (CBCT) is growing in the clinical arena due to its ability to provide 3D information during interventions, its high diagnostic quality (sub-millimeter resolution), and its short scanning times (60 s). In many situations, the short scanning time of CBCT is followed by a time-consuming 3D reconstruction. The standard reconstruction algorithm for CBCT data is the filtered backprojection, which for a volume of size 256(3) takes up to 25 min on a standard system. Recent developments in the area of Graphic Processing Units (GPUs) make it possible to have access to high-performance computing solutions at a low cost, allowing their use in many scientific problems. We have implemented an algorithm for 3D reconstruction of CBCT data using the Compute Unified Device Architecture (CUDA) provided by NVIDIA (NVIDIA Corporation, Santa Clara, California), which was executed on a NVIDIA GeForce GTX 280. Our implementation results in improved reconstruction times from minutes, and perhaps hours, to a matter of seconds, while also giving the clinician the ability to view 3D volumetric data at higher resolutions. We evaluated our implementation on ten clinical data sets and one phantom data set to observe if differences occur between CPU and GPU-based reconstructions. By using our approach, the computation time for 256(3) is reduced from 25 min on the CPU to 3.2 s on the GPU. The GPU reconstruction time for 512(3) volumes is 8.5 s. Copyright 2009 Elsevier Ireland Ltd. All rights reserved.

The development of GPU-based parallel PRNG for Monte Carlo applications in CUDA Fortran

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kargaran, Hamed, E-mail: h-kargaran@sbu.ac.ir; Minuchehr, Abdolhamid; Zolfaghari, Ahmad

The implementation of Monte Carlo simulation on the CUDA Fortran requires a fast random number generation with good statistical properties on GPU. In this study, a GPU-based parallel pseudo random number generator (GPPRNG) have been proposed to use in high performance computing systems. According to the type of GPU memory usage, GPU scheme is divided into two work modes including GLOBAL-MODE and SHARED-MODE. To generate parallel random numbers based on the independent sequence method, the combination of middle-square method and chaotic map along with the Xorshift PRNG have been employed. Implementation of our developed PPRNG on a single GPU showedmore » a speedup of 150x and 470x (with respect to the speed of PRNG on a single CPU core) for GLOBAL-MODE and SHARED-MODE, respectively. To evaluate the accuracy of our developed GPPRNG, its performance was compared to that of some other commercially available PPRNGs such as MATLAB, FORTRAN and Miller-Park algorithm through employing the specific standard tests. The results of this comparison showed that the developed GPPRNG in this study can be used as a fast and accurate tool for computational science applications.« less

Evaluation of the OSC-TV iterative reconstruction algorithm for cone-beam optical CT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Matenine, Dmitri, E-mail: dmitri.matenine.1@ulaval.ca; Mascolo-Fortin, Julia, E-mail: julia.mascolo-fortin.1@ulaval.ca; Goussard, Yves, E-mail: yves.goussard@polymtl.ca

Purpose: The present work evaluates an iterative reconstruction approach, namely, the ordered subsets convex (OSC) algorithm with regularization via total variation (TV) minimization in the field of cone-beam optical computed tomography (optical CT). One of the uses of optical CT is gel-based 3D dosimetry for radiation therapy, where it is employed to map dose distributions in radiosensitive gels. Model-based iterative reconstruction may improve optical CT image quality and contribute to a wider use of optical CT in clinical gel dosimetry. Methods: This algorithm was evaluated using experimental data acquired by a cone-beam optical CT system, as well as complementary numericalmore » simulations. A fast GPU implementation of OSC-TV was used to achieve reconstruction times comparable to those of conventional filtered backprojection. Images obtained via OSC-TV were compared with the corresponding filtered backprojections. Spatial resolution and uniformity phantoms were scanned and respective reconstructions were subject to evaluation of the modulation transfer function, image uniformity, and accuracy. The artifacts due to refraction and total signal loss from opaque objects were also studied. Results: The cone-beam optical CT data reconstructions showed that OSC-TV outperforms filtered backprojection in terms of image quality, thanks to a model-based simulation of the photon attenuation process. It was shown to significantly improve the image spatial resolution and reduce image noise. The accuracy of the estimation of linear attenuation coefficients remained similar to that obtained via filtered backprojection. Certain image artifacts due to opaque objects were reduced. Nevertheless, the common artifact due to the gel container walls could not be eliminated. Conclusions: The use of iterative reconstruction improves cone-beam optical CT image quality in many ways. The comparisons between OSC-TV and filtered backprojection presented in this paper demonstrate that OSC-TV can potentially improve the rendering of spatial features and reduce cone-beam optical CT artifacts.« less

Evaluation of the OSC-TV iterative reconstruction algorithm for cone-beam optical CT.

PubMed

Matenine, Dmitri; Mascolo-Fortin, Julia; Goussard, Yves; Després, Philippe

2015-11-01

The present work evaluates an iterative reconstruction approach, namely, the ordered subsets convex (OSC) algorithm with regularization via total variation (TV) minimization in the field of cone-beam optical computed tomography (optical CT). One of the uses of optical CT is gel-based 3D dosimetry for radiation therapy, where it is employed to map dose distributions in radiosensitive gels. Model-based iterative reconstruction may improve optical CT image quality and contribute to a wider use of optical CT in clinical gel dosimetry. This algorithm was evaluated using experimental data acquired by a cone-beam optical CT system, as well as complementary numerical simulations. A fast GPU implementation of OSC-TV was used to achieve reconstruction times comparable to those of conventional filtered backprojection. Images obtained via OSC-TV were compared with the corresponding filtered backprojections. Spatial resolution and uniformity phantoms were scanned and respective reconstructions were subject to evaluation of the modulation transfer function, image uniformity, and accuracy. The artifacts due to refraction and total signal loss from opaque objects were also studied. The cone-beam optical CT data reconstructions showed that OSC-TV outperforms filtered backprojection in terms of image quality, thanks to a model-based simulation of the photon attenuation process. It was shown to significantly improve the image spatial resolution and reduce image noise. The accuracy of the estimation of linear attenuation coefficients remained similar to that obtained via filtered backprojection. Certain image artifacts due to opaque objects were reduced. Nevertheless, the common artifact due to the gel container walls could not be eliminated. The use of iterative reconstruction improves cone-beam optical CT image quality in many ways. The comparisons between OSC-TV and filtered backprojection presented in this paper demonstrate that OSC-TV can potentially improve the rendering of spatial features and reduce cone-beam optical CT artifacts.

The Research and Test of Fast Radio Burst Real-time Search Algorithm Based on GPU Acceleration

NASA Astrophysics Data System (ADS)

Wang, J.; Chen, M. Z.; Pei, X.; Wang, Z. Q.

2017-03-01

In order to satisfy the research needs of Nanshan 25 m radio telescope of Xinjiang Astronomical Observatory (XAO) and study the key technology of the planned QiTai radio Telescope (QTT), the receiver group of XAO studied the GPU (Graphics Processing Unit) based real-time FRB searching algorithm which developed from the original FRB searching algorithm based on CPU (Central Processing Unit), and built the FRB real-time searching system. The comparison of the GPU system and the CPU system shows that: on the basis of ensuring the accuracy of the search, the speed of the GPU accelerated algorithm is improved by 35-45 times compared with the CPU algorithm.

A fast GPU-based Monte Carlo simulation of proton transport with detailed modeling of nonelastic interactions.

PubMed

Wan Chan Tseung, H; Ma, J; Beltran, C

2015-06-01

Very fast Monte Carlo (MC) simulations of proton transport have been implemented recently on graphics processing units (GPUs). However, these MCs usually use simplified models for nonelastic proton-nucleus interactions. Our primary goal is to build a GPU-based proton transport MC with detailed modeling of elastic and nonelastic proton-nucleus collisions. Using the cuda framework, the authors implemented GPU kernels for the following tasks: (1) simulation of beam spots from our possible scanning nozzle configurations, (2) proton propagation through CT geometry, taking into account nuclear elastic scattering, multiple scattering, and energy loss straggling, (3) modeling of the intranuclear cascade stage of nonelastic interactions when they occur, (4) simulation of nuclear evaporation, and (5) statistical error estimates on the dose. To validate our MC, the authors performed (1) secondary particle yield calculations in proton collisions with therapeutically relevant nuclei, (2) dose calculations in homogeneous phantoms, (3) recalculations of complex head and neck treatment plans from a commercially available treatment planning system, and compared with (GEANT)4.9.6p2/TOPAS. Yields, energy, and angular distributions of secondaries from nonelastic collisions on various nuclei are in good agreement with the (GEANT)4.9.6p2 Bertini and Binary cascade models. The 3D-gamma pass rate at 2%-2 mm for treatment plan simulations is typically 98%. The net computational time on a NVIDIA GTX680 card, including all CPU-GPU data transfers, is ∼ 20 s for 1 × 10(7) proton histories. Our GPU-based MC is the first of its kind to include a detailed nuclear model to handle nonelastic interactions of protons with any nucleus. Dosimetric calculations are in very good agreement with (GEANT)4.9.6p2/TOPAS. Our MC is being integrated into a framework to perform fast routine clinical QA of pencil-beam based treatment plans, and is being used as the dose calculation engine in a clinically applicable MC-based IMPT treatment planning system. The detailed nuclear modeling will allow us to perform very fast linear energy transfer and neutron dose estimates on the GPU.

GPU real-time processing in NA62 trigger system

NASA Astrophysics Data System (ADS)

Ammendola, R.; Biagioni, A.; Chiozzi, S.; Cretaro, P.; Di Lorenzo, S.; Fantechi, R.; Fiorini, M.; Frezza, O.; Lamanna, G.; Lo Cicero, F.; Lonardo, A.; Martinelli, M.; Neri, I.; Paolucci, P. S.; Pastorelli, E.; Piandani, R.; Piccini, M.; Pontisso, L.; Rossetti, D.; Simula, F.; Sozzi, M.; Vicini, P.

2017-01-01

A commercial Graphics Processing Unit (GPU) is used to build a fast Level 0 (L0) trigger system tested parasitically with the TDAQ (Trigger and Data Acquisition systems) of the NA62 experiment at CERN. In particular, the parallel computing power of the GPU is exploited to perform real-time fitting in the Ring Imaging CHerenkov (RICH) detector. Direct GPU communication using a FPGA-based board has been used to reduce the data transmission latency. The performance of the system for multi-ring reconstrunction obtained during the NA62 physics run will be presented.

GPU-based Branchless Distance-Driven Projection and Backprojection

PubMed Central

Liu, Rui; Fu, Lin; De Man, Bruno; Yu, Hengyong

2017-01-01

Projection and backprojection operations are essential in a variety of image reconstruction and physical correction algorithms in CT. The distance-driven (DD) projection and backprojection are widely used for their highly sequential memory access pattern and low arithmetic cost. However, a typical DD implementation has an inner loop that adjusts the calculation depending on the relative position between voxel and detector cell boundaries. The irregularity of the branch behavior makes it inefficient to be implemented on massively parallel computing devices such as graphics processing units (GPUs). Such irregular branch behaviors can be eliminated by factorizing the DD operation as three branchless steps: integration, linear interpolation, and differentiation, all of which are highly amenable to massive vectorization. In this paper, we implement and evaluate a highly parallel branchless DD algorithm for 3D cone beam CT. The algorithm utilizes the texture memory and hardware interpolation on GPUs to achieve fast computational speed. The developed branchless DD algorithm achieved 137-fold speedup for forward projection and 188-fold speedup for backprojection relative to a single-thread CPU implementation. Compared with a state-of-the-art 32-thread CPU implementation, the proposed branchless DD achieved 8-fold acceleration for forward projection and 10-fold acceleration for backprojection. GPU based branchless DD method was evaluated by iterative reconstruction algorithms with both simulation and real datasets. It obtained visually identical images as the CPU reference algorithm. PMID:29333480

GPU-based Branchless Distance-Driven Projection and Backprojection.

PubMed

Liu, Rui; Fu, Lin; De Man, Bruno; Yu, Hengyong

2017-12-01

Projection and backprojection operations are essential in a variety of image reconstruction and physical correction algorithms in CT. The distance-driven (DD) projection and backprojection are widely used for their highly sequential memory access pattern and low arithmetic cost. However, a typical DD implementation has an inner loop that adjusts the calculation depending on the relative position between voxel and detector cell boundaries. The irregularity of the branch behavior makes it inefficient to be implemented on massively parallel computing devices such as graphics processing units (GPUs). Such irregular branch behaviors can be eliminated by factorizing the DD operation as three branchless steps: integration, linear interpolation, and differentiation, all of which are highly amenable to massive vectorization. In this paper, we implement and evaluate a highly parallel branchless DD algorithm for 3D cone beam CT. The algorithm utilizes the texture memory and hardware interpolation on GPUs to achieve fast computational speed. The developed branchless DD algorithm achieved 137-fold speedup for forward projection and 188-fold speedup for backprojection relative to a single-thread CPU implementation. Compared with a state-of-the-art 32-thread CPU implementation, the proposed branchless DD achieved 8-fold acceleration for forward projection and 10-fold acceleration for backprojection. GPU based branchless DD method was evaluated by iterative reconstruction algorithms with both simulation and real datasets. It obtained visually identical images as the CPU reference algorithm.

Efficient implementation of the 3D-DDA ray traversal algorithm on GPU and its application in radiation dose calculation.

PubMed

Xiao, Kai; Chen, Danny Z; Hu, X Sharon; Zhou, Bo

2012-12-01

The three-dimensional digital differential analyzer (3D-DDA) algorithm is a widely used ray traversal method, which is also at the core of many convolution∕superposition (C∕S) dose calculation approaches. However, porting existing C∕S dose calculation methods onto graphics processing unit (GPU) has brought challenges to retaining the efficiency of this algorithm. In particular, straightforward implementation of the original 3D-DDA algorithm inflicts a lot of branch divergence which conflicts with the GPU programming model and leads to suboptimal performance. In this paper, an efficient GPU implementation of the 3D-DDA algorithm is proposed, which effectively reduces such branch divergence and improves performance of the C∕S dose calculation programs running on GPU. The main idea of the proposed method is to convert a number of conditional statements in the original 3D-DDA algorithm into a set of simple operations (e.g., arithmetic, comparison, and logic) which are better supported by the GPU architecture. To verify and demonstrate the performance improvement, this ray traversal method was integrated into a GPU-based collapsed cone convolution∕superposition (CCCS) dose calculation program. The proposed method has been tested using a water phantom and various clinical cases on an NVIDIA GTX570 GPU. The CCCS dose calculation program based on the efficient 3D-DDA ray traversal implementation runs 1.42 ∼ 2.67× faster than the one based on the original 3D-DDA implementation, without losing any accuracy. The results show that the proposed method can effectively reduce branch divergence in the original 3D-DDA ray traversal algorithm and improve the performance of the CCCS program running on GPU. Considering the wide utilization of the 3D-DDA algorithm, various applications can benefit from this implementation method.

A GPU accelerated and error-controlled solver for the unbounded Poisson equation in three dimensions

NASA Astrophysics Data System (ADS)

Exl, Lukas

2017-12-01

An efficient solver for the three dimensional free-space Poisson equation is presented. The underlying numerical method is based on finite Fourier series approximation. While the error of all involved approximations can be fully controlled, the overall computation error is driven by the convergence of the finite Fourier series of the density. For smooth and fast-decaying densities the proposed method will be spectrally accurate. The method scales with O(N log N) operations, where N is the total number of discretization points in the Cartesian grid. The majority of the computational costs come from fast Fourier transforms (FFT), which makes it ideal for GPU computation. Several numerical computations on CPU and GPU validate the method and show efficiency and convergence behavior. Tests are performed using the Vienna Scientific Cluster 3 (VSC3). A free MATLAB implementation for CPU and GPU is provided to the interested community.

GPU-based prompt gamma ray imaging from boron neutron capture therapy.

PubMed

Yoon, Do-Kun; Jung, Joo-Young; Jo Hong, Key; Sil Lee, Keum; Suk Suh, Tae

2015-01-01

The purpose of this research is to perform the fast reconstruction of a prompt gamma ray image using a graphics processing unit (GPU) computation from boron neutron capture therapy (BNCT) simulations. To evaluate the accuracy of the reconstructed image, a phantom including four boron uptake regions (BURs) was used in the simulation. After the Monte Carlo simulation of the BNCT, the modified ordered subset expectation maximization reconstruction algorithm using the GPU computation was used to reconstruct the images with fewer projections. The computation times for image reconstruction were compared between the GPU and the central processing unit (CPU). Also, the accuracy of the reconstructed image was evaluated by a receiver operating characteristic (ROC) curve analysis. The image reconstruction time using the GPU was 196 times faster than the conventional reconstruction time using the CPU. For the four BURs, the area under curve values from the ROC curve were 0.6726 (A-region), 0.6890 (B-region), 0.7384 (C-region), and 0.8009 (D-region). The tomographic image using the prompt gamma ray event from the BNCT simulation was acquired using the GPU computation in order to perform a fast reconstruction during treatment. The authors verified the feasibility of the prompt gamma ray image reconstruction using the GPU computation for BNCT simulations.

TU-AB-BRC-09: Fast Dose-Averaged LET and Biological Dose Calculations for Proton Therapy Using Graphics Cards

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wan, H; Tseung, Chan; Beltran, C

Purpose: To demonstrate fast and accurate Monte Carlo (MC) calculations of proton dose-averaged linear energy transfer (LETd) and biological dose (BD) on a Graphics Processing Unit (GPU) card. Methods: A previously validated GPU-based MC simulation of proton transport was used to rapidly generate LETd distributions for proton treatment plans. Since this MC handles proton-nuclei interactions on an event-by-event using a Bertini intranuclear cascade-evaporation model, secondary protons were taken into account. The smaller contributions of secondary neutrons and recoil nuclei were ignored. Recent work has shown that LETd values are sensitive to the scoring method. The GPU-based LETd calculations were verifiedmore » by comparing with a TOPAS custom scorer that uses tabulated stopping powers, following recommendations by other authors. Comparisons were made for prostate and head-and-neck patients. A python script is used to convert the MC-generated LETd distributions to BD using a variety of published linear quadratic models, and to export the BD in DICOM format for subsequent evaluation. Results: Very good agreement is obtained between TOPAS and our GPU MC. Given a complex head-and-neck plan with 1 mm voxel spacing, the physical dose, LETd and BD calculations for 10{sup 8} proton histories can be completed in ∼5 minutes using a NVIDIA Titan X card. The rapid turnover means that MC feedback can be obtained on dosimetric plan accuracy as well as BD hotspot locations, particularly in regards to their proximity to critical structures. In our institution the GPU MC-generated dose, LETd and BD maps are used to assess plan quality for all patients undergoing treatment. Conclusion: Fast and accurate MC-based LETd calculations can be performed on the GPU. The resulting BD maps provide valuable feedback during treatment plan review. Partially funded by Varian Medical Systems.« less

GPU-based prompt gamma ray imaging from boron neutron capture therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yoon, Do-Kun; Jung, Joo-Young; Suk Suh, Tae, E-mail: suhsanta@catholic.ac.kr

Purpose: The purpose of this research is to perform the fast reconstruction of a prompt gamma ray image using a graphics processing unit (GPU) computation from boron neutron capture therapy (BNCT) simulations. Methods: To evaluate the accuracy of the reconstructed image, a phantom including four boron uptake regions (BURs) was used in the simulation. After the Monte Carlo simulation of the BNCT, the modified ordered subset expectation maximization reconstruction algorithm using the GPU computation was used to reconstruct the images with fewer projections. The computation times for image reconstruction were compared between the GPU and the central processing unit (CPU).more » Also, the accuracy of the reconstructed image was evaluated by a receiver operating characteristic (ROC) curve analysis. Results: The image reconstruction time using the GPU was 196 times faster than the conventional reconstruction time using the CPU. For the four BURs, the area under curve values from the ROC curve were 0.6726 (A-region), 0.6890 (B-region), 0.7384 (C-region), and 0.8009 (D-region). Conclusions: The tomographic image using the prompt gamma ray event from the BNCT simulation was acquired using the GPU computation in order to perform a fast reconstruction during treatment. The authors verified the feasibility of the prompt gamma ray image reconstruction using the GPU computation for BNCT simulations.« less

TU-FG-BRB-07: GPU-Based Prompt Gamma Ray Imaging From Boron Neutron Capture Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, S; Suh, T; Yoon, D

Purpose: The purpose of this research is to perform the fast reconstruction of a prompt gamma ray image using a graphics processing unit (GPU) computation from boron neutron capture therapy (BNCT) simulations. Methods: To evaluate the accuracy of the reconstructed image, a phantom including four boron uptake regions (BURs) was used in the simulation. After the Monte Carlo simulation of the BNCT, the modified ordered subset expectation maximization reconstruction algorithm using the GPU computation was used to reconstruct the images with fewer projections. The computation times for image reconstruction were compared between the GPU and the central processing unit (CPU).more » Also, the accuracy of the reconstructed image was evaluated by a receiver operating characteristic (ROC) curve analysis. Results: The image reconstruction time using the GPU was 196 times faster than the conventional reconstruction time using the CPU. For the four BURs, the area under curve values from the ROC curve were 0.6726 (A-region), 0.6890 (B-region), 0.7384 (C-region), and 0.8009 (D-region). Conclusion: The tomographic image using the prompt gamma ray event from the BNCT simulation was acquired using the GPU computation in order to perform a fast reconstruction during treatment. The authors verified the feasibility of the prompt gamma ray reconstruction using the GPU computation for BNCT simulations.« less

Fast MPEG-CDVS Encoder With GPU-CPU Hybrid Computing

NASA Astrophysics Data System (ADS)

Duan, Ling-Yu; Sun, Wei; Zhang, Xinfeng; Wang, Shiqi; Chen, Jie; Yin, Jianxiong; See, Simon; Huang, Tiejun; Kot, Alex C.; Gao, Wen

2018-05-01

The compact descriptors for visual search (CDVS) standard from ISO/IEC moving pictures experts group (MPEG) has succeeded in enabling the interoperability for efficient and effective image retrieval by standardizing the bitstream syntax of compact feature descriptors. However, the intensive computation of CDVS encoder unfortunately hinders its widely deployment in industry for large-scale visual search. In this paper, we revisit the merits of low complexity design of CDVS core techniques and present a very fast CDVS encoder by leveraging the massive parallel execution resources of GPU. We elegantly shift the computation-intensive and parallel-friendly modules to the state-of-the-arts GPU platforms, in which the thread block allocation and the memory access are jointly optimized to eliminate performance loss. In addition, those operations with heavy data dependence are allocated to CPU to resolve the extra but non-necessary computation burden for GPU. Furthermore, we have demonstrated the proposed fast CDVS encoder can work well with those convolution neural network approaches which has harmoniously leveraged the advantages of GPU platforms, and yielded significant performance improvements. Comprehensive experimental results over benchmarks are evaluated, which has shown that the fast CDVS encoder using GPU-CPU hybrid computing is promising for scalable visual search.

Compute-unified device architecture implementation of a block-matching algorithm for multiple graphical processing unit cards

PubMed Central

Massanes, Francesc; Cadennes, Marie; Brankov, Jovan G.

2012-01-01

In this paper we describe and evaluate a fast implementation of a classical block matching motion estimation algorithm for multiple Graphical Processing Units (GPUs) using the Compute Unified Device Architecture (CUDA) computing engine. The implemented block matching algorithm (BMA) uses summed absolute difference (SAD) error criterion and full grid search (FS) for finding optimal block displacement. In this evaluation we compared the execution time of a GPU and CPU implementation for images of various sizes, using integer and non-integer search grids. The results show that use of a GPU card can shorten computation time by a factor of 200 times for integer and 1000 times for a non-integer search grid. The additional speedup for non-integer search grid comes from the fact that GPU has built-in hardware for image interpolation. Further, when using multiple GPU cards, the presented evaluation shows the importance of the data splitting method across multiple cards, but an almost linear speedup with a number of cards is achievable. In addition we compared execution time of the proposed FS GPU implementation with two existing, highly optimized non-full grid search CPU based motion estimations methods, namely implementation of the Pyramidal Lucas Kanade Optical flow algorithm in OpenCV and Simplified Unsymmetrical multi-Hexagon search in H.264/AVC standard. In these comparisons, FS GPU implementation still showed modest improvement even though the computational complexity of FS GPU implementation is substantially higher than non-FS CPU implementation. We also demonstrated that for an image sequence of 720×480 pixels in resolution, commonly used in video surveillance, the proposed GPU implementation is sufficiently fast for real-time motion estimation at 30 frames-per-second using two NVIDIA C1060 Tesla GPU cards. PMID:22347787

Compute-unified device architecture implementation of a block-matching algorithm for multiple graphical processing unit cards.

PubMed

Massanes, Francesc; Cadennes, Marie; Brankov, Jovan G

2011-07-01

In this paper we describe and evaluate a fast implementation of a classical block matching motion estimation algorithm for multiple Graphical Processing Units (GPUs) using the Compute Unified Device Architecture (CUDA) computing engine. The implemented block matching algorithm (BMA) uses summed absolute difference (SAD) error criterion and full grid search (FS) for finding optimal block displacement. In this evaluation we compared the execution time of a GPU and CPU implementation for images of various sizes, using integer and non-integer search grids.The results show that use of a GPU card can shorten computation time by a factor of 200 times for integer and 1000 times for a non-integer search grid. The additional speedup for non-integer search grid comes from the fact that GPU has built-in hardware for image interpolation. Further, when using multiple GPU cards, the presented evaluation shows the importance of the data splitting method across multiple cards, but an almost linear speedup with a number of cards is achievable.In addition we compared execution time of the proposed FS GPU implementation with two existing, highly optimized non-full grid search CPU based motion estimations methods, namely implementation of the Pyramidal Lucas Kanade Optical flow algorithm in OpenCV and Simplified Unsymmetrical multi-Hexagon search in H.264/AVC standard. In these comparisons, FS GPU implementation still showed modest improvement even though the computational complexity of FS GPU implementation is substantially higher than non-FS CPU implementation.We also demonstrated that for an image sequence of 720×480 pixels in resolution, commonly used in video surveillance, the proposed GPU implementation is sufficiently fast for real-time motion estimation at 30 frames-per-second using two NVIDIA C1060 Tesla GPU cards.

A Fully GPU-Based Ray-Driven Backprojector via a Ray-Culling Scheme with Voxel-Level Parallelization for Cone-Beam CT Reconstruction.

PubMed

Park, Hyeong-Gyu; Shin, Yeong-Gil; Lee, Ho

2015-12-01

A ray-driven backprojector is based on ray-tracing, which computes the length of the intersection between the ray paths and each voxel to be reconstructed. To reduce the computational burden caused by these exhaustive intersection tests, we propose a fully graphics processing unit (GPU)-based ray-driven backprojector in conjunction with a ray-culling scheme that enables straightforward parallelization without compromising the high computing performance of a GPU. The purpose of the ray-culling scheme is to reduce the number of ray-voxel intersection tests by excluding rays irrelevant to a specific voxel computation. This rejection step is based on an axis-aligned bounding box (AABB) enclosing a region of voxel projection, where eight vertices of each voxel are projected onto the detector plane. The range of the rectangular-shaped AABB is determined by min/max operations on the coordinates in the region. Using the indices of pixels inside the AABB, the rays passing through the voxel can be identified and the voxel is weighted as the length of intersection between the voxel and the ray. This procedure makes it possible to reflect voxel-level parallelization, allowing an independent calculation at each voxel, which is feasible for a GPU implementation. To eliminate redundant calculations during ray-culling, a shared-memory optimization is applied to exploit the GPU memory hierarchy. In experimental results using real measurement data with phantoms, the proposed GPU-based ray-culling scheme reconstructed a volume of resolution 28032803176 in 77 seconds from 680 projections of resolution 10243768 , which is 26 times and 7.5 times faster than standard CPU-based and GPU-based ray-driven backprojectors, respectively. Qualitative and quantitative analyses showed that the ray-driven backprojector provides high-quality reconstruction images when compared with those generated by the Feldkamp-Davis-Kress algorithm using a pixel-driven backprojector, with an average of 2.5 times higher contrast-to-noise ratio, 1.04 times higher universal quality index, and 1.39 times higher normalized mutual information. © The Author(s) 2014.

General rigid motion correction for computed tomography imaging based on locally linear embedding

NASA Astrophysics Data System (ADS)

Chen, Mianyi; He, Peng; Feng, Peng; Liu, Baodong; Yang, Qingsong; Wei, Biao; Wang, Ge

2018-02-01

The patient motion can damage the quality of computed tomography images, which are typically acquired in cone-beam geometry. The rigid patient motion is characterized by six geometric parameters and are more challenging to correct than in fan-beam geometry. We extend our previous rigid patient motion correction method based on the principle of locally linear embedding (LLE) from fan-beam to cone-beam geometry and accelerate the computational procedure with the graphics processing unit (GPU)-based all scale tomographic reconstruction Antwerp toolbox. The major merit of our method is that we need neither fiducial markers nor motion-tracking devices. The numerical and experimental studies show that the LLE-based patient motion correction is capable of calibrating the six parameters of the patient motion simultaneously, reducing patient motion artifacts significantly.

Incompressible SPH (ISPH) with fast Poisson solver on a GPU

NASA Astrophysics Data System (ADS)

Chow, Alex D.; Rogers, Benedict D.; Lind, Steven J.; Stansby, Peter K.

2018-05-01

This paper presents a fast incompressible SPH (ISPH) solver implemented to run entirely on a graphics processing unit (GPU) capable of simulating several millions of particles in three dimensions on a single GPU. The ISPH algorithm is implemented by converting the highly optimised open-source weakly-compressible SPH (WCSPH) code DualSPHysics to run ISPH on the GPU, combining it with the open-source linear algebra library ViennaCL for fast solutions of the pressure Poisson equation (PPE). Several challenges are addressed with this research: constructing a PPE matrix every timestep on the GPU for moving particles, optimising the limited GPU memory, and exploiting fast matrix solvers. The ISPH pressure projection algorithm is implemented as 4 separate stages, each with a particle sweep, including an algorithm for the population of the PPE matrix suitable for the GPU, and mixed precision storage methods. An accurate and robust ISPH boundary condition ideal for parallel processing is also established by adapting an existing WCSPH boundary condition for ISPH. A variety of validation cases are presented: an impulsively started plate, incompressible flow around a moving square in a box, and dambreaks (2-D and 3-D) which demonstrate the accuracy, flexibility, and speed of the methodology. Fragmentation of the free surface is shown to influence the performance of matrix preconditioners and therefore the PPE matrix solution time. The Jacobi preconditioner demonstrates robustness and reliability in the presence of fragmented flows. For a dambreak simulation, GPU speed ups demonstrate up to 10-18 times and 1.1-4.5 times compared to single-threaded and 16-threaded CPU run times respectively.

A software tool of digital tomosynthesis application for patient positioning in radiotherapy.

PubMed

Yan, Hui; Dai, Jian-Rong

2016-03-08

Digital Tomosynthesis (DTS) is an image modality in reconstructing tomographic images from two-dimensional kV projections covering a narrow scan angles. Comparing with conventional cone-beam CT (CBCT), it requires less time and radiation dose in data acquisition. It is feasible to apply this technique in patient positioning in radiotherapy. To facilitate its clinical application, a software tool was developed and the reconstruction processes were accelerated by graphic process-ing unit (GPU). Two reconstruction and two registration processes are required for DTS application which is different from conventional CBCT application which requires one image reconstruction process and one image registration process. The reconstruction stage consists of productions of two types of DTS. One type of DTS is reconstructed from cone-beam (CB) projections covering a narrow scan angle and is named onboard DTS (ODTS), which represents the real patient position in treatment room. Another type of DTS is reconstructed from digitally reconstructed radiography (DRR) and is named reference DTS (RDTS), which represents the ideal patient position in treatment room. Prior to the reconstruction of RDTS, The DRRs are reconstructed from planning CT using the same acquisition setting of CB projections. The registration stage consists of two matching processes between ODTS and RDTS. The target shift in lateral and longitudinal axes are obtained from the matching between ODTS and RDTS in coronal view, while the target shift in longitudinal and vertical axes are obtained from the matching between ODTS and RDTS in sagittal view. In this software, both DRR and DTS reconstruction algorithms were implemented on GPU environments for acceleration purpose. The comprehensive evaluation of this software tool was performed including geometric accuracy, image quality, registration accuracy, and reconstruction efficiency. The average correlation coefficient between DRR/DTS generated by GPU-based algorithm and CPU-based algorithm is 0.99. Based on the measurements of cube phantom on DTS, the geometric errors are within 0.5 mm in three axes. For both cube phantom and pelvic phantom, the registration errors are within 0.5 mm in three axes. Compared with reconstruction performance of CPU-based algorithms, the performances of DRR and DTS reconstructions are improved by a factor of 15 to 20. A GPU-based software tool was developed for DTS application for patient positioning of radiotherapy. The geometric and registration accuracy met the clinical requirement in patient setup of radiotherapy. The high performance of DRR and DTS reconstruction algorithms was achieved by the GPU-based computation environments. It is a useful software tool for researcher and clinician in evaluating DTS application in patient positioning of radiotherapy.

Sub-second pencil beam dose calculation on GPU for adaptive proton therapy.

PubMed

da Silva, Joakim; Ansorge, Richard; Jena, Rajesh

2015-06-21

Although proton therapy delivered using scanned pencil beams has the potential to produce better dose conformity than conventional radiotherapy, the created dose distributions are more sensitive to anatomical changes and patient motion. Therefore, the introduction of adaptive treatment techniques where the dose can be monitored as it is being delivered is highly desirable. We present a GPU-based dose calculation engine relying on the widely used pencil beam algorithm, developed for on-line dose calculation. The calculation engine was implemented from scratch, with each step of the algorithm parallelized and adapted to run efficiently on the GPU architecture. To ensure fast calculation, it employs several application-specific modifications and simplifications, and a fast scatter-based implementation of the computationally expensive kernel superposition step. The calculation time for a skull base treatment plan using two beam directions was 0.22 s on an Nvidia Tesla K40 GPU, whereas a test case of a cubic target in water from the literature took 0.14 s to calculate. The accuracy of the patient dose distributions was assessed by calculating the γ-index with respect to a gold standard Monte Carlo simulation. The passing rates were 99.2% and 96.7%, respectively, for the 3%/3 mm and 2%/2 mm criteria, matching those produced by a clinical treatment planning system.

Fast MPEG-CDVS Encoder With GPU-CPU Hybrid Computing.

PubMed

Duan, Ling-Yu; Sun, Wei; Zhang, Xinfeng; Wang, Shiqi; Chen, Jie; Yin, Jianxiong; See, Simon; Huang, Tiejun; Kot, Alex C; Gao, Wen

2018-05-01

The compact descriptors for visual search (CDVS) standard from ISO/IEC moving pictures experts group has succeeded in enabling the interoperability for efficient and effective image retrieval by standardizing the bitstream syntax of compact feature descriptors. However, the intensive computation of a CDVS encoder unfortunately hinders its widely deployment in industry for large-scale visual search. In this paper, we revisit the merits of low complexity design of CDVS core techniques and present a very fast CDVS encoder by leveraging the massive parallel execution resources of graphics processing unit (GPU). We elegantly shift the computation-intensive and parallel-friendly modules to the state-of-the-arts GPU platforms, in which the thread block allocation as well as the memory access mechanism are jointly optimized to eliminate performance loss. In addition, those operations with heavy data dependence are allocated to CPU for resolving the extra but non-necessary computation burden for GPU. Furthermore, we have demonstrated the proposed fast CDVS encoder can work well with those convolution neural network approaches which enables to leverage the advantages of GPU platforms harmoniously, and yield significant performance improvements. Comprehensive experimental results over benchmarks are evaluated, which has shown that the fast CDVS encoder using GPU-CPU hybrid computing is promising for scalable visual search.

System matrix computation vs storage on GPU: A comparative study in cone beam CT.

PubMed

Matenine, Dmitri; Côté, Geoffroi; Mascolo-Fortin, Julia; Goussard, Yves; Després, Philippe

2018-02-01

Iterative reconstruction algorithms in computed tomography (CT) require a fast method for computing the intersection distances between the trajectories of photons and the object, also called ray tracing or system matrix computation. This work focused on the thin-ray model is aimed at comparing different system matrix handling strategies using graphical processing units (GPUs). In this work, the system matrix is modeled by thin rays intersecting a regular grid of box-shaped voxels, known to be an accurate representation of the forward projection operator in CT. However, an uncompressed system matrix exceeds the random access memory (RAM) capacities of typical computers by one order of magnitude or more. Considering the RAM limitations of GPU hardware, several system matrix handling methods were compared: full storage of a compressed system matrix, on-the-fly computation of its coefficients, and partial storage of the system matrix with partial on-the-fly computation. These methods were tested on geometries mimicking a cone beam CT (CBCT) acquisition of a human head. Execution times of three routines of interest were compared: forward projection, backprojection, and ordered-subsets convex (OSC) iteration. A fully stored system matrix yielded the shortest backprojection and OSC iteration times, with a 1.52× acceleration for OSC when compared to the on-the-fly approach. Nevertheless, the maximum problem size was bound by the available GPU RAM and geometrical symmetries. On-the-fly coefficient computation did not require symmetries and was shown to be the fastest for forward projection. It also offered reasonable execution times of about 176.4 ms per view per OSC iteration for a detector of 512 × 448 pixels and a volume of 384 3 voxels, using commodity GPU hardware. Partial system matrix storage has shown a performance similar to the on-the-fly approach, while still relying on symmetries. Partial system matrix storage was shown to yield the lowest relative performance. On-the-fly ray tracing was shown to be the most flexible method, yielding reasonable execution times. A fully stored system matrix allowed for the lowest backprojection and OSC iteration times and may be of interest for certain performance-oriented applications. © 2017 American Association of Physicists in Medicine.

SU-F-BRD-07: Fast Monte Carlo-Based Biological Optimization of Proton Therapy Treatment Plans for Thyroid Tumors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wan Chan Tseung, H; Ma, J; Ma, D

2015-06-15

Purpose: To demonstrate the feasibility of fast Monte Carlo (MC) based biological planning for the treatment of thyroid tumors in spot-scanning proton therapy. Methods: Recently, we developed a fast and accurate GPU-based MC simulation of proton transport that was benchmarked against Geant4.9.6 and used as the dose calculation engine in a clinically-applicable GPU-accelerated IMPT optimizer. Besides dose, it can simultaneously score the dose-averaged LET (LETd), which makes fast biological dose (BD) estimates possible. To convert from LETd to BD, we used a linear relation based on cellular irradiation data. Given a thyroid patient with a 93cc tumor volume, we createdmore » a 2-field IMPT plan in Eclipse (Varian Medical Systems). This plan was re-calculated with our MC to obtain the BD distribution. A second 5-field plan was made with our in-house optimizer, using pre-generated MC dose and LETd maps. Constraints were placed to maintain the target dose to within 25% of the prescription, while maximizing the BD. The plan optimization and calculation of dose and LETd maps were performed on a GPU cluster. The conventional IMPT and biologically-optimized plans were compared. Results: The mean target physical and biological doses from our biologically-optimized plan were, respectively, 5% and 14% higher than those from the MC re-calculation of the IMPT plan. Dose sparing to critical structures in our plan was also improved. The biological optimization, including the initial dose and LETd map calculations, can be completed in a clinically viable time (∼30 minutes) on a cluster of 25 GPUs. Conclusion: Taking advantage of GPU acceleration, we created a MC-based, biologically optimized treatment plan for a thyroid patient. Compared to a standard IMPT plan, a 5% increase in the target’s physical dose resulted in ∼3 times as much increase in the BD. Biological planning was thus effective in escalating the target BD.« less

Protein-protein docking on hardware accelerators: comparison of GPU and MIC architectures

PubMed Central

2015-01-01

Background The hardware accelerators will provide solutions to computationally complex problems in bioinformatics fields. However, the effect of acceleration depends on the nature of the application, thus selection of an appropriate accelerator requires some consideration. Results In the present study, we compared the effects of acceleration using graphics processing unit (GPU) and many integrated core (MIC) on the speed of fast Fourier transform (FFT)-based protein-protein docking calculation. The GPU implementation performed the protein-protein docking calculations approximately five times faster than the MIC offload mode implementation. The MIC native mode implementation has the advantage in the implementation costs. However, the performance was worse with larger protein pairs because of memory limitations. Conclusion The results suggest that GPU is more suitable than MIC for accelerating FFT-based protein-protein docking applications. PMID:25707855

SU-E-T-558: Monte Carlo Photon Transport Simulations On GPU with Quadric Geometry

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chi, Y; Tian, Z; Jiang, S

Purpose: Monte Carlo simulation on GPU has experienced rapid advancements over the past a few years and tremendous accelerations have been achieved. Yet existing packages were developed only in voxelized geometry. In some applications, e.g. radioactive seed modeling, simulations in more complicated geometry are needed. This abstract reports our initial efforts towards developing a quadric geometry module aiming at expanding the application scope of GPU-based MC simulations. Methods: We defined the simulation geometry consisting of a number of homogeneous bodies, each specified by its material composition and limiting surfaces characterized by quadric functions. A tree data structure was utilized tomore » define geometric relationship between different bodies. We modified our GPU-based photon MC transport package to incorporate this geometry. Specifically, geometry parameters were loaded into GPU’s shared memory for fast access. Geometry functions were rewritten to enable the identification of the body that contains the current particle location via a fast searching algorithm based on the tree data structure. Results: We tested our package in an example problem of HDR-brachytherapy dose calculation for shielded cylinder. The dose under the quadric geometry and that under the voxelized geometry agreed in 94.2% of total voxels within 20% isodose line based on a statistical t-test (95% confidence level), where the reference dose was defined to be the one at 0.5cm away from the cylinder surface. It took 243sec to transport 100million source photons under this quadric geometry on an NVidia Titan GPU card. Compared with simulation time of 99.6sec in the voxelized geometry, including quadric geometry reduced efficiency due to the complicated geometry-related computations. Conclusion: Our GPU-based MC package has been extended to support photon transport simulation in quadric geometry. Satisfactory accuracy was observed with a reduced efficiency. Developments for charged particle transport in this geometry are currently in progress.« less

GPU-based ultra-fast dose calculation using a finite size pencil beam model.

PubMed

Gu, Xuejun; Choi, Dongju; Men, Chunhua; Pan, Hubert; Majumdar, Amitava; Jiang, Steve B

2009-10-21

Online adaptive radiation therapy (ART) is an attractive concept that promises the ability to deliver an optimal treatment in response to the inter-fraction variability in patient anatomy. However, it has yet to be realized due to technical limitations. Fast dose deposit coefficient calculation is a critical component of the online planning process that is required for plan optimization of intensity-modulated radiation therapy (IMRT). Computer graphics processing units (GPUs) are well suited to provide the requisite fast performance for the data-parallel nature of dose calculation. In this work, we develop a dose calculation engine based on a finite-size pencil beam (FSPB) algorithm and a GPU parallel computing framework. The developed framework can accommodate any FSPB model. We test our implementation in the case of a water phantom and the case of a prostate cancer patient with varying beamlet and voxel sizes. All testing scenarios achieved speedup ranging from 200 to 400 times when using a NVIDIA Tesla C1060 card in comparison with a 2.27 GHz Intel Xeon CPU. The computational time for calculating dose deposition coefficients for a nine-field prostate IMRT plan with this new framework is less than 1 s. This indicates that the GPU-based FSPB algorithm is well suited for online re-planning for adaptive radiotherapy.

TH-A-18C-04: Ultrafast Cone-Beam CT Scatter Correction with GPU-Based Monte Carlo Simulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xu, Y; Southern Medical University, Guangzhou; Bai, T

2014-06-15

Purpose: Scatter artifacts severely degrade image quality of cone-beam CT (CBCT). We present an ultrafast scatter correction framework by using GPU-based Monte Carlo (MC) simulation and prior patient CT image, aiming at automatically finish the whole process including both scatter correction and reconstructions within 30 seconds. Methods: The method consists of six steps: 1) FDK reconstruction using raw projection data; 2) Rigid Registration of planning CT to the FDK results; 3) MC scatter calculation at sparse view angles using the planning CT; 4) Interpolation of the calculated scatter signals to other angles; 5) Removal of scatter from the raw projections;more » 6) FDK reconstruction using the scatter-corrected projections. In addition to using GPU to accelerate MC photon simulations, we also use a small number of photons and a down-sampled CT image in simulation to further reduce computation time. A novel denoising algorithm is used to eliminate MC scatter noise caused by low photon numbers. The method is validated on head-and-neck cases with simulated and clinical data. Results: We have studied impacts of photo histories, volume down sampling factors on the accuracy of scatter estimation. The Fourier analysis was conducted to show that scatter images calculated at 31 angles are sufficient to restore those at all angles with <0.1% error. For the simulated case with a resolution of 512×512×100, we simulated 10M photons per angle. The total computation time is 23.77 seconds on a Nvidia GTX Titan GPU. The scatter-induced shading/cupping artifacts are substantially reduced, and the average HU error of a region-of-interest is reduced from 75.9 to 19.0 HU. Similar results were found for a real patient case. Conclusion: A practical ultrafast MC-based CBCT scatter correction scheme is developed. The whole process of scatter correction and reconstruction is accomplished within 30 seconds. This study is supported in part by NIH (1R01CA154747-01), The Core Technology Research in Strategic Emerging Industry, Guangdong, China (2011A081402003)« less

GPU-Based Real-Time Volumetric Ultrasound Image Reconstruction for a Ring Array

PubMed Central

Choe, Jung Woo; Nikoozadeh, Amin; Oralkan, Ömer; Khuri-Yakub, Butrus T.

2014-01-01

Synthetic phased array (SPA) beamforming with Hadamard coding and aperture weighting is an optimal option for real-time volumetric imaging with a ring array, a particularly attractive geometry in intracardiac and intravascular applications. However, the imaging frame rate of this method is limited by the immense computational load required in synthetic beamforming. For fast imaging with a ring array, we developed graphics processing unit (GPU)-based, real-time image reconstruction software that exploits massive data-level parallelism in beamforming operations. The GPU-based software reconstructs and displays three cross-sectional images at 45 frames per second (fps). This frame rate is 4.5 times higher than that for our previously-developed multi-core CPU-based software. In an alternative imaging mode, it shows one B-mode image rotating about the axis and its maximum intensity projection (MIP), processed at a rate of 104 fps. This paper describes the image reconstruction procedure on the GPU platform and presents the experimental images obtained using this software. PMID:23529080

SU-E-T-395: Multi-GPU-Based VMAT Treatment Plan Optimization Using a Column-Generation Approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tian, Z; Shi, F; Jia, X

Purpose: GPU has been employed to speed up VMAT optimizations from hours to minutes. However, its limited memory capacity makes it difficult to handle cases with a huge dose-deposition-coefficient (DDC) matrix, e.g. those with a large target size, multiple arcs, small beam angle intervals and/or small beamlet size. We propose multi-GPU-based VMAT optimization to solve this memory issue to make GPU-based VMAT more practical for clinical use. Methods: Our column-generation-based method generates apertures sequentially by iteratively searching for an optimal feasible aperture (referred as pricing problem, PP) and optimizing aperture intensities (referred as master problem, MP). The PP requires accessmore » to the large DDC matrix, which is implemented on a multi-GPU system. Each GPU stores a DDC sub-matrix corresponding to one fraction of beam angles and is only responsible for calculation related to those angles. Broadcast and parallel reduction schemes are adopted for inter-GPU data transfer. MP is a relatively small-scale problem and is implemented on one GPU. One headand- neck cancer case was used for test. Three different strategies for VMAT optimization on single GPU were also implemented for comparison: (S1) truncating DDC matrix to ignore its small value entries for optimization; (S2) transferring DDC matrix part by part to GPU during optimizations whenever needed; (S3) moving DDC matrix related calculation onto CPU. Results: Our multi-GPU-based implementation reaches a good plan within 1 minute. Although S1 was 10 seconds faster than our method, the obtained plan quality is worse. Both S2 and S3 handle the full DDC matrix and hence yield the same plan as in our method. However, the computation time is longer, namely 4 minutes and 30 minutes, respectively. Conclusion: Our multi-GPU-based VMAT optimization can effectively solve the limited memory issue with good plan quality and high efficiency, making GPUbased ultra-fast VMAT planning practical for real clinical use.« less

GPU-based stochastic-gradient optimization for non-rigid medical image registration in time-critical applications

NASA Astrophysics Data System (ADS)

Bhosale, Parag; Staring, Marius; Al-Ars, Zaid; Berendsen, Floris F.

2018-03-01

Currently, non-rigid image registration algorithms are too computationally intensive to use in time-critical applications. Existing implementations that focus on speed typically address this by either parallelization on GPU-hardware, or by introducing methodically novel techniques into CPU-oriented algorithms. Stochastic gradient descent (SGD) optimization and variations thereof have proven to drastically reduce the computational burden for CPU-based image registration, but have not been successfully applied in GPU hardware due to its stochastic nature. This paper proposes 1) NiftyRegSGD, a SGD optimization for the GPU-based image registration tool NiftyReg, 2) random chunk sampler, a new random sampling strategy that better utilizes the memory bandwidth of GPU hardware. Experiments have been performed on 3D lung CT data of 19 patients, which compared NiftyRegSGD (with and without random chunk sampler) with CPU-based elastix Fast Adaptive SGD (FASGD) and NiftyReg. The registration runtime was 21.5s, 4.4s and 2.8s for elastix-FASGD, NiftyRegSGD without, and NiftyRegSGD with random chunk sampling, respectively, while similar accuracy was obtained. Our method is publicly available at https://github.com/SuperElastix/NiftyRegSGD.

TU-AB-303-08: GPU-Based Software Platform for Efficient Image-Guided Adaptive Radiation Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Park, S; Robinson, A; McNutt, T

2015-06-15

Purpose: In this study, we develop an integrated software platform for adaptive radiation therapy (ART) that combines fast and accurate image registration, segmentation, and dose computation/accumulation methods. Methods: The proposed system consists of three key components; 1) deformable image registration (DIR), 2) automatic segmentation, and 3) dose computation/accumulation. The computationally intensive modules including DIR and dose computation have been implemented on a graphics processing unit (GPU). All required patient-specific data including the planning CT (pCT) with contours, daily cone-beam CTs, and treatment plan are automatically queried and retrieved from their own databases. To improve the accuracy of DIR between pCTmore » and CBCTs, we use the double force demons DIR algorithm in combination with iterative CBCT intensity correction by local intensity histogram matching. Segmentation of daily CBCT is then obtained by propagating contours from the pCT. Daily dose delivered to the patient is computed on the registered pCT by a GPU-accelerated superposition/convolution algorithm. Finally, computed daily doses are accumulated to show the total delivered dose to date. Results: Since the accuracy of DIR critically affects the quality of the other processes, we first evaluated our DIR method on eight head-and-neck cancer cases and compared its performance. Normalized mutual-information (NMI) and normalized cross-correlation (NCC) computed as similarity measures, and our method produced overall NMI of 0.663 and NCC of 0.987, outperforming conventional methods by 3.8% and 1.9%, respectively. Experimental results show that our registration method is more consistent and roust than existing algorithms, and also computationally efficient. Computation time at each fraction took around one minute (30–50 seconds for registration and 15–25 seconds for dose computation). Conclusion: We developed an integrated GPU-accelerated software platform that enables accurate and efficient DIR, auto-segmentation, and dose computation, thus supporting an efficient ART workflow. This work was supported by NIH/NCI under grant R42CA137886.« less

Architecting the Finite Element Method Pipeline for the GPU.

PubMed

Fu, Zhisong; Lewis, T James; Kirby, Robert M; Whitaker, Ross T

2014-02-01

The finite element method (FEM) is a widely employed numerical technique for approximating the solution of partial differential equations (PDEs) in various science and engineering applications. Many of these applications benefit from fast execution of the FEM pipeline. One way to accelerate the FEM pipeline is by exploiting advances in modern computational hardware, such as the many-core streaming processors like the graphical processing unit (GPU). In this paper, we present the algorithms and data-structures necessary to move the entire FEM pipeline to the GPU. First we propose an efficient GPU-based algorithm to generate local element information and to assemble the global linear system associated with the FEM discretization of an elliptic PDE. To solve the corresponding linear system efficiently on the GPU, we implement a conjugate gradient method preconditioned with a geometry-informed algebraic multi-grid (AMG) method preconditioner. We propose a new fine-grained parallelism strategy, a corresponding multigrid cycling stage and efficient data mapping to the many-core architecture of GPU. Comparison of our on-GPU assembly versus a traditional serial implementation on the CPU achieves up to an 87 × speedup. Focusing on the linear system solver alone, we achieve a speedup of up to 51 × versus use of a comparable state-of-the-art serial CPU linear system solver. Furthermore, the method compares favorably with other GPU-based, sparse, linear solvers.

Sub-second pencil beam dose calculation on GPU for adaptive proton therapy

NASA Astrophysics Data System (ADS)

da Silva, Joakim; Ansorge, Richard; Jena, Rajesh

2015-06-01

Although proton therapy delivered using scanned pencil beams has the potential to produce better dose conformity than conventional radiotherapy, the created dose distributions are more sensitive to anatomical changes and patient motion. Therefore, the introduction of adaptive treatment techniques where the dose can be monitored as it is being delivered is highly desirable. We present a GPU-based dose calculation engine relying on the widely used pencil beam algorithm, developed for on-line dose calculation. The calculation engine was implemented from scratch, with each step of the algorithm parallelized and adapted to run efficiently on the GPU architecture. To ensure fast calculation, it employs several application-specific modifications and simplifications, and a fast scatter-based implementation of the computationally expensive kernel superposition step. The calculation time for a skull base treatment plan using two beam directions was 0.22 s on an Nvidia Tesla K40 GPU, whereas a test case of a cubic target in water from the literature took 0.14 s to calculate. The accuracy of the patient dose distributions was assessed by calculating the γ-index with respect to a gold standard Monte Carlo simulation. The passing rates were 99.2% and 96.7%, respectively, for the 3%/3 mm and 2%/2 mm criteria, matching those produced by a clinical treatment planning system.

Fast Simulation of Dynamic Ultrasound Images Using the GPU.

PubMed

Storve, Sigurd; Torp, Hans

2017-10-01

Simulated ultrasound data is a valuable tool for development and validation of quantitative image analysis methods in echocardiography. Unfortunately, simulation time can become prohibitive for phantoms consisting of a large number of point scatterers. The COLE algorithm by Gao et al. is a fast convolution-based simulator that trades simulation accuracy for improved speed. We present highly efficient parallelized CPU and GPU implementations of the COLE algorithm with an emphasis on dynamic simulations involving moving point scatterers. We argue that it is crucial to minimize the amount of data transfers from the CPU to achieve good performance on the GPU. We achieve this by storing the complete trajectories of the dynamic point scatterers as spline curves in the GPU memory. This leads to good efficiency when simulating sequences consisting of a large number of frames, such as B-mode and tissue Doppler data for a full cardiac cycle. In addition, we propose a phase-based subsample delay technique that efficiently eliminates flickering artifacts seen in B-mode sequences when COLE is used without enough temporal oversampling. To assess the performance, we used a laptop computer and a desktop computer, each equipped with a multicore Intel CPU and an NVIDIA GPU. Running the simulator on a high-end TITAN X GPU, we observed two orders of magnitude speedup compared to the parallel CPU version, three orders of magnitude speedup compared to simulation times reported by Gao et al. in their paper on COLE, and a speedup of 27000 times compared to the multithreaded version of Field II, using numbers reported in a paper by Jensen. We hope that by releasing the simulator as an open-source project we will encourage its use and further development.

A Fourier-based compressed sensing technique for accelerated CT image reconstruction using first-order methods.

PubMed

Choi, Kihwan; Li, Ruijiang; Nam, Haewon; Xing, Lei

2014-06-21

As a solution to iterative CT image reconstruction, first-order methods are prominent for the large-scale capability and the fast convergence rate [Formula: see text]. In practice, the CT system matrix with a large condition number may lead to slow convergence speed despite the theoretically promising upper bound. The aim of this study is to develop a Fourier-based scaling technique to enhance the convergence speed of first-order methods applied to CT image reconstruction. Instead of working in the projection domain, we transform the projection data and construct a data fidelity model in Fourier space. Inspired by the filtered backprojection formalism, the data are appropriately weighted in Fourier space. We formulate an optimization problem based on weighted least-squares in the Fourier space and total-variation (TV) regularization in image space for parallel-beam, fan-beam and cone-beam CT geometry. To achieve the maximum computational speed, the optimization problem is solved using a fast iterative shrinkage-thresholding algorithm with backtracking line search and GPU implementation of projection/backprojection. The performance of the proposed algorithm is demonstrated through a series of digital simulation and experimental phantom studies. The results are compared with the existing TV regularized techniques based on statistics-based weighted least-squares as well as basic algebraic reconstruction technique. The proposed Fourier-based compressed sensing (CS) method significantly improves both the image quality and the convergence rate compared to the existing CS techniques.

Diffusion of external magnetic fields into the cone-in-shell target in the fast ignition

NASA Astrophysics Data System (ADS)

Sunahara, Atsushi; Morita, Hiroki; Johzaki, Tomoyuki; Nagatomo, Hideo; Fujioka, Shinsuke; Hassanein, Ahmed; Firex Project Team

2017-10-01

We simulated the diffusion of externally applied magnetic fields into cone-in-shell target in the fast ignition. Recently, in the fast ignition scheme, the externally magnetic fields up to kilo-Tesla is used to guide fast electrons to the high-dense imploded core. In order to study the profile of the magnetic field, we have developed 2D cylindrical Maxwell equation solver with Ohm's law, and carried out simulations of diffusion of externally applied magnetic fields into a cone-in-shell target. We estimated the conductivity of the cone and shell target based on the assumption of Saha-ionization equilibrium. Also, we calculated the temporal evolution of the target temperature heated by the eddy current driven by temporal variation of magnetic fields, based on the accurate equation of state. Both, the diffusion of magnetic field and the increase of target temperature interact with each other. We present our results of temporal evolution of the magnetic field and its diffusion into the cone and shell target.

A survey of GPU-based acceleration techniques in MRI reconstructions

PubMed Central

Wang, Haifeng; Peng, Hanchuan; Chang, Yuchou

2018-01-01

Image reconstruction in magnetic resonance imaging (MRI) clinical applications has become increasingly more complicated. However, diagnostic and treatment require very fast computational procedure. Modern competitive platforms of graphics processing unit (GPU) have been used to make high-performance parallel computations available, and attractive to common consumers for computing massively parallel reconstruction problems at commodity price. GPUs have also become more and more important for reconstruction computations, especially when deep learning starts to be applied into MRI reconstruction. The motivation of this survey is to review the image reconstruction schemes of GPU computing for MRI applications and provide a summary reference for researchers in MRI community. PMID:29675361

A survey of GPU-based acceleration techniques in MRI reconstructions.

PubMed

Wang, Haifeng; Peng, Hanchuan; Chang, Yuchou; Liang, Dong

2018-03-01

Image reconstruction in magnetic resonance imaging (MRI) clinical applications has become increasingly more complicated. However, diagnostic and treatment require very fast computational procedure. Modern competitive platforms of graphics processing unit (GPU) have been used to make high-performance parallel computations available, and attractive to common consumers for computing massively parallel reconstruction problems at commodity price. GPUs have also become more and more important for reconstruction computations, especially when deep learning starts to be applied into MRI reconstruction. The motivation of this survey is to review the image reconstruction schemes of GPU computing for MRI applications and provide a summary reference for researchers in MRI community.

Parallel Computer System for 3D Visualization Stereo on GPU

NASA Astrophysics Data System (ADS)

Al-Oraiqat, Anas M.; Zori, Sergii A.

2018-03-01

This paper proposes the organization of a parallel computer system based on Graphic Processors Unit (GPU) for 3D stereo image synthesis. The development is based on the modified ray tracing method developed by the authors for fast search of tracing rays intersections with scene objects. The system allows significant increase in the productivity for the 3D stereo synthesis of photorealistic quality. The generalized procedure of 3D stereo image synthesis on the Graphics Processing Unit/Graphics Processing Clusters (GPU/GPC) is proposed. The efficiency of the proposed solutions by GPU implementation is compared with single-threaded and multithreaded implementations on the CPU. The achieved average acceleration in multi-thread implementation on the test GPU and CPU is about 7.5 and 1.6 times, respectively. Studying the influence of choosing the size and configuration of the computational Compute Unified Device Archi-tecture (CUDA) network on the computational speed shows the importance of their correct selection. The obtained experimental estimations can be significantly improved by new GPUs with a large number of processing cores and multiprocessors, as well as optimized configuration of the computing CUDA network.

GPU-based RFA simulation for minimally invasive cancer treatment of liver tumours.

PubMed

Mariappan, Panchatcharam; Weir, Phil; Flanagan, Ronan; Voglreiter, Philip; Alhonnoro, Tuomas; Pollari, Mika; Moche, Michael; Busse, Harald; Futterer, Jurgen; Portugaller, Horst Rupert; Sequeiros, Roberto Blanco; Kolesnik, Marina

2017-01-01

Radiofrequency ablation (RFA) is one of the most popular and well-standardized minimally invasive cancer treatments (MICT) for liver tumours, employed where surgical resection has been contraindicated. Less-experienced interventional radiologists (IRs) require an appropriate planning tool for the treatment to help avoid incomplete treatment and so reduce the tumour recurrence risk. Although a few tools are available to predict the ablation lesion geometry, the process is computationally expensive. Also, in our implementation, a few patient-specific parameters are used to improve the accuracy of the lesion prediction. Advanced heterogeneous computing using personal computers, incorporating the graphics processing unit (GPU) and the central processing unit (CPU), is proposed to predict the ablation lesion geometry. The most recent GPU technology is used to accelerate the finite element approximation of Penne's bioheat equation and a three state cell model. Patient-specific input parameters are used in the bioheat model to improve accuracy of the predicted lesion. A fast GPU-based RFA solver is developed to predict the lesion by doing most of the computational tasks in the GPU, while reserving the CPU for concurrent tasks such as lesion extraction based on the heat deposition at each finite element node. The solver takes less than 3 min for a treatment duration of 26 min. When the model receives patient-specific input parameters, the deviation between real and predicted lesion is below 3 mm. A multi-centre retrospective study indicates that the fast RFA solver is capable of providing the IR with the predicted lesion in the short time period before the intervention begins when the patient has been clinically prepared for the treatment.

WE-G-18A-04: 3D Dictionary Learning Based Statistical Iterative Reconstruction for Low-Dose Cone Beam CT Imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bai, T; UT Southwestern Medical Center, Dallas, TX; Yan, H

2014-06-15

Purpose: To develop a 3D dictionary learning based statistical reconstruction algorithm on graphic processing units (GPU), to improve the quality of low-dose cone beam CT (CBCT) imaging with high efficiency. Methods: A 3D dictionary containing 256 small volumes (atoms) of 3x3x3 voxels was trained from a high quality volume image. During reconstruction, we utilized a Cholesky decomposition based orthogonal matching pursuit algorithm to find a sparse representation on this dictionary basis of each patch in the reconstructed image, in order to regularize the image quality. To accelerate the time-consuming sparse coding in the 3D case, we implemented our algorithm inmore » a parallel fashion by taking advantage of the tremendous computational power of GPU. Evaluations are performed based on a head-neck patient case. FDK reconstruction with full dataset of 364 projections is used as the reference. We compared the proposed 3D dictionary learning based method with a tight frame (TF) based one using a subset data of 121 projections. The image qualities under different resolutions in z-direction, with or without statistical weighting are also studied. Results: Compared to the TF-based CBCT reconstruction, our experiments indicated that 3D dictionary learning based CBCT reconstruction is able to recover finer structures, to remove more streaking artifacts, and is less susceptible to blocky artifacts. It is also observed that statistical reconstruction approach is sensitive to inconsistency between the forward and backward projection operations in parallel computing. Using high a spatial resolution along z direction helps improving the algorithm robustness. Conclusion: 3D dictionary learning based CBCT reconstruction algorithm is able to sense the structural information while suppressing noise, and hence to achieve high quality reconstruction. The GPU realization of the whole algorithm offers a significant efficiency enhancement, making this algorithm more feasible for potential clinical application. A high zresolution is preferred to stabilize statistical iterative reconstruction. This work was supported in part by NIH(1R01CA154747-01), NSFC((No. 61172163), Research Fund for the Doctoral Program of Higher Education of China (No. 20110201110011), China Scholarship Council.« less

CUDASW++ 3.0: accelerating Smith-Waterman protein database search by coupling CPU and GPU SIMD instructions.

PubMed

Liu, Yongchao; Wirawan, Adrianto; Schmidt, Bertil

2013-04-04

The maximal sensitivity for local alignments makes the Smith-Waterman algorithm a popular choice for protein sequence database search based on pairwise alignment. However, the algorithm is compute-intensive due to a quadratic time complexity. Corresponding runtimes are further compounded by the rapid growth of sequence databases. We present CUDASW++ 3.0, a fast Smith-Waterman protein database search algorithm, which couples CPU and GPU SIMD instructions and carries out concurrent CPU and GPU computations. For the CPU computation, this algorithm employs SSE-based vector execution units as accelerators. For the GPU computation, we have investigated for the first time a GPU SIMD parallelization, which employs CUDA PTX SIMD video instructions to gain more data parallelism beyond the SIMT execution model. Moreover, sequence alignment workloads are automatically distributed over CPUs and GPUs based on their respective compute capabilities. Evaluation on the Swiss-Prot database shows that CUDASW++ 3.0 gains a performance improvement over CUDASW++ 2.0 up to 2.9 and 3.2, with a maximum performance of 119.0 and 185.6 GCUPS, on a single-GPU GeForce GTX 680 and a dual-GPU GeForce GTX 690 graphics card, respectively. In addition, our algorithm has demonstrated significant speedups over other top-performing tools: SWIPE and BLAST+. CUDASW++ 3.0 is written in CUDA C++ and PTX assembly languages, targeting GPUs based on the Kepler architecture. This algorithm obtains significant speedups over its predecessor: CUDASW++ 2.0, by benefiting from the use of CPU and GPU SIMD instructions as well as the concurrent execution on CPUs and GPUs. The source code and the simulated data are available at http://cudasw.sourceforge.net.

Fast 3D elastic micro-seismic source location using new GPU features

NASA Astrophysics Data System (ADS)

Xue, Qingfeng; Wang, Yibo; Chang, Xu

2016-12-01

In this paper, we describe new GPU features and their applications in passive seismic - micro-seismic location. Locating micro-seismic events is quite important in seismic exploration, especially when searching for unconventional oil and gas resources. Different from the traditional ray-based methods, the wave equation method, such as the method we use in our paper, has a remarkable advantage in adapting to low signal-to-noise ratio conditions and does not need a person to select the data. However, because it has a conspicuous deficiency due to its computation cost, these methods are not widely used in industrial fields. To make the method useful, we implement imaging-like wave equation micro-seismic location in a 3D elastic media and use GPU to accelerate our algorithm. We also introduce some new GPU features into the implementation to solve the data transfer and GPU utilization problems. Numerical and field data experiments show that our method can achieve a more than 30% performance improvement in GPU implementation just by using these new features.

irGPU.proton.Net: Irregular strong charge interaction networks of protonatable groups in protein molecules--a GPU solver using the fast multipole method and statistical thermodynamics.

PubMed

Kantardjiev, Alexander A

2015-04-05

A cluster of strongly interacting ionization groups in protein molecules with irregular ionization behavior is suggestive for specific structure-function relationship. However, their computational treatment is unconventional (e.g., lack of convergence in naive self-consistent iterative algorithm). The stringent evaluation requires evaluation of Boltzmann averaged statistical mechanics sums and electrostatic energy estimation for each microstate. irGPU: Irregular strong interactions in proteins--a GPU solver is novel solution to a versatile problem in protein biophysics--atypical protonation behavior of coupled groups. The computational severity of the problem is alleviated by parallelization (via GPU kernels) which is applied for the electrostatic interaction evaluation (including explicit electrostatics via the fast multipole method) as well as statistical mechanics sums (partition function) estimation. Special attention is given to the ease of the service and encapsulation of theoretical details without sacrificing rigor of computational procedures. irGPU is not just a solution-in-principle but a promising practical application with potential to entice community into deeper understanding of principles governing biomolecule mechanisms. © 2015 Wiley Periodicals, Inc.

Overview of implementation of DARPA GPU program in SAIC

NASA Astrophysics Data System (ADS)

Braunreiter, Dennis; Furtek, Jeremy; Chen, Hai-Wen; Healy, Dennis

2008-04-01

This paper reviews the implementation of DARPA MTO STAP-BOY program for both Phase I and II conducted at Science Applications International Corporation (SAIC). The STAP-BOY program conducts fast covariance factorization and tuning techniques for space-time adaptive process (STAP) Algorithm Implementation on Graphics Processor unit (GPU) Architectures for Embedded Systems. The first part of our presentation on the DARPA STAP-BOY program will focus on GPU implementation and algorithm innovations for a prototype radar STAP algorithm. The STAP algorithm will be implemented on the GPU, using stream programming (from companies such as PeakStream, ATI Technologies' CTM, and NVIDIA) and traditional graphics APIs. This algorithm will include fast range adaptive STAP weight updates and beamforming applications, each of which has been modified to exploit the parallel nature of graphics architectures.

High-throughput GPU-based LDPC decoding

NASA Astrophysics Data System (ADS)

Chang, Yang-Lang; Chang, Cheng-Chun; Huang, Min-Yu; Huang, Bormin

2010-08-01

Low-density parity-check (LDPC) code is a linear block code known to approach the Shannon limit via the iterative sum-product algorithm. LDPC codes have been adopted in most current communication systems such as DVB-S2, WiMAX, WI-FI and 10GBASE-T. LDPC for the needs of reliable and flexible communication links for a wide variety of communication standards and configurations have inspired the demand for high-performance and flexibility computing. Accordingly, finding a fast and reconfigurable developing platform for designing the high-throughput LDPC decoder has become important especially for rapidly changing communication standards and configurations. In this paper, a new graphic-processing-unit (GPU) LDPC decoding platform with the asynchronous data transfer is proposed to realize this practical implementation. Experimental results showed that the proposed GPU-based decoder achieved 271x speedup compared to its CPU-based counterpart. It can serve as a high-throughput LDPC decoder.

GPU-accelerated Kernel Regression Reconstruction for Freehand 3D Ultrasound Imaging.

PubMed

Wen, Tiexiang; Li, Ling; Zhu, Qingsong; Qin, Wenjian; Gu, Jia; Yang, Feng; Xie, Yaoqin

2017-07-01

Volume reconstruction method plays an important role in improving reconstructed volumetric image quality for freehand three-dimensional (3D) ultrasound imaging. By utilizing the capability of programmable graphics processing unit (GPU), we can achieve a real-time incremental volume reconstruction at a speed of 25-50 frames per second (fps). After incremental reconstruction and visualization, hole-filling is performed on GPU to fill remaining empty voxels. However, traditional pixel nearest neighbor-based hole-filling fails to reconstruct volume with high image quality. On the contrary, the kernel regression provides an accurate volume reconstruction method for 3D ultrasound imaging but with the cost of heavy computational complexity. In this paper, a GPU-based fast kernel regression method is proposed for high-quality volume after the incremental reconstruction of freehand ultrasound. The experimental results show that improved image quality for speckle reduction and details preservation can be obtained with the parameter setting of kernel window size of [Formula: see text] and kernel bandwidth of 1.0. The computational performance of the proposed GPU-based method can be over 200 times faster than that on central processing unit (CPU), and the volume with size of 50 million voxels in our experiment can be reconstructed within 10 seconds.

Accelerating Computation of DCM for ERP in MATLAB by External Function Calls to the GPU.

PubMed

Wang, Wei-Jen; Hsieh, I-Fan; Chen, Chun-Chuan

2013-01-01

This study aims to improve the performance of Dynamic Causal Modelling for Event Related Potentials (DCM for ERP) in MATLAB by using external function calls to a graphics processing unit (GPU). DCM for ERP is an advanced method for studying neuronal effective connectivity. DCM utilizes an iterative procedure, the expectation maximization (EM) algorithm, to find the optimal parameters given a set of observations and the underlying probability model. As the EM algorithm is computationally demanding and the analysis faces possible combinatorial explosion of models to be tested, we propose a parallel computing scheme using the GPU to achieve a fast estimation of DCM for ERP. The computation of DCM for ERP is dynamically partitioned and distributed to threads for parallel processing, according to the DCM model complexity and the hardware constraints. The performance efficiency of this hardware-dependent thread arrangement strategy was evaluated using the synthetic data. The experimental data were used to validate the accuracy of the proposed computing scheme and quantify the time saving in practice. The simulation results show that the proposed scheme can accelerate the computation by a factor of 155 for the parallel part. For experimental data, the speedup factor is about 7 per model on average, depending on the model complexity and the data. This GPU-based implementation of DCM for ERP gives qualitatively the same results as the original MATLAB implementation does at the group level analysis. In conclusion, we believe that the proposed GPU-based implementation is very useful for users as a fast screen tool to select the most likely model and may provide implementation guidance for possible future clinical applications such as online diagnosis.

Accelerating Computation of DCM for ERP in MATLAB by External Function Calls to the GPU

PubMed Central

Wang, Wei-Jen; Hsieh, I-Fan; Chen, Chun-Chuan

2013-01-01

This study aims to improve the performance of Dynamic Causal Modelling for Event Related Potentials (DCM for ERP) in MATLAB by using external function calls to a graphics processing unit (GPU). DCM for ERP is an advanced method for studying neuronal effective connectivity. DCM utilizes an iterative procedure, the expectation maximization (EM) algorithm, to find the optimal parameters given a set of observations and the underlying probability model. As the EM algorithm is computationally demanding and the analysis faces possible combinatorial explosion of models to be tested, we propose a parallel computing scheme using the GPU to achieve a fast estimation of DCM for ERP. The computation of DCM for ERP is dynamically partitioned and distributed to threads for parallel processing, according to the DCM model complexity and the hardware constraints. The performance efficiency of this hardware-dependent thread arrangement strategy was evaluated using the synthetic data. The experimental data were used to validate the accuracy of the proposed computing scheme and quantify the time saving in practice. The simulation results show that the proposed scheme can accelerate the computation by a factor of 155 for the parallel part. For experimental data, the speedup factor is about 7 per model on average, depending on the model complexity and the data. This GPU-based implementation of DCM for ERP gives qualitatively the same results as the original MATLAB implementation does at the group level analysis. In conclusion, we believe that the proposed GPU-based implementation is very useful for users as a fast screen tool to select the most likely model and may provide implementation guidance for possible future clinical applications such as online diagnosis. PMID:23840507

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lindstrom, P; Cohen, J D

We present a streaming geometry compression codec for multiresolution, uniformly-gridded, triangular terrain patches that supports very fast decompression. Our method is based on linear prediction and residual coding for lossless compression of the full-resolution data. As simplified patches on coarser levels in the hierarchy already incur some data loss, we optionally allow further quantization for more lossy compression. The quantization levels are adaptive on a per-patch basis, while still permitting seamless, adaptive tessellations of the terrain. Our geometry compression on such a hierarchy achieves compression ratios of 3:1 to 12:1. Our scheme is not only suitable for fast decompression onmore » the CPU, but also for parallel decoding on the GPU with peak throughput over 2 billion triangles per second. Each terrain patch is independently decompressed on the fly from a variable-rate bitstream by a GPU geometry program with no branches or conditionals. Thus we can store the geometry compressed on the GPU, reducing storage and bandwidth requirements throughout the system. In our rendering approach, only compressed bitstreams and the decoded height values in the view-dependent 'cut' are explicitly stored on the GPU. Normal vectors are computed in a streaming fashion, and remaining geometry and texture coordinates, as well as mesh connectivity, are shared and re-used for all patches. We demonstrate and evaluate our algorithms on a small prototype system in which all compressed geometry fits in the GPU memory and decompression occurs on the fly every rendering frame without any cache maintenance.« less

Fully iterative scatter corrected digital breast tomosynthesis using GPU-based fast Monte Carlo simulation and composition ratio update

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, Kyungsang; Ye, Jong Chul, E-mail: jong.ye@kaist.ac.kr; Lee, Taewon

2015-09-15

Purpose: In digital breast tomosynthesis (DBT), scatter correction is highly desirable, as it improves image quality at low doses. Because the DBT detector panel is typically stationary during the source rotation, antiscatter grids are not generally compatible with DBT; thus, a software-based scatter correction is required. This work proposes a fully iterative scatter correction method that uses a novel fast Monte Carlo simulation (MCS) with a tissue-composition ratio estimation technique for DBT imaging. Methods: To apply MCS to scatter estimation, the material composition in each voxel should be known. To overcome the lack of prior accurate knowledge of tissue compositionmore » for DBT, a tissue-composition ratio is estimated based on the observation that the breast tissues are principally composed of adipose and glandular tissues. Using this approximation, the composition ratio can be estimated from the reconstructed attenuation coefficients, and the scatter distribution can then be estimated by MCS using the composition ratio. The scatter estimation and image reconstruction procedures can be performed iteratively until an acceptable accuracy is achieved. For practical use, (i) the authors have implemented a fast MCS using a graphics processing unit (GPU), (ii) the MCS is simplified to transport only x-rays in the energy range of 10–50 keV, modeling Rayleigh and Compton scattering and the photoelectric effect using the tissue-composition ratio of adipose and glandular tissues, and (iii) downsampling is used because the scatter distribution varies rather smoothly. Results: The authors have demonstrated that the proposed method can accurately estimate the scatter distribution, and that the contrast-to-noise ratio of the final reconstructed image is significantly improved. The authors validated the performance of the MCS by changing the tissue thickness, composition ratio, and x-ray energy. The authors confirmed that the tissue-composition ratio estimation was quite accurate under a variety of conditions. Our GPU-based fast MCS implementation took approximately 3 s to generate each angular projection for a 6 cm thick breast, which is believed to make this process acceptable for clinical applications. In addition, the clinical preferences of three radiologists were evaluated; the preference for the proposed method compared to the preference for the convolution-based method was statistically meaningful (p < 0.05, McNemar test). Conclusions: The proposed fully iterative scatter correction method and the GPU-based fast MCS using tissue-composition ratio estimation successfully improved the image quality within a reasonable computational time, which may potentially increase the clinical utility of DBT.« less

GPU-accelerated Monte Carlo convolution/superposition implementation for dose calculation.

PubMed

Zhou, Bo; Yu, Cedric X; Chen, Danny Z; Hu, X Sharon

2010-11-01

Dose calculation is a key component in radiation treatment planning systems. Its performance and accuracy are crucial to the quality of treatment plans as emerging advanced radiation therapy technologies are exerting ever tighter constraints on dose calculation. A common practice is to choose either a deterministic method such as the convolution/superposition (CS) method for speed or a Monte Carlo (MC) method for accuracy. The goal of this work is to boost the performance of a hybrid Monte Carlo convolution/superposition (MCCS) method by devising a graphics processing unit (GPU) implementation so as to make the method practical for day-to-day usage. Although the MCCS algorithm combines the merits of MC fluence generation and CS fluence transport, it is still not fast enough to be used as a day-to-day planning tool. To alleviate the speed issue of MC algorithms, the authors adopted MCCS as their target method and implemented a GPU-based version. In order to fully utilize the GPU computing power, the MCCS algorithm is modified to match the GPU hardware architecture. The performance of the authors' GPU-based implementation on an Nvidia GTX260 card is compared to a multithreaded software implementation on a quad-core system. A speedup in the range of 6.7-11.4x is observed for the clinical cases used. The less than 2% statistical fluctuation also indicates that the accuracy of the authors' GPU-based implementation is in good agreement with the results from the quad-core CPU implementation. This work shows that GPU is a feasible and cost-efficient solution compared to other alternatives such as using cluster machines or field-programmable gate arrays for satisfying the increasing demands on computation speed and accuracy of dose calculation. But there are also inherent limitations of using GPU for accelerating MC-type applications, which are also analyzed in detail in this article.

Heterogeneous computing architecture for fast detection of SNP-SNP interactions.

PubMed

Sluga, Davor; Curk, Tomaz; Zupan, Blaz; Lotric, Uros

2014-06-25

The extent of data in a typical genome-wide association study (GWAS) poses considerable computational challenges to software tools for gene-gene interaction discovery. Exhaustive evaluation of all interactions among hundreds of thousands to millions of single nucleotide polymorphisms (SNPs) may require weeks or even months of computation. Massively parallel hardware within a modern Graphic Processing Unit (GPU) and Many Integrated Core (MIC) coprocessors can shorten the run time considerably. While the utility of GPU-based implementations in bioinformatics has been well studied, MIC architecture has been introduced only recently and may provide a number of comparative advantages that have yet to be explored and tested. We have developed a heterogeneous, GPU and Intel MIC-accelerated software module for SNP-SNP interaction discovery to replace the previously single-threaded computational core in the interactive web-based data exploration program SNPsyn. We report on differences between these two modern massively parallel architectures and their software environments. Their utility resulted in an order of magnitude shorter execution times when compared to the single-threaded CPU implementation. GPU implementation on a single Nvidia Tesla K20 runs twice as fast as that for the MIC architecture-based Xeon Phi P5110 coprocessor, but also requires considerably more programming effort. General purpose GPUs are a mature platform with large amounts of computing power capable of tackling inherently parallel problems, but can prove demanding for the programmer. On the other hand the new MIC architecture, albeit lacking in performance reduces the programming effort and makes it up with a more general architecture suitable for a wider range of problems.

Heterogeneous computing architecture for fast detection of SNP-SNP interactions

PubMed Central

2014-01-01

Background The extent of data in a typical genome-wide association study (GWAS) poses considerable computational challenges to software tools for gene-gene interaction discovery. Exhaustive evaluation of all interactions among hundreds of thousands to millions of single nucleotide polymorphisms (SNPs) may require weeks or even months of computation. Massively parallel hardware within a modern Graphic Processing Unit (GPU) and Many Integrated Core (MIC) coprocessors can shorten the run time considerably. While the utility of GPU-based implementations in bioinformatics has been well studied, MIC architecture has been introduced only recently and may provide a number of comparative advantages that have yet to be explored and tested. Results We have developed a heterogeneous, GPU and Intel MIC-accelerated software module for SNP-SNP interaction discovery to replace the previously single-threaded computational core in the interactive web-based data exploration program SNPsyn. We report on differences between these two modern massively parallel architectures and their software environments. Their utility resulted in an order of magnitude shorter execution times when compared to the single-threaded CPU implementation. GPU implementation on a single Nvidia Tesla K20 runs twice as fast as that for the MIC architecture-based Xeon Phi P5110 coprocessor, but also requires considerably more programming effort. Conclusions General purpose GPUs are a mature platform with large amounts of computing power capable of tackling inherently parallel problems, but can prove demanding for the programmer. On the other hand the new MIC architecture, albeit lacking in performance reduces the programming effort and makes it up with a more general architecture suitable for a wider range of problems. PMID:24964802

Fast parallel tandem mass spectral library searching using GPU hardware acceleration.

PubMed

Baumgardner, Lydia Ashleigh; Shanmugam, Avinash Kumar; Lam, Henry; Eng, Jimmy K; Martin, Daniel B

2011-06-03

Mass spectrometry-based proteomics is a maturing discipline of biologic research that is experiencing substantial growth. Instrumentation has steadily improved over time with the advent of faster and more sensitive instruments collecting ever larger data files. Consequently, the computational process of matching a peptide fragmentation pattern to its sequence, traditionally accomplished by sequence database searching and more recently also by spectral library searching, has become a bottleneck in many mass spectrometry experiments. In both of these methods, the main rate-limiting step is the comparison of an acquired spectrum with all potential matches from a spectral library or sequence database. This is a highly parallelizable process because the core computational element can be represented as a simple but arithmetically intense multiplication of two vectors. In this paper, we present a proof of concept project taking advantage of the massively parallel computing available on graphics processing units (GPUs) to distribute and accelerate the process of spectral assignment using spectral library searching. This program, which we have named FastPaSS (for Fast Parallelized Spectral Searching), is implemented in CUDA (Compute Unified Device Architecture) from NVIDIA, which allows direct access to the processors in an NVIDIA GPU. Our efforts demonstrate the feasibility of GPU computing for spectral assignment, through implementation of the validated spectral searching algorithm SpectraST in the CUDA environment.

GPU-based low-level trigger system for the standalone reconstruction of the ring-shaped hit patterns in the RICH Cherenkov detector of NA62 experiment

NASA Astrophysics Data System (ADS)

Ammendola, R.; Biagioni, A.; Chiozzi, S.; Cretaro, P.; Cotta Ramusino, A.; Di Lorenzo, S.; Fantechi, R.; Fiorini, M.; Frezza, O.; Gianoli, A.; Lamanna, G.; Lo Cicero, F.; Lonardo, A.; Martinelli, M.; Neri, I.; Paolucci, P. S.; Pastorelli, E.; Piandani, R.; Piccini, M.; Pontisso, L.; Rossetti, D.; Simula, F.; Sozzi, M.; Vicini, P.

2017-03-01

This project aims to exploit the parallel computing power of a commercial Graphics Processing Unit (GPU) to implement fast pattern matching in the Ring Imaging Cherenkov (RICH) detector for the level 0 (L0) trigger of the NA62 experiment. In this approach, the ring-fitting algorithm is seedless, being fed with raw RICH data, with no previous information on the ring position from other detectors. Moreover, since the L0 trigger is provided with a more elaborated information than a simple multiplicity number, it results in a higher selection power. Two methods have been studied in order to reduce the data transfer latency from the readout boards of the detector to the GPU, i.e., the use of a dedicated NIC device driver with very low latency and a direct data transfer protocol from a custom FPGA-based NIC to the GPU. The performance of the system, developed through the FPGA approach, for multi-ring Cherenkov online reconstruction obtained during the NA62 physics runs is presented.

Use of a graphics processing unit (GPU) to facilitate real-time 3D graphic presentation of the patient skin-dose distribution during fluoroscopic interventional procedures

PubMed Central

Rana, Vijay; Rudin, Stephen; Bednarek, Daniel R.

2012-01-01

We have developed a dose-tracking system (DTS) that calculates the radiation dose to the patient’s skin in real-time by acquiring exposure parameters and imaging-system-geometry from the digital bus on a Toshiba Infinix C-arm unit. The cumulative dose values are then displayed as a color map on an OpenGL-based 3D graphic of the patient for immediate feedback to the interventionalist. Determination of those elements on the surface of the patient 3D-graphic that intersect the beam and calculation of the dose for these elements in real time demands fast computation. Reducing the size of the elements results in more computation load on the computer processor and therefore a tradeoff occurs between the resolution of the patient graphic and the real-time performance of the DTS. The speed of the DTS for calculating dose to the skin is limited by the central processing unit (CPU) and can be improved by using the parallel processing power of a graphics processing unit (GPU). Here, we compare the performance speed of GPU-based DTS software to that of the current CPU-based software as a function of the resolution of the patient graphics. Results show a tremendous improvement in speed using the GPU. While an increase in the spatial resolution of the patient graphics resulted in slowing down the computational speed of the DTS on the CPU, the speed of the GPU-based DTS was hardly affected. This GPU-based DTS can be a powerful tool for providing accurate, real-time feedback about patient skin-dose to physicians while performing interventional procedures. PMID:24027616

Use of a graphics processing unit (GPU) to facilitate real-time 3D graphic presentation of the patient skin-dose distribution during fluoroscopic interventional procedures.

PubMed

Rana, Vijay; Rudin, Stephen; Bednarek, Daniel R

2012-02-23

We have developed a dose-tracking system (DTS) that calculates the radiation dose to the patient's skin in real-time by acquiring exposure parameters and imaging-system-geometry from the digital bus on a Toshiba Infinix C-arm unit. The cumulative dose values are then displayed as a color map on an OpenGL-based 3D graphic of the patient for immediate feedback to the interventionalist. Determination of those elements on the surface of the patient 3D-graphic that intersect the beam and calculation of the dose for these elements in real time demands fast computation. Reducing the size of the elements results in more computation load on the computer processor and therefore a tradeoff occurs between the resolution of the patient graphic and the real-time performance of the DTS. The speed of the DTS for calculating dose to the skin is limited by the central processing unit (CPU) and can be improved by using the parallel processing power of a graphics processing unit (GPU). Here, we compare the performance speed of GPU-based DTS software to that of the current CPU-based software as a function of the resolution of the patient graphics. Results show a tremendous improvement in speed using the GPU. While an increase in the spatial resolution of the patient graphics resulted in slowing down the computational speed of the DTS on the CPU, the speed of the GPU-based DTS was hardly affected. This GPU-based DTS can be a powerful tool for providing accurate, real-time feedback about patient skin-dose to physicians while performing interventional procedures.

GPU applications for data processing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vladymyrov, Mykhailo, E-mail: mykhailo.vladymyrov@cern.ch; Aleksandrov, Andrey; INFN sezione di Napoli, I-80125 Napoli

2015-12-31

Modern experiments that use nuclear photoemulsion imply fast and efficient data acquisition from the emulsion can be performed. The new approaches in developing scanning systems require real-time processing of large amount of data. Methods that use Graphical Processing Unit (GPU) computing power for emulsion data processing are presented here. It is shown how the GPU-accelerated emulsion processing helped us to rise the scanning speed by factor of nine.

MIGS-GPU: Microarray Image Gridding and Segmentation on the GPU.

PubMed

Katsigiannis, Stamos; Zacharia, Eleni; Maroulis, Dimitris

2017-05-01

Complementary DNA (cDNA) microarray is a powerful tool for simultaneously studying the expression level of thousands of genes. Nevertheless, the analysis of microarray images remains an arduous and challenging task due to the poor quality of the images that often suffer from noise, artifacts, and uneven background. In this study, the MIGS-GPU [Microarray Image Gridding and Segmentation on Graphics Processing Unit (GPU)] software for gridding and segmenting microarray images is presented. MIGS-GPU's computations are performed on the GPU by means of the compute unified device architecture (CUDA) in order to achieve fast performance and increase the utilization of available system resources. Evaluation on both real and synthetic cDNA microarray images showed that MIGS-GPU provides better performance than state-of-the-art alternatives, while the proposed GPU implementation achieves significantly lower computational times compared to the respective CPU approaches. Consequently, MIGS-GPU can be an advantageous and useful tool for biomedical laboratories, offering a user-friendly interface that requires minimum input in order to run.

Next-generation acceleration and code optimization for light transport in turbid media using GPUs

PubMed Central

Alerstam, Erik; Lo, William Chun Yip; Han, Tianyi David; Rose, Jonathan; Andersson-Engels, Stefan; Lilge, Lothar

2010-01-01

A highly optimized Monte Carlo (MC) code package for simulating light transport is developed on the latest graphics processing unit (GPU) built for general-purpose computing from NVIDIA - the Fermi GPU. In biomedical optics, the MC method is the gold standard approach for simulating light transport in biological tissue, both due to its accuracy and its flexibility in modelling realistic, heterogeneous tissue geometry in 3-D. However, the widespread use of MC simulations in inverse problems, such as treatment planning for PDT, is limited by their long computation time. Despite its parallel nature, optimizing MC code on the GPU has been shown to be a challenge, particularly when the sharing of simulation result matrices among many parallel threads demands the frequent use of atomic instructions to access the slow GPU global memory. This paper proposes an optimization scheme that utilizes the fast shared memory to resolve the performance bottleneck caused by atomic access, and discusses numerous other optimization techniques needed to harness the full potential of the GPU. Using these techniques, a widely accepted MC code package in biophotonics, called MCML, was successfully accelerated on a Fermi GPU by approximately 600x compared to a state-of-the-art Intel Core i7 CPU. A skin model consisting of 7 layers was used as the standard simulation geometry. To demonstrate the possibility of GPU cluster computing, the same GPU code was executed on four GPUs, showing a linear improvement in performance with an increasing number of GPUs. The GPU-based MCML code package, named GPU-MCML, is compatible with a wide range of graphics cards and is released as an open-source software in two versions: an optimized version tuned for high performance and a simplified version for beginners (http://code.google.com/p/gpumcml). PMID:21258498

GPU accelerated generation of digitally reconstructed radiographs for 2-D/3-D image registration.

PubMed

Dorgham, Osama M; Laycock, Stephen D; Fisher, Mark H

2012-09-01

Recent advances in programming languages for graphics processing units (GPUs) provide developers with a convenient way of implementing applications which can be executed on the CPU and GPU interchangeably. GPUs are becoming relatively cheap, powerful, and widely available hardware components, which can be used to perform intensive calculations. The last decade of hardware performance developments shows that GPU-based computation is progressing significantly faster than CPU-based computation, particularly if one considers the execution of highly parallelisable algorithms. Future predictions illustrate that this trend is likely to continue. In this paper, we introduce a way of accelerating 2-D/3-D image registration by developing a hybrid system which executes on the CPU and utilizes the GPU for parallelizing the generation of digitally reconstructed radiographs (DRRs). Based on the advancements of the GPU over the CPU, it is timely to exploit the benefits of many-core GPU technology by developing algorithms for DRR generation. Although some previous work has investigated the rendering of DRRs using the GPU, this paper investigates approximations which reduce the computational overhead while still maintaining a quality consistent with that needed for 2-D/3-D registration with sufficient accuracy to be clinically acceptable in certain applications of radiation oncology. Furthermore, by comparing implementations of 2-D/3-D registration on the CPU and GPU, we investigate current performance and propose an optimal framework for PC implementations addressing the rigid registration problem. Using this framework, we are able to render DRR images from a 256×256×133 CT volume in ~24 ms using an NVidia GeForce 8800 GTX and in ~2 ms using NVidia GeForce GTX 580. In addition to applications requiring fast automatic patient setup, these levels of performance suggest image-guided radiation therapy at video frame rates is technically feasible using relatively low cost PC architecture.

Improved preconditioned conjugate gradient algorithm and application in 3D inversion of gravity-gradiometry data

NASA Astrophysics Data System (ADS)

Wang, Tai-Han; Huang, Da-Nian; Ma, Guo-Qing; Meng, Zhao-Hai; Li, Ye

2017-06-01

With the continuous development of full tensor gradiometer (FTG) measurement techniques, three-dimensional (3D) inversion of FTG data is becoming increasingly used in oil and gas exploration. In the fast processing and interpretation of large-scale high-precision data, the use of the graphics processing unit process unit (GPU) and preconditioning methods are very important in the data inversion. In this paper, an improved preconditioned conjugate gradient algorithm is proposed by combining the symmetric successive over-relaxation (SSOR) technique and the incomplete Choleksy decomposition conjugate gradient algorithm (ICCG). Since preparing the preconditioner requires extra time, a parallel implement based on GPU is proposed. The improved method is then applied in the inversion of noisecontaminated synthetic data to prove its adaptability in the inversion of 3D FTG data. Results show that the parallel SSOR-ICCG algorithm based on NVIDIA Tesla C2050 GPU achieves a speedup of approximately 25 times that of a serial program using a 2.0 GHz Central Processing Unit (CPU). Real airborne gravity-gradiometry data from Vinton salt dome (southwest Louisiana, USA) are also considered. Good results are obtained, which verifies the efficiency and feasibility of the proposed parallel method in fast inversion of 3D FTG data.

A GPU-accelerated and Monte Carlo-based intensity modulated proton therapy optimization system.

PubMed

Ma, Jiasen; Beltran, Chris; Seum Wan Chan Tseung, Hok; Herman, Michael G

2014-12-01

Conventional spot scanning intensity modulated proton therapy (IMPT) treatment planning systems (TPSs) optimize proton spot weights based on analytical dose calculations. These analytical dose calculations have been shown to have severe limitations in heterogeneous materials. Monte Carlo (MC) methods do not have these limitations; however, MC-based systems have been of limited clinical use due to the large number of beam spots in IMPT and the extremely long calculation time of traditional MC techniques. In this work, the authors present a clinically applicable IMPT TPS that utilizes a very fast MC calculation. An in-house graphics processing unit (GPU)-based MC dose calculation engine was employed to generate the dose influence map for each proton spot. With the MC generated influence map, a modified least-squares optimization method was used to achieve the desired dose volume histograms (DVHs). The intrinsic CT image resolution was adopted for voxelization in simulation and optimization to preserve spatial resolution. The optimizations were computed on a multi-GPU framework to mitigate the memory limitation issues for the large dose influence maps that resulted from maintaining the intrinsic CT resolution. The effects of tail cutoff and starting condition were studied and minimized in this work. For relatively large and complex three-field head and neck cases, i.e., >100,000 spots with a target volume of ∼ 1000 cm(3) and multiple surrounding critical structures, the optimization together with the initial MC dose influence map calculation was done in a clinically viable time frame (less than 30 min) on a GPU cluster consisting of 24 Nvidia GeForce GTX Titan cards. The in-house MC TPS plans were comparable to a commercial TPS plans based on DVH comparisons. A MC-based treatment planning system was developed. The treatment planning can be performed in a clinically viable time frame on a hardware system costing around 45,000 dollars. The fast calculation and optimization make the system easily expandable to robust and multicriteria optimization.

Fast parallel tandem mass spectral library searching using GPU hardware acceleration

PubMed Central

Baumgardner, Lydia Ashleigh; Shanmugam, Avinash Kumar; Lam, Henry; Eng, Jimmy K.; Martin, Daniel B.

2011-01-01

Mass spectrometry-based proteomics is a maturing discipline of biologic research that is experiencing substantial growth. Instrumentation has steadily improved over time with the advent of faster and more sensitive instruments collecting ever larger data files. Consequently, the computational process of matching a peptide fragmentation pattern to its sequence, traditionally accomplished by sequence database searching and more recently also by spectral library searching, has become a bottleneck in many mass spectrometry experiments. In both of these methods, the main rate limiting step is the comparison of an acquired spectrum with all potential matches from a spectral library or sequence database. This is a highly parallelizable process because the core computational element can be represented as a simple but arithmetically intense multiplication of two vectors. In this paper we present a proof of concept project taking advantage of the massively parallel computing available on graphics processing units (GPUs) to distribute and accelerate the process of spectral assignment using spectral library searching. This program, which we have named FastPaSS (for Fast Parallelized Spectral Searching) is implemented in CUDA (Compute Unified Device Architecture) from NVIDIA which allows direct access to the processors in an NVIDIA GPU. Our efforts demonstrate the feasibility of GPU computing for spectral assignment, through implementation of the validated spectral searching algorithm SpectraST in the CUDA environment. PMID:21545112

GPU based framework for geospatial analyses

NASA Astrophysics Data System (ADS)

Cosmin Sandric, Ionut; Ionita, Cristian; Dardala, Marian; Furtuna, Titus

2017-04-01

Parallel processing on multiple CPU cores is already used at large scale in geocomputing, but parallel processing on graphics cards is just at the beginning. Being able to use an simple laptop with a dedicated graphics card for advanced and very fast geocomputation is an advantage that each scientist wants to have. The necessity to have high speed computation in geosciences has increased in the last 10 years, mostly due to the increase in the available datasets. These datasets are becoming more and more detailed and hence they require more space to store and more time to process. Distributed computation on multicore CPU's and GPU's plays an important role by processing one by one small parts from these big datasets. These way of computations allows to speed up the process, because instead of using just one process for each dataset, the user can use all the cores from a CPU or up to hundreds of cores from GPU The framework provide to the end user a standalone tools for morphometry analyses at multiscale level. An important part of the framework is dedicated to uncertainty propagation in geospatial analyses. The uncertainty may come from the data collection or may be induced by the model or may have an infinite sources. These uncertainties plays important roles when a spatial delineation of the phenomena is modelled. Uncertainty propagation is implemented inside the GPU framework using Monte Carlo simulations. The GPU framework with the standalone tools proved to be a reliable tool for modelling complex natural phenomena The framework is based on NVidia Cuda technology and is written in C++ programming language. The code source will be available on github at https://github.com/sandricionut/GeoRsGPU Acknowledgement: GPU framework for geospatial analysis, Young Researchers Grant (ICUB-University of Bucharest) 2016, director Ionut Sandric

Fast 2D flood modelling using GPU technology - recent applications and new developments

NASA Astrophysics Data System (ADS)

Crossley, Amanda; Lamb, Rob; Waller, Simon; Dunning, Paul

2010-05-01

In recent years there has been considerable interest amongst scientists and engineers in exploiting the potential of commodity graphics hardware for desktop parallel computing. The Graphics Processing Units (GPUs) that are used in PC graphics cards have now evolved into powerful parallel co-processors that can be used to accelerate the numerical codes used for floodplain inundation modelling. We report in this paper on experience over the past two years in developing and applying two dimensional (2D) flood inundation models using GPUs to achieve significant practical performance benefits. Starting with a solution scheme for the 2D diffusion wave approximation to the 2D Shallow Water Equations (SWEs), we have demonstrated the capability to reduce model run times in ‘real-world' applications using GPU hardware and programming techniques. We then present results from a GPU-based 2D finite volume SWE solver. A series of numerical test cases demonstrate that the model produces outputs that are accurate and consistent with reference results published elsewhere. In comparisons conducted for a real world test case, the GPU-based SWE model was over 100 times faster than the CPU version. We conclude with some discussion of practical experience in using the GPU technology for flood mapping applications, and for research projects investigating use of Monte Carlo simulation methods for the analysis of uncertainty in 2D flood modelling.

Multicore and GPU algorithms for Nussinov RNA folding

PubMed Central

2014-01-01

Background One segment of a RNA sequence might be paired with another segment of the same RNA sequence due to the force of hydrogen bonds. This two-dimensional structure is called the RNA sequence's secondary structure. Several algorithms have been proposed to predict an RNA sequence's secondary structure. These algorithms are referred to as RNA folding algorithms. Results We develop cache efficient, multicore, and GPU algorithms for RNA folding using Nussinov's algorithm. Conclusions Our cache efficient algorithm provides a speedup between 1.6 and 3.0 relative to a naive straightforward single core code. The multicore version of the cache efficient single core algorithm provides a speedup, relative to the naive single core algorithm, between 7.5 and 14.0 on a 6 core hyperthreaded CPU. Our GPU algorithm for the NVIDIA C2050 is up to 1582 times as fast as the naive single core algorithm and between 5.1 and 11.2 times as fast as the fastest previously known GPU algorithm for Nussinov RNA folding. PMID:25082539

TU-EF-304-07: Monte Carlo-Based Inverse Treatment Plan Optimization for Intensity Modulated Proton Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Y; UT Southwestern Medical Center, Dallas, TX; Tian, Z

2015-06-15

Purpose: Intensity-modulated proton therapy (IMPT) is increasingly used in proton therapy. For IMPT optimization, Monte Carlo (MC) is desired for spots dose calculations because of its high accuracy, especially in cases with a high level of heterogeneity. It is also preferred in biological optimization problems due to the capability of computing quantities related to biological effects. However, MC simulation is typically too slow to be used for this purpose. Although GPU-based MC engines have become available, the achieved efficiency is still not ideal. The purpose of this work is to develop a new optimization scheme to include GPU-based MC intomore » IMPT. Methods: A conventional approach using MC in IMPT simply calls the MC dose engine repeatedly for each spot dose calculations. However, this is not the optimal approach, because of the unnecessary computations on some spots that turned out to have very small weights after solving the optimization problem. GPU-memory writing conflict occurring at a small beam size also reduces computational efficiency. To solve these problems, we developed a new framework that iteratively performs MC dose calculations and plan optimizations. At each dose calculation step, the particles were sampled from different spots altogether with Metropolis algorithm, such that the particle number is proportional to the latest optimized spot intensity. Simultaneously transporting particles from multiple spots also mitigated the memory writing conflict problem. Results: We have validated the proposed MC-based optimization schemes in one prostate case. The total computation time of our method was ∼5–6 min on one NVIDIA GPU card, including both spot dose calculation and plan optimization, whereas a conventional method naively using the same GPU-based MC engine were ∼3 times slower. Conclusion: A fast GPU-based MC dose calculation method along with a novel optimization workflow is developed. The high efficiency makes it attractive for clinical usages.« less

WE-G-18A-03: Cone Artifacts Correction in Iterative Cone Beam CT Reconstruction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yan, H; Folkerts, M; Jiang, S

Purpose: For iterative reconstruction (IR) in cone-beam CT (CBCT) imaging, data truncation along the superior-inferior (SI) direction causes severe cone artifacts in the reconstructed CBCT volume images. Not only does it reduce the effective SI coverage of the reconstructed volume, it also hinders the IR algorithm convergence. This is particular a problem for regularization based IR, where smoothing type regularization operations tend to propagate the artifacts to a large area. It is our purpose to develop a practical cone artifacts correction solution. Methods: We found it is the missing data residing in the truncated cone area that leads to inconsistencymore » between the calculated forward projections and measured projections. We overcome this problem by using FDK type reconstruction to estimate the missing data and design weighting factors to compensate the inconsistency caused by the missing data. We validate the proposed methods in our multi-GPU low-dose CBCT reconstruction system on multiple patients' datasets. Results: Compared to the FDK reconstruction with full datasets, while IR is able to reconstruct CBCT images using a subset of projection data, the severe cone artifacts degrade overall image quality. For head-neck case under a full-fan mode, 13 out of 80 slices are contaminated. It is even more severe in pelvis case under half-fan mode, where 36 out of 80 slices are affected, leading to inferior soft-tissue delineation. By applying the proposed method, the cone artifacts are effectively corrected, with a mean intensity difference decreased from ∼497 HU to ∼39HU for those contaminated slices. Conclusion: A practical and effective solution for cone artifacts correction is proposed and validated in CBCT IR algorithm. This study is supported in part by NIH (1R01CA154747-01)« less

Simulations of Fuel Assembly and Fast-Electron Transport in Integrated Fast-Ignition Experiments on OMEGA

NASA Astrophysics Data System (ADS)

Solodov, A. A.; Theobald, W.; Anderson, K. S.; Shvydky, A.; Epstein, R.; Betti, R.; Myatt, J. F.; Stoeckl, C.; Jarrott, L. C.; McGuffey, C.; Qiao, B.; Beg, F. N.; Wei, M. S.; Stephens, R. B.

2013-10-01

Integrated fast-ignition experiments on OMEGA benefit from improved performance of the OMEGA EP laser, including higher contrast, higher energy, and a smaller focus. Recent 8-keV, Cu-Kα flash radiography of cone-in-shell implosions and cone-tip breakout measurements showed good agreement with the 2-D radiation-hydrodynamic simulations using the code DRACO. DRACO simulations show that the fuel assembly can be further improved by optimizing the compression laser pulse, evacuating air from the shell, and by adjusting the material of the cone tip. This is found to delay the cone-tip breakout by ~220 ps and increase the core areal density from ~80 mg/cm2 in the current experiments to ~500 mg/cm2 at the time of the OMEGA EP beam arrival before the cone-tip breakout. Simulations using the code LSP of fast-electron transport in the recent integrated OMEGA experiments with Cu-doped shells will be presented. Cu-doping is added to probe the transport of fast electrons via their induced Cu K-shell fluorescent emission. This material is based upon work supported by the Department of Energy National Nuclear Security Administration DE-NA0001944 and the Office of Science under DE-FC02-04ER54789.

Efficient Parallel Levenberg-Marquardt Model Fitting towards Real-Time Automated Parametric Imaging Microscopy

PubMed Central

Zhu, Xiang; Zhang, Dianwen

2013-01-01

We present a fast, accurate and robust parallel Levenberg-Marquardt minimization optimizer, GPU-LMFit, which is implemented on graphics processing unit for high performance scalable parallel model fitting processing. GPU-LMFit can provide a dramatic speed-up in massive model fitting analyses to enable real-time automated pixel-wise parametric imaging microscopy. We demonstrate the performance of GPU-LMFit for the applications in superresolution localization microscopy and fluorescence lifetime imaging microscopy. PMID:24130785

CUDA-based acceleration of collateral filtering in brain MR images

NASA Astrophysics Data System (ADS)

Li, Cheng-Yuan; Chang, Herng-Hua

2017-02-01

Image denoising is one of the fundamental and essential tasks within image processing. In medical imaging, finding an effective algorithm that can remove random noise in MR images is important. This paper proposes an effective noise reduction method for brain magnetic resonance (MR) images. Our approach is based on the collateral filter which is a more powerful method than the bilateral filter in many cases. However, the computation of the collateral filter algorithm is quite time-consuming. To solve this problem, we improved the collateral filter algorithm with parallel computing using GPU. We adopted CUDA, an application programming interface for GPU by NVIDIA, to accelerate the computation. Our experimental evaluation on an Intel Xeon CPU E5-2620 v3 2.40GHz with a NVIDIA Tesla K40c GPU indicated that the proposed implementation runs dramatically faster than the traditional collateral filter. We believe that the proposed framework has established a general blueprint for achieving fast and robust filtering in a wide variety of medical image denoising applications.

FastGCN: A GPU Accelerated Tool for Fast Gene Co-Expression Networks

PubMed Central

Liang, Meimei; Zhang, Futao; Jin, Gulei; Zhu, Jun

2015-01-01

Gene co-expression networks comprise one type of valuable biological networks. Many methods and tools have been published to construct gene co-expression networks; however, most of these tools and methods are inconvenient and time consuming for large datasets. We have developed a user-friendly, accelerated and optimized tool for constructing gene co-expression networks that can fully harness the parallel nature of GPU (Graphic Processing Unit) architectures. Genetic entropies were exploited to filter out genes with no or small expression changes in the raw data preprocessing step. Pearson correlation coefficients were then calculated. After that, we normalized these coefficients and employed the False Discovery Rate to control the multiple tests. At last, modules identification was conducted to construct the co-expression networks. All of these calculations were implemented on a GPU. We also compressed the coefficient matrix to save space. We compared the performance of the GPU implementation with those of multi-core CPU implementations with 16 CPU threads, single-thread C/C++ implementation and single-thread R implementation. Our results show that GPU implementation largely outperforms single-thread C/C++ implementation and single-thread R implementation, and GPU implementation outperforms multi-core CPU implementation when the number of genes increases. With the test dataset containing 16,000 genes and 590 individuals, we can achieve greater than 63 times the speed using a GPU implementation compared with a single-thread R implementation when 50 percent of genes were filtered out and about 80 times the speed when no genes were filtered out. PMID:25602758

FastGCN: a GPU accelerated tool for fast gene co-expression networks.

PubMed

Liang, Meimei; Zhang, Futao; Jin, Gulei; Zhu, Jun

2015-01-01

Gene co-expression networks comprise one type of valuable biological networks. Many methods and tools have been published to construct gene co-expression networks; however, most of these tools and methods are inconvenient and time consuming for large datasets. We have developed a user-friendly, accelerated and optimized tool for constructing gene co-expression networks that can fully harness the parallel nature of GPU (Graphic Processing Unit) architectures. Genetic entropies were exploited to filter out genes with no or small expression changes in the raw data preprocessing step. Pearson correlation coefficients were then calculated. After that, we normalized these coefficients and employed the False Discovery Rate to control the multiple tests. At last, modules identification was conducted to construct the co-expression networks. All of these calculations were implemented on a GPU. We also compressed the coefficient matrix to save space. We compared the performance of the GPU implementation with those of multi-core CPU implementations with 16 CPU threads, single-thread C/C++ implementation and single-thread R implementation. Our results show that GPU implementation largely outperforms single-thread C/C++ implementation and single-thread R implementation, and GPU implementation outperforms multi-core CPU implementation when the number of genes increases. With the test dataset containing 16,000 genes and 590 individuals, we can achieve greater than 63 times the speed using a GPU implementation compared with a single-thread R implementation when 50 percent of genes were filtered out and about 80 times the speed when no genes were filtered out.

High-performance GPU-based rendering for real-time, rigid 2D/3D-image registration and motion prediction in radiation oncology.

PubMed

Spoerk, Jakob; Gendrin, Christelle; Weber, Christoph; Figl, Michael; Pawiro, Supriyanto Ardjo; Furtado, Hugo; Fabri, Daniella; Bloch, Christoph; Bergmann, Helmar; Gröller, Eduard; Birkfellner, Wolfgang

2012-02-01

A common problem in image-guided radiation therapy (IGRT) of lung cancer as well as other malignant diseases is the compensation of periodic and aperiodic motion during dose delivery. Modern systems for image-guided radiation oncology allow for the acquisition of cone-beam computed tomography data in the treatment room as well as the acquisition of planar radiographs during the treatment. A mid-term research goal is the compensation of tumor target volume motion by 2D/3D Registration. In 2D/3D registration, spatial information on organ location is derived by an iterative comparison of perspective volume renderings, so-called digitally rendered radiographs (DRR) from computed tomography volume data, and planar reference x-rays. Currently, this rendering process is very time consuming, and real-time registration, which should at least provide data on organ position in less than a second, has not come into existence. We present two GPU-based rendering algorithms which generate a DRR of 512×512 pixels size from a CT dataset of 53 MB size at a pace of almost 100 Hz. This rendering rate is feasible by applying a number of algorithmic simplifications which range from alternative volume-driven rendering approaches - namely so-called wobbled splatting - to sub-sampling of the DRR-image by means of specialized raycasting techniques. Furthermore, general purpose graphics processing unit (GPGPU) programming paradigms were consequently utilized. Rendering quality and performance as well as the influence on the quality and performance of the overall registration process were measured and analyzed in detail. The results show that both methods are competitive and pave the way for fast motion compensation by rigid and possibly even non-rigid 2D/3D registration and, beyond that, adaptive filtering of motion models in IGRT. Copyright © 2011. Published by Elsevier GmbH.

High-performance GPU-based rendering for real-time, rigid 2D/3D-image registration and motion prediction in radiation oncology

PubMed Central

Spoerk, Jakob; Gendrin, Christelle; Weber, Christoph; Figl, Michael; Pawiro, Supriyanto Ardjo; Furtado, Hugo; Fabri, Daniella; Bloch, Christoph; Bergmann, Helmar; Gröller, Eduard; Birkfellner, Wolfgang

2012-01-01

A common problem in image-guided radiation therapy (IGRT) of lung cancer as well as other malignant diseases is the compensation of periodic and aperiodic motion during dose delivery. Modern systems for image-guided radiation oncology allow for the acquisition of cone-beam computed tomography data in the treatment room as well as the acquisition of planar radiographs during the treatment. A mid-term research goal is the compensation of tumor target volume motion by 2D/3D registration. In 2D/3D registration, spatial information on organ location is derived by an iterative comparison of perspective volume renderings, so-called digitally rendered radiographs (DRR) from computed tomography volume data, and planar reference x-rays. Currently, this rendering process is very time consuming, and real-time registration, which should at least provide data on organ position in less than a second, has not come into existence. We present two GPU-based rendering algorithms which generate a DRR of 512 × 512 pixels size from a CT dataset of 53 MB size at a pace of almost 100 Hz. This rendering rate is feasible by applying a number of algorithmic simplifications which range from alternative volume-driven rendering approaches – namely so-called wobbled splatting – to sub-sampling of the DRR-image by means of specialized raycasting techniques. Furthermore, general purpose graphics processing unit (GPGPU) programming paradigms were consequently utilized. Rendering quality and performance as well as the influence on the quality and performance of the overall registration process were measured and analyzed in detail. The results show that both methods are competitive and pave the way for fast motion compensation by rigid and possibly even non-rigid 2D/3D registration and, beyond that, adaptive filtering of motion models in IGRT. PMID:21782399

A GPU OpenCL based cross-platform Monte Carlo dose calculation engine (goMC)

NASA Astrophysics Data System (ADS)

Tian, Zhen; Shi, Feng; Folkerts, Michael; Qin, Nan; Jiang, Steve B.; Jia, Xun

2015-09-01

Monte Carlo (MC) simulation has been recognized as the most accurate dose calculation method for radiotherapy. However, the extremely long computation time impedes its clinical application. Recently, a lot of effort has been made to realize fast MC dose calculation on graphic processing units (GPUs). However, most of the GPU-based MC dose engines have been developed under NVidia’s CUDA environment. This limits the code portability to other platforms, hindering the introduction of GPU-based MC simulations to clinical practice. The objective of this paper is to develop a GPU OpenCL based cross-platform MC dose engine named goMC with coupled photon-electron simulation for external photon and electron radiotherapy in the MeV energy range. Compared to our previously developed GPU-based MC code named gDPM (Jia et al 2012 Phys. Med. Biol. 57 7783-97), goMC has two major differences. First, it was developed under the OpenCL environment for high code portability and hence could be run not only on different GPU cards but also on CPU platforms. Second, we adopted the electron transport model used in EGSnrc MC package and PENELOPE’s random hinge method in our new dose engine, instead of the dose planning method employed in gDPM. Dose distributions were calculated for a 15 MeV electron beam and a 6 MV photon beam in a homogenous water phantom, a water-bone-lung-water slab phantom and a half-slab phantom. Satisfactory agreement between the two MC dose engines goMC and gDPM was observed in all cases. The average dose differences in the regions that received a dose higher than 10% of the maximum dose were 0.48-0.53% for the electron beam cases and 0.15-0.17% for the photon beam cases. In terms of efficiency, goMC was ~4-16% slower than gDPM when running on the same NVidia TITAN card for all the cases we tested, due to both the different electron transport models and the different development environments. The code portability of our new dose engine goMC was validated by successfully running it on a variety of different computing devices including an NVidia GPU card, two AMD GPU cards and an Intel CPU processor. Computational efficiency among these platforms was compared.

A GPU OpenCL based cross-platform Monte Carlo dose calculation engine (goMC).

PubMed

Tian, Zhen; Shi, Feng; Folkerts, Michael; Qin, Nan; Jiang, Steve B; Jia, Xun

2015-10-07

Monte Carlo (MC) simulation has been recognized as the most accurate dose calculation method for radiotherapy. However, the extremely long computation time impedes its clinical application. Recently, a lot of effort has been made to realize fast MC dose calculation on graphic processing units (GPUs). However, most of the GPU-based MC dose engines have been developed under NVidia's CUDA environment. This limits the code portability to other platforms, hindering the introduction of GPU-based MC simulations to clinical practice. The objective of this paper is to develop a GPU OpenCL based cross-platform MC dose engine named goMC with coupled photon-electron simulation for external photon and electron radiotherapy in the MeV energy range. Compared to our previously developed GPU-based MC code named gDPM (Jia et al 2012 Phys. Med. Biol. 57 7783-97), goMC has two major differences. First, it was developed under the OpenCL environment for high code portability and hence could be run not only on different GPU cards but also on CPU platforms. Second, we adopted the electron transport model used in EGSnrc MC package and PENELOPE's random hinge method in our new dose engine, instead of the dose planning method employed in gDPM. Dose distributions were calculated for a 15 MeV electron beam and a 6 MV photon beam in a homogenous water phantom, a water-bone-lung-water slab phantom and a half-slab phantom. Satisfactory agreement between the two MC dose engines goMC and gDPM was observed in all cases. The average dose differences in the regions that received a dose higher than 10% of the maximum dose were 0.48-0.53% for the electron beam cases and 0.15-0.17% for the photon beam cases. In terms of efficiency, goMC was ~4-16% slower than gDPM when running on the same NVidia TITAN card for all the cases we tested, due to both the different electron transport models and the different development environments. The code portability of our new dose engine goMC was validated by successfully running it on a variety of different computing devices including an NVidia GPU card, two AMD GPU cards and an Intel CPU processor. Computational efficiency among these platforms was compared.

GPU-accelerated low-latency real-time searches for gravitational waves from compact binary coalescence

NASA Astrophysics Data System (ADS)

Liu, Yuan; Du, Zhihui; Chung, Shin Kee; Hooper, Shaun; Blair, David; Wen, Linqing

2012-12-01

We present a graphics processing unit (GPU)-accelerated time-domain low-latency algorithm to search for gravitational waves (GWs) from coalescing binaries of compact objects based on the summed parallel infinite impulse response (SPIIR) filtering technique. The aim is to facilitate fast detection of GWs with a minimum delay to allow prompt electromagnetic follow-up observations. To maximize the GPU acceleration, we apply an efficient batched parallel computing model that significantly reduces the number of synchronizations in SPIIR and optimizes the usage of the memory and hardware resource. Our code is tested on the CUDA ‘Fermi’ architecture in a GTX 480 graphics card and its performance is compared with a single core of Intel Core i7 920 (2.67 GHz). A 58-fold speedup is achieved while giving results in close agreement with the CPU implementation. Our result indicates that it is possible to conduct a full search for GWs from compact binary coalescence in real time with only one desktop computer equipped with a Fermi GPU card for the initial LIGO detectors which in the past required more than 100 CPUs.

GPU-based Green's function simulations of shear waves generated by an applied acoustic radiation force in elastic and viscoelastic models.

PubMed

Yang, Yiqun; Urban, Matthew W; McGough, Robert J

2018-05-15

Shear wave calculations induced by an acoustic radiation force are very time-consuming on desktop computers, and high-performance graphics processing units (GPUs) achieve dramatic reductions in the computation time for these simulations. The acoustic radiation force is calculated using the fast near field method and the angular spectrum approach, and then the shear waves are calculated in parallel with Green's functions on a GPU. This combination enables rapid evaluation of shear waves for push beams with different spatial samplings and for apertures with different f/#. Relative to shear wave simulations that evaluate the same algorithm on an Intel i7 desktop computer, a high performance nVidia GPU reduces the time required for these calculations by a factor of 45 and 700 when applied to elastic and viscoelastic shear wave simulation models, respectively. These GPU-accelerated simulations also compared to measurements in different viscoelastic phantoms, and the results are similar. For parametric evaluations and for comparisons with measured shear wave data, shear wave simulations with the Green's function approach are ideally suited for high-performance GPUs.

A GPU-based mipmapping method for water surface visualization

NASA Astrophysics Data System (ADS)

Li, Hua; Quan, Wei; Xu, Chao; Wu, Yan

2018-03-01

Visualization of water surface is a hot topic in computer graphics. In this paper, we presented a fast method to generate wide range of water surface with good image quality both near and far from the viewpoint. This method utilized uniform mesh and Fractal Perlin noise to model water surface. Mipmapping technology was enforced to the surface textures, which adjust the resolution with respect to the distance from the viewpoint and reduce the computing cost. Lighting effect was computed based on shadow mapping technology, Snell's law and Fresnel term. The render pipeline utilizes a CPU-GPU shared memory structure, which improves the rendering efficiency. Experiment results show that our approach visualizes water surface with good image quality at real-time frame rates performance.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shiraga, H.; Nagatomo, H.; Theobald, W.

Here, integrated fast ignition experiments were performed at ILE, Osaka, and LLE, Rochester, in which a nanosecond driver laser implodes a deuterated plastic shell in front of the tip of a hollow metal cone and an intense ultrashort-pulse laser is injected through the cone to heat the compressed plasma. Based on the initial successful results of fast electron heating of cone-in-shell targets, large-energy short-pulse laser beam lines were constructed and became operational: OMEGA-EP at Rochester and LFEX at Osaka. Neutron enhancement due to heating with a ~kJ short-pulse laser has been demonstrated in the integrated experiments at Osaka and Rochester.more » The neutron yields are being analyzed by comparing the experimental results with simulations. Details of the fast electron beam transport and the electron energy deposition in the imploded fuel plasma are complicated and further studies are imperative. The hydrodynamics of the implosion was studied including the interaction of the imploded core plasma with the cone tip. Theory and simulation studies are presented on the hydrodynamics of a high-gain target for a fast ignition point design.« less

NiftySim: A GPU-based nonlinear finite element package for simulation of soft tissue biomechanics.

PubMed

Johnsen, Stian F; Taylor, Zeike A; Clarkson, Matthew J; Hipwell, John; Modat, Marc; Eiben, Bjoern; Han, Lianghao; Hu, Yipeng; Mertzanidou, Thomy; Hawkes, David J; Ourselin, Sebastien

2015-07-01

NiftySim, an open-source finite element toolkit, has been designed to allow incorporation of high-performance soft tissue simulation capabilities into biomedical applications. The toolkit provides the option of execution on fast graphics processing unit (GPU) hardware, numerous constitutive models and solid-element options, membrane and shell elements, and contact modelling facilities, in a simple to use library. The toolkit is founded on the total Lagrangian explicit dynamics (TLEDs) algorithm, which has been shown to be efficient and accurate for simulation of soft tissues. The base code is written in C[Formula: see text], and GPU execution is achieved using the nVidia CUDA framework. In most cases, interaction with the underlying solvers can be achieved through a single Simulator class, which may be embedded directly in third-party applications such as, surgical guidance systems. Advanced capabilities such as contact modelling and nonlinear constitutive models are also provided, as are more experimental technologies like reduced order modelling. A consistent description of the underlying solution algorithm, its implementation with a focus on GPU execution, and examples of the toolkit's usage in biomedical applications are provided. Efficient mapping of the TLED algorithm to parallel hardware results in very high computational performance, far exceeding that available in commercial packages. The NiftySim toolkit provides high-performance soft tissue simulation capabilities using GPU technology for biomechanical simulation research applications in medical image computing, surgical simulation, and surgical guidance applications.

ARCHERRT – A GPU-based and photon-electron coupled Monte Carlo dose computing engine for radiation therapy: Software development and application to helical tomotherapy

PubMed Central

Su, Lin; Yang, Youming; Bednarz, Bryan; Sterpin, Edmond; Du, Xining; Liu, Tianyu; Ji, Wei; Xu, X. George

2014-01-01

Purpose: Using the graphical processing units (GPU) hardware technology, an extremely fast Monte Carlo (MC) code ARCHERRT is developed for radiation dose calculations in radiation therapy. This paper describes the detailed software development and testing for three clinical TomoTherapy® cases: the prostate, lung, and head & neck. Methods: To obtain clinically relevant dose distributions, phase space files (PSFs) created from optimized radiation therapy treatment plan fluence maps were used as the input to ARCHERRT. Patient-specific phantoms were constructed from patient CT images. Batch simulations were employed to facilitate the time-consuming task of loading large PSFs, and to improve the estimation of statistical uncertainty. Furthermore, two different Woodcock tracking algorithms were implemented and their relative performance was compared. The dose curves of an Elekta accelerator PSF incident on a homogeneous water phantom were benchmarked against DOSXYZnrc. For each of the treatment cases, dose volume histograms and isodose maps were produced from ARCHERRT and the general-purpose code, GEANT4. The gamma index analysis was performed to evaluate the similarity of voxel doses obtained from these two codes. The hardware accelerators used in this study are one NVIDIA K20 GPU, one NVIDIA K40 GPU, and six NVIDIA M2090 GPUs. In addition, to make a fairer comparison of the CPU and GPU performance, a multithreaded CPU code was developed using OpenMP and tested on an Intel E5-2620 CPU. Results: For the water phantom, the depth dose curve and dose profiles from ARCHERRT agree well with DOSXYZnrc. For clinical cases, results from ARCHERRT are compared with those from GEANT4 and good agreement is observed. Gamma index test is performed for voxels whose dose is greater than 10% of maximum dose. For 2%/2mm criteria, the passing rates for the prostate, lung case, and head & neck cases are 99.7%, 98.5%, and 97.2%, respectively. Due to specific architecture of GPU, modified Woodcock tracking algorithm performed inferior to the original one. ARCHERRT achieves a fast speed for PSF-based dose calculations. With a single M2090 card, the simulations cost about 60, 50, 80 s for three cases, respectively, with the 1% statistical error in the PTV. Using the latest K40 card, the simulations are 1.7–1.8 times faster. More impressively, six M2090 cards could finish the simulations in 8.9–13.4 s. For comparison, the same simulations on Intel E5-2620 (12 hyperthreading) cost about 500–800 s. Conclusions: ARCHERRT was developed successfully to perform fast and accurate MC dose calculation for radiotherapy using PSFs and patient CT phantoms. PMID:24989378

ARCHERRT - a GPU-based and photon-electron coupled Monte Carlo dose computing engine for radiation therapy: software development and application to helical tomotherapy.

PubMed

Su, Lin; Yang, Youming; Bednarz, Bryan; Sterpin, Edmond; Du, Xining; Liu, Tianyu; Ji, Wei; Xu, X George

2014-07-01

Using the graphical processing units (GPU) hardware technology, an extremely fast Monte Carlo (MC) code ARCHERRT is developed for radiation dose calculations in radiation therapy. This paper describes the detailed software development and testing for three clinical TomoTherapy® cases: the prostate, lung, and head & neck. To obtain clinically relevant dose distributions, phase space files (PSFs) created from optimized radiation therapy treatment plan fluence maps were used as the input to ARCHERRT. Patient-specific phantoms were constructed from patient CT images. Batch simulations were employed to facilitate the time-consuming task of loading large PSFs, and to improve the estimation of statistical uncertainty. Furthermore, two different Woodcock tracking algorithms were implemented and their relative performance was compared. The dose curves of an Elekta accelerator PSF incident on a homogeneous water phantom were benchmarked against DOSXYZnrc. For each of the treatment cases, dose volume histograms and isodose maps were produced from ARCHERRT and the general-purpose code, GEANT4. The gamma index analysis was performed to evaluate the similarity of voxel doses obtained from these two codes. The hardware accelerators used in this study are one NVIDIA K20 GPU, one NVIDIA K40 GPU, and six NVIDIA M2090 GPUs. In addition, to make a fairer comparison of the CPU and GPU performance, a multithreaded CPU code was developed using OpenMP and tested on an Intel E5-2620 CPU. For the water phantom, the depth dose curve and dose profiles from ARCHERRT agree well with DOSXYZnrc. For clinical cases, results from ARCHERRT are compared with those from GEANT4 and good agreement is observed. Gamma index test is performed for voxels whose dose is greater than 10% of maximum dose. For 2%/2mm criteria, the passing rates for the prostate, lung case, and head & neck cases are 99.7%, 98.5%, and 97.2%, respectively. Due to specific architecture of GPU, modified Woodcock tracking algorithm performed inferior to the original one. ARCHERRT achieves a fast speed for PSF-based dose calculations. With a single M2090 card, the simulations cost about 60, 50, 80 s for three cases, respectively, with the 1% statistical error in the PTV. Using the latest K40 card, the simulations are 1.7-1.8 times faster. More impressively, six M2090 cards could finish the simulations in 8.9-13.4 s. For comparison, the same simulations on Intel E5-2620 (12 hyperthreading) cost about 500-800 s. ARCHERRT was developed successfully to perform fast and accurate MC dose calculation for radiotherapy using PSFs and patient CT phantoms.

GPU-based simulation of optical propagation through turbulence for active and passive imaging

NASA Astrophysics Data System (ADS)

Monnier, Goulven; Duval, François-Régis; Amram, Solène

2014-10-01

IMOTEP is a GPU-based (Graphical Processing Units) software relying on a fast parallel implementation of Fresnel diffraction through successive phase screens. Its applications include active imaging, laser telemetry and passive imaging through turbulence with anisoplanatic spatial and temporal fluctuations. Thanks to parallel implementation on GPU, speedups ranging from 40X to 70X are achieved. The present paper gives a brief overview of IMOTEP models, algorithms, implementation and user interface. It then focuses on major improvements recently brought to the anisoplanatic imaging simulation method. Previously, we took advantage of the computational power offered by the GPU to develop a simulation method based on large series of deterministic realisations of the PSF distorted by turbulence. The phase screen propagation algorithm, by reproducing higher moments of the incident wavefront distortion, provides realistic PSFs. However, we first used a coarse gaussian model to fit the numerical PSFs and characterise there spatial statistics through only 3 parameters (two-dimensional displacements of centroid and width). Meanwhile, this approach was unable to reproduce the effects related to the details of the PSF structure, especially the "speckles" leading to prominent high-frequency content in short-exposure images. To overcome this limitation, we recently implemented a new empirical model of the PSF, based on Principal Components Analysis (PCA), ought to catch most of the PSF complexity. The GPU implementation allows estimating and handling efficiently the numerous (up to several hundreds) principal components typically required under the strong turbulence regime. A first demanding computational step involves PCA, phase screen propagation and covariance estimates. In a second step, realistic instantaneous images, fully accounting for anisoplanatic effects, are quickly generated. Preliminary results are presented.

Development of a GPU Compatible Version of the Fast Radiation Code RRTMG

NASA Astrophysics Data System (ADS)

Iacono, M. J.; Mlawer, E. J.; Berthiaume, D.; Cady-Pereira, K. E.; Suarez, M.; Oreopoulos, L.; Lee, D.

2012-12-01

The absorption of solar radiation and emission/absorption of thermal radiation are crucial components of the physics that drive Earth's climate and weather. Therefore, accurate radiative transfer calculations are necessary for realistic climate and weather simulations. Efficient radiation codes have been developed for this purpose, but their accuracy requirements still necessitate that as much as 30% of the computational time of a GCM is spent computing radiative fluxes and heating rates. The overall computational expense constitutes a limitation on a GCM's predictive ability if it becomes an impediment to adding new physics to or increasing the spatial and/or vertical resolution of the model. The emergence of Graphics Processing Unit (GPU) technology, which will allow the parallel computation of multiple independent radiative calculations in a GCM, will lead to a fundamental change in the competition between accuracy and speed. Processing time previously consumed by radiative transfer will now be available for the modeling of other processes, such as physics parameterizations, without any sacrifice in the accuracy of the radiative transfer. Furthermore, fast radiation calculations can be performed much more frequently and will allow the modeling of radiative effects of rapid changes in the atmosphere. The fast radiation code RRTMG, developed at Atmospheric and Environmental Research (AER), is utilized operationally in many dynamical models throughout the world. We will present the results from the first stage of an effort to create a version of the RRTMG radiation code designed to run efficiently in a GPU environment. This effort will focus on the RRTMG implementation in GEOS-5. RRTMG has an internal pseudo-spectral vector of length of order 100 that, when combined with the much greater length of the global horizontal grid vector from which the radiation code is called in GEOS-5, makes RRTMG/GEOS-5 particularly suited to achieving a significant speed improvement through GPU technology. This large number of independent cases will allow us to take full advantage of the computational power of the latest GPUs, ensuring that all thread cores in the GPU remain active, a key criterion for obtaining significant speedup. The CUDA (Compute Unified Device Architecture) Fortran compiler developed by PGI and Nvidia will allow us to construct this parallel implementation on the GPU while remaining in the Fortran language. This implementation will scale very well across various CUDA-supported GPUs such as the recently released Fermi Nvidia cards. We will present the computational speed improvements of the GPU-compatible code relative to the standard CPU-based RRTMG with respect to a very large and diverse suite of atmospheric profiles. This suite will also be utilized to demonstrate the minimal impact of the code restructuring on the accuracy of radiation calculations. The GPU-compatible version of RRTMG will be directly applicable to future versions of GEOS-5, but it is also likely to provide significant associated benefits for other GCMs that employ RRTMG.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wan Chan Tseung, Hok Seum, E-mail: wanchantseung.hok@mayo.edu; Ma, Jiasen; Kreofsky, Cole R.

Purpose: Our aim is to demonstrate the feasibility of fast Monte Carlo (MC)–based inverse biological planning for the treatment of head and neck tumors in spot-scanning proton therapy. Methods and Materials: Recently, a fast and accurate graphics processor unit (GPU)–based MC simulation of proton transport was developed and used as the dose-calculation engine in a GPU-accelerated intensity modulated proton therapy (IMPT) optimizer. Besides dose, the MC can simultaneously score the dose-averaged linear energy transfer (LET{sub d}), which makes biological dose (BD) optimization possible. To convert from LET{sub d} to BD, a simple linear relation was assumed. By use of thismore » novel optimizer, inverse biological planning was applied to 4 patients, including 2 small and 1 large thyroid tumor targets, as well as 1 glioma case. To create these plans, constraints were placed to maintain the physical dose (PD) within 1.25 times the prescription while maximizing target BD. For comparison, conventional intensity modulated radiation therapy (IMRT) and IMPT plans were also created using Eclipse (Varian Medical Systems) in each case. The same critical-structure PD constraints were used for the IMRT, IMPT, and biologically optimized plans. The BD distributions for the IMPT plans were obtained through MC recalculations. Results: Compared with standard IMPT, the biologically optimal plans for patients with small tumor targets displayed a BD escalation that was around twice the PD increase. Dose sparing to critical structures was improved compared with both IMRT and IMPT. No significant BD increase could be achieved for the large thyroid tumor case and when the presence of critical structures mitigated the contribution of additional fields. The calculation of the biologically optimized plans can be completed in a clinically viable time (<30 minutes) on a small 24-GPU system. Conclusions: By exploiting GPU acceleration, MC-based, biologically optimized plans were created for small–tumor target patients. This optimizer will be used in an upcoming feasibility trial on LET{sub d} painting for radioresistant tumors.« less

Multi-GPU implementation of a VMAT treatment plan optimization algorithm.

PubMed

Tian, Zhen; Peng, Fei; Folkerts, Michael; Tan, Jun; Jia, Xun; Jiang, Steve B

2015-06-01

Volumetric modulated arc therapy (VMAT) optimization is a computationally challenging problem due to its large data size, high degrees of freedom, and many hardware constraints. High-performance graphics processing units (GPUs) have been used to speed up the computations. However, GPU's relatively small memory size cannot handle cases with a large dose-deposition coefficient (DDC) matrix in cases of, e.g., those with a large target size, multiple targets, multiple arcs, and/or small beamlet size. The main purpose of this paper is to report an implementation of a column-generation-based VMAT algorithm, previously developed in the authors' group, on a multi-GPU platform to solve the memory limitation problem. While the column-generation-based VMAT algorithm has been previously developed, the GPU implementation details have not been reported. Hence, another purpose is to present detailed techniques employed for GPU implementation. The authors also would like to utilize this particular problem as an example problem to study the feasibility of using a multi-GPU platform to solve large-scale problems in medical physics. The column-generation approach generates VMAT apertures sequentially by solving a pricing problem (PP) and a master problem (MP) iteratively. In the authors' method, the sparse DDC matrix is first stored on a CPU in coordinate list format (COO). On the GPU side, this matrix is split into four submatrices according to beam angles, which are stored on four GPUs in compressed sparse row format. Computation of beamlet price, the first step in PP, is accomplished using multi-GPUs. A fast inter-GPU data transfer scheme is accomplished using peer-to-peer access. The remaining steps of PP and MP problems are implemented on CPU or a single GPU due to their modest problem scale and computational loads. Barzilai and Borwein algorithm with a subspace step scheme is adopted here to solve the MP problem. A head and neck (H&N) cancer case is then used to validate the authors' method. The authors also compare their multi-GPU implementation with three different single GPU implementation strategies, i.e., truncating DDC matrix (S1), repeatedly transferring DDC matrix between CPU and GPU (S2), and porting computations involving DDC matrix to CPU (S3), in terms of both plan quality and computational efficiency. Two more H&N patient cases and three prostate cases are used to demonstrate the advantages of the authors' method. The authors' multi-GPU implementation can finish the optimization process within ∼ 1 min for the H&N patient case. S1 leads to an inferior plan quality although its total time was 10 s shorter than the multi-GPU implementation due to the reduced matrix size. S2 and S3 yield the same plan quality as the multi-GPU implementation but take ∼4 and ∼6 min, respectively. High computational efficiency was consistently achieved for the other five patient cases tested, with VMAT plans of clinically acceptable quality obtained within 23-46 s. Conversely, to obtain clinically comparable or acceptable plans for all six of these VMAT cases that the authors have tested in this paper, the optimization time needed in a commercial TPS system on CPU was found to be in an order of several minutes. The results demonstrate that the multi-GPU implementation of the authors' column-generation-based VMAT optimization can handle the large-scale VMAT optimization problem efficiently without sacrificing plan quality. The authors' study may serve as an example to shed some light on other large-scale medical physics problems that require multi-GPU techniques.

An efficient spectral crystal plasticity solver for GPU architectures

NASA Astrophysics Data System (ADS)

Malahe, Michael

2018-03-01

We present a spectral crystal plasticity (CP) solver for graphics processing unit (GPU) architectures that achieves a tenfold increase in efficiency over prior GPU solvers. The approach makes use of a database containing a spectral decomposition of CP simulations performed using a conventional iterative solver over a parameter space of crystal orientations and applied velocity gradients. The key improvements in efficiency come from reducing global memory transactions, exposing more instruction-level parallelism, reducing integer instructions and performing fast range reductions on trigonometric arguments. The scheme also makes more efficient use of memory than prior work, allowing for larger problems to be solved on a single GPU. We illustrate these improvements with a simulation of 390 million crystal grains on a consumer-grade GPU, which executes at a rate of 2.72 s per strain step.

Shape measurement and vibration analysis of moving speaker cone

NASA Astrophysics Data System (ADS)

Zhang, Qican; Liu, Yuankun; Lehtonen, Petri

2014-06-01

Surface three-dimensional (3-D) shape information is needed for many fast processes such as structural testing of material, standing waves on loudspeaker cone, etc. Usually measurement is done from limited number of points using electrical sensors or laser distance meters. Fourier Transform Profilometry (FTP) enables fast shape measurement of the whole surface. Method is based on angled sinusoidal fringe pattern projection and image capturing. FTP requires only one image of the deformed fringe pattern to restore the 3-D shape of the measured object, which makes real-time or dynamic data processing possible. In our experiment the method was used for loudspeaker cone distortion measurement in dynamic conditions. For sound quality issues it is important that the whole cone moves in same phase and there are no partial waves. Our imaging resolution was 1280x1024 pixels and frame rate was 200 fps. Using our setup we found unwanted spatial waves in our sample cone.

Comprehensive evaluations of cone-beam CT dose in image-guided radiation therapy via GPU-based Monte Carlo simulations

NASA Astrophysics Data System (ADS)

Montanari, Davide; Scolari, Enrica; Silvestri, Chiara; Jiang Graves, Yan; Yan, Hao; Cervino, Laura; Rice, Roger; Jiang, Steve B.; Jia, Xun

2014-03-01

Cone beam CT (CBCT) has been widely used for patient setup in image-guided radiation therapy (IGRT). Radiation dose from CBCT scans has become a clinical concern. The purposes of this study are (1) to commission a graphics processing unit (GPU)-based Monte Carlo (MC) dose calculation package gCTD for Varian On-Board Imaging (OBI) system and test the calculation accuracy, and (2) to quantitatively evaluate CBCT dose from the OBI system in typical IGRT scan protocols. We first conducted dose measurements in a water phantom. X-ray source model parameters used in gCTD are obtained through a commissioning process. gCTD accuracy is demonstrated by comparing calculations with measurements in water and in CTDI phantoms. Twenty-five brain cancer patients are used to study dose in a standard-dose head protocol, and 25 prostate cancer patients are used to study dose in pelvis protocol and pelvis spotlight protocol. Mean dose to each organ is calculated. Mean dose to 2% voxels that have the highest dose is also computed to quantify the maximum dose. It is found that the mean dose value to an organ varies largely among patients. Moreover, dose distribution is highly non-homogeneous inside an organ. The maximum dose is found to be 1-3 times higher than the mean dose depending on the organ, and is up to eight times higher for the entire body due to the very high dose region in bony structures. High computational efficiency has also been observed in our studies, such that MC dose calculation time is less than 5 min for a typical case.

Heterogeneous CPU-GPU moving targets detection for UAV video

NASA Astrophysics Data System (ADS)

Li, Maowen; Tang, Linbo; Han, Yuqi; Yu, Chunlei; Zhang, Chao; Fu, Huiquan

2017-07-01

Moving targets detection is gaining popularity in civilian and military applications. On some monitoring platform of motion detection, some low-resolution stationary cameras are replaced by moving HD camera based on UAVs. The pixels of moving targets in the HD Video taken by UAV are always in a minority, and the background of the frame is usually moving because of the motion of UAVs. The high computational cost of the algorithm prevents running it at higher resolutions the pixels of frame. Hence, to solve the problem of moving targets detection based UAVs video, we propose a heterogeneous CPU-GPU moving target detection algorithm for UAV video. More specifically, we use background registration to eliminate the impact of the moving background and frame difference to detect small moving targets. In order to achieve the effect of real-time processing, we design the solution of heterogeneous CPU-GPU framework for our method. The experimental results show that our method can detect the main moving targets from the HD video taken by UAV, and the average process time is 52.16ms per frame which is fast enough to solve the problem.

Technical note: RabbitCT--an open platform for benchmarking 3D cone-beam reconstruction algorithms.

PubMed

Rohkohl, C; Keck, B; Hofmann, H G; Hornegger, J

2009-09-01

Fast 3D cone beam reconstruction is mandatory for many clinical workflows. For that reason, researchers and industry work hard on hardware-optimized 3D reconstruction. Backprojection is a major component of many reconstruction algorithms that require a projection of each voxel onto the projection data, including data interpolation, before updating the voxel value. This step is the bottleneck of most reconstruction algorithms and the focus of optimization in recent publications. A crucial limitation, however, of these publications is that the presented results are not comparable to each other. This is mainly due to variations in data acquisitions, preprocessing, and chosen geometries and the lack of a common publicly available test dataset. The authors provide such a standardized dataset that allows for substantial comparison of hardware accelerated backprojection methods. They developed an open platform RabbitCT (www.rabbitCT.com) for worldwide comparison in backprojection performance and ranking on different architectures using a specific high resolution C-arm CT dataset of a rabbit. This includes a sophisticated benchmark interface, a prototype implementation in C++, and image quality measures. At the time of writing, six backprojection implementations are already listed on the website. Optimizations include multithreading using Intel threading building blocks and OpenMP, vectorization using SSE, and computation on the GPU using CUDA 2.0. There is a need for objectively comparing backprojection implementations for reconstruction algorithms. RabbitCT aims to provide a solution to this problem by offering an open platform with fair chances for all participants. The authors are looking forward to a growing community and await feedback regarding future evaluations of novel software- and hardware-based acceleration schemes.

High-fidelity artifact correction for cone-beam CT imaging of the brain

NASA Astrophysics Data System (ADS)

Sisniega, A.; Zbijewski, W.; Xu, J.; Dang, H.; Stayman, J. W.; Yorkston, J.; Aygun, N.; Koliatsos, V.; Siewerdsen, J. H.

2015-02-01

CT is the frontline imaging modality for diagnosis of acute traumatic brain injury (TBI), involving the detection of fresh blood in the brain (contrast of 30-50 HU, detail size down to 1 mm) in a non-contrast-enhanced exam. A dedicated point-of-care imaging system based on cone-beam CT (CBCT) could benefit early detection of TBI and improve direction to appropriate therapy. However, flat-panel detector (FPD) CBCT is challenged by artifacts that degrade contrast resolution and limit application in soft-tissue imaging. We present and evaluate a fairly comprehensive framework for artifact correction to enable soft-tissue brain imaging with FPD CBCT. The framework includes a fast Monte Carlo (MC)-based scatter estimation method complemented by corrections for detector lag, veiling glare, and beam hardening. The fast MC scatter estimation combines GPU acceleration, variance reduction, and simulation with a low number of photon histories and reduced number of projection angles (sparse MC) augmented by kernel de-noising to yield a runtime of ~4 min per scan. Scatter correction is combined with two-pass beam hardening correction. Detector lag correction is based on temporal deconvolution of the measured lag response function. The effects of detector veiling glare are reduced by deconvolution of the glare response function representing the long range tails of the detector point-spread function. The performance of the correction framework is quantified in experiments using a realistic head phantom on a testbench for FPD CBCT. Uncorrected reconstructions were non-diagnostic for soft-tissue imaging tasks in the brain. After processing with the artifact correction framework, image uniformity was substantially improved, and artifacts were reduced to a level that enabled visualization of ~3 mm simulated bleeds throughout the brain. Non-uniformity (cupping) was reduced by a factor of 5, and contrast of simulated bleeds was improved from ~7 to 49.7 HU, in good agreement with the nominal blood contrast of 50 HU. Although noise was amplified by the corrections, the contrast-to-noise ratio (CNR) of simulated bleeds was improved by nearly a factor of 3.5 (CNR = 0.54 without corrections and 1.91 after correction). The resulting image quality motivates further development and translation of the FPD-CBCT system for imaging of acute TBI.

SU-F-BRCD-09: Total Variation (TV) Based Fast Convergent Iterative CBCT Reconstruction with GPU Acceleration.

PubMed

Xu, Q; Yang, D; Tan, J; Anastasio, M

2012-06-01

To improve image quality and reduce imaging dose in CBCT for radiation therapy applications and to realize near real-time image reconstruction based on use of a fast convergence iterative algorithm and acceleration by multi-GPUs. An iterative image reconstruction that sought to minimize a weighted least squares cost function that employed total variation (TV) regularization was employed to mitigate projection data incompleteness and noise. To achieve rapid 3D image reconstruction (< 1 min), a highly optimized multiple-GPU implementation of the algorithm was developed. The convergence rate and reconstruction accuracy were evaluated using a modified 3D Shepp-Logan digital phantom and a Catphan-600 physical phantom. The reconstructed images were compared with the clinical FDK reconstruction results. Digital phantom studies showed that only 15 iterations and 60 iterations are needed to achieve algorithm convergence for 360-view and 60-view cases, respectively. The RMSE was reduced to 10-4 and 10-2, respectively, by using 15 iterations for each case. Our algorithm required 5.4s to complete one iteration for the 60-view case using one Tesla C2075 GPU. The few-view study indicated that our iterative algorithm has great potential to reduce the imaging dose and preserve good image quality. For the physical Catphan studies, the images obtained from the iterative algorithm possessed better spatial resolution and higher SNRs than those obtained from by use of a clinical FDK reconstruction algorithm. We have developed a fast convergence iterative algorithm for CBCT image reconstruction. The developed algorithm yielded images with better spatial resolution and higher SNR than those produced by a commercial FDK tool. In addition, from the few-view study, the iterative algorithm has shown great potential for significantly reducing imaging dose. We expect that the developed reconstruction approach will facilitate applications including IGART and patient daily CBCT-based treatment localization. © 2012 American Association of Physicists in Medicine.

Fast generation of computer-generated hologram by graphics processing unit

NASA Astrophysics Data System (ADS)

Matsuda, Sho; Fujii, Tomohiko; Yamaguchi, Takeshi; Yoshikawa, Hiroshi

2009-02-01

A cylindrical hologram is well known to be viewable in 360 deg. This hologram depends high pixel resolution.Therefore, Computer-Generated Cylindrical Hologram (CGCH) requires huge calculation amount.In our previous research, we used look-up table method for fast calculation with Intel Pentium4 2.8 GHz.It took 480 hours to calculate high resolution CGCH (504,000 x 63,000 pixels and the average number of object points are 27,000).To improve quality of CGCH reconstructed image, fringe pattern requires higher spatial frequency and resolution.Therefore, to increase the calculation speed, we have to change the calculation method. In this paper, to reduce the calculation time of CGCH (912,000 x 108,000 pixels), we employ Graphics Processing Unit (GPU).It took 4,406 hours to calculate high resolution CGCH on Xeon 3.4 GHz.Since GPU has many streaming processors and a parallel processing structure, GPU works as the high performance parallel processor.In addition, GPU gives max performance to 2 dimensional data and streaming data.Recently, GPU can be utilized for the general purpose (GPGPU).For example, NVIDIA's GeForce7 series became a programmable processor with Cg programming language.Next GeForce8 series have CUDA as software development kit made by NVIDIA.Theoretically, calculation ability of GPU is announced as 500 GFLOPS. From the experimental result, we have achieved that 47 times faster calculation compared with our previous work which used CPU.Therefore, CGCH can be generated in 95 hours.So, total time is 110 hours to calculate and print the CGCH.

GPU-based Green’s function simulations of shear waves generated by an applied acoustic radiation force in elastic and viscoelastic models

NASA Astrophysics Data System (ADS)

Yang, Yiqun; Urban, Matthew W.; McGough, Robert J.

2018-05-01

Shear wave calculations induced by an acoustic radiation force are very time-consuming on desktop computers, and high-performance graphics processing units (GPUs) achieve dramatic reductions in the computation time for these simulations. The acoustic radiation force is calculated using the fast near field method and the angular spectrum approach, and then the shear waves are calculated in parallel with Green’s functions on a GPU. This combination enables rapid evaluation of shear waves for push beams with different spatial samplings and for apertures with different f/#. Relative to shear wave simulations that evaluate the same algorithm on an Intel i7 desktop computer, a high performance nVidia GPU reduces the time required for these calculations by a factor of 45 and 700 when applied to elastic and viscoelastic shear wave simulation models, respectively. These GPU-accelerated simulations also compared to measurements in different viscoelastic phantoms, and the results are similar. For parametric evaluations and for comparisons with measured shear wave data, shear wave simulations with the Green’s function approach are ideally suited for high-performance GPUs.

GPU-Q-J, a fast method for calculating root mean square deviation (RMSD) after optimal superposition

PubMed Central

2011-01-01

Background Calculation of the root mean square deviation (RMSD) between the atomic coordinates of two optimally superposed structures is a basic component of structural comparison techniques. We describe a quaternion based method, GPU-Q-J, that is stable with single precision calculations and suitable for graphics processor units (GPUs). The application was implemented on an ATI 4770 graphics card in C/C++ and Brook+ in Linux where it was 260 to 760 times faster than existing unoptimized CPU methods. Source code is available from the Compbio website http://software.compbio.washington.edu/misc/downloads/st_gpu_fit/ or from the author LHH. Findings The Nutritious Rice for the World Project (NRW) on World Community Grid predicted de novo, the structures of over 62,000 small proteins and protein domains returning a total of 10 billion candidate structures. Clustering ensembles of structures on this scale requires calculation of large similarity matrices consisting of RMSDs between each pair of structures in the set. As a real-world test, we calculated the matrices for 6 different ensembles from NRW. The GPU method was 260 times faster that the fastest existing CPU based method and over 500 times faster than the method that had been previously used. Conclusions GPU-Q-J is a significant advance over previous CPU methods. It relieves a major bottleneck in the clustering of large numbers of structures for NRW. It also has applications in structure comparison methods that involve multiple superposition and RMSD determination steps, particularly when such methods are applied on a proteome and genome wide scale. PMID:21453553

Accelerating Pseudo-Random Number Generator for MCNP on GPU

NASA Astrophysics Data System (ADS)

Gong, Chunye; Liu, Jie; Chi, Lihua; Hu, Qingfeng; Deng, Li; Gong, Zhenghu

2010-09-01

Pseudo-random number generators (PRNG) are intensively used in many stochastic algorithms in particle simulations, artificial neural networks and other scientific computation. The PRNG in Monte Carlo N-Particle Transport Code (MCNP) requires long period, high quality, flexible jump and fast enough. In this paper, we implement such a PRNG for MCNP on NVIDIA's GTX200 Graphics Processor Units (GPU) using CUDA programming model. Results shows that 3.80 to 8.10 times speedup are achieved compared with 4 to 6 cores CPUs and more than 679.18 million double precision random numbers can be generated per second on GPU.

Fast GPU-based computation of spatial multigrid multiframe LMEM for PET.

PubMed

Nassiri, Moulay Ali; Carrier, Jean-François; Després, Philippe

2015-09-01

Significant efforts were invested during the last decade to accelerate PET list-mode reconstructions, notably with GPU devices. However, the computation time per event is still relatively long, and the list-mode efficiency on the GPU is well below the histogram-mode efficiency. Since list-mode data are not arranged in any regular pattern, costly accesses to the GPU global memory can hardly be optimized and geometrical symmetries cannot be used. To overcome obstacles that limit the acceleration of reconstruction from list-mode on the GPU, a multigrid and multiframe approach of an expectation-maximization algorithm was developed. The reconstruction process is started during data acquisition, and calculations are executed concurrently on the GPU and the CPU, while the system matrix is computed on-the-fly. A new convergence criterion also was introduced, which is computationally more efficient on the GPU. The implementation was tested on a Tesla C2050 GPU device for a Gemini GXL PET system geometry. The results show that the proposed algorithm (multigrid and multiframe list-mode expectation-maximization, MGMF-LMEM) converges to the same solution as the LMEM algorithm more than three times faster. The execution time of the MGMF-LMEM algorithm was 1.1 s per million of events on the Tesla C2050 hardware used, for a reconstructed space of 188 x 188 x 57 voxels of 2 x 2 x 3.15 mm3. For 17- and 22-mm simulated hot lesions, the MGMF-LMEM algorithm led on the first iteration to contrast recovery coefficients (CRC) of more than 75 % of the maximum CRC while achieving a minimum in the relative mean square error. Therefore, the MGMF-LMEM algorithm can be used as a one-pass method to perform real-time reconstructions for low-count acquisitions, as in list-mode gated studies. The computation time for one iteration and 60 millions of events was approximately 66 s.

ARCHER{sub RT} – A GPU-based and photon-electron coupled Monte Carlo dose computing engine for radiation therapy: Software development and application to helical tomotherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Su, Lin; Du, Xining; Liu, Tianyu

Purpose: Using the graphical processing units (GPU) hardware technology, an extremely fast Monte Carlo (MC) code ARCHER{sub RT} is developed for radiation dose calculations in radiation therapy. This paper describes the detailed software development and testing for three clinical TomoTherapy® cases: the prostate, lung, and head and neck. Methods: To obtain clinically relevant dose distributions, phase space files (PSFs) created from optimized radiation therapy treatment plan fluence maps were used as the input to ARCHER{sub RT}. Patient-specific phantoms were constructed from patient CT images. Batch simulations were employed to facilitate the time-consuming task of loading large PSFs, and to improvemore » the estimation of statistical uncertainty. Furthermore, two different Woodcock tracking algorithms were implemented and their relative performance was compared. The dose curves of an Elekta accelerator PSF incident on a homogeneous water phantom were benchmarked against DOSXYZnrc. For each of the treatment cases, dose volume histograms and isodose maps were produced from ARCHER{sub RT} and the general-purpose code, GEANT4. The gamma index analysis was performed to evaluate the similarity of voxel doses obtained from these two codes. The hardware accelerators used in this study are one NVIDIA K20 GPU, one NVIDIA K40 GPU, and six NVIDIA M2090 GPUs. In addition, to make a fairer comparison of the CPU and GPU performance, a multithreaded CPU code was developed using OpenMP and tested on an Intel E5-2620 CPU. Results: For the water phantom, the depth dose curve and dose profiles from ARCHER{sub RT} agree well with DOSXYZnrc. For clinical cases, results from ARCHER{sub RT} are compared with those from GEANT4 and good agreement is observed. Gamma index test is performed for voxels whose dose is greater than 10% of maximum dose. For 2%/2mm criteria, the passing rates for the prostate, lung case, and head and neck cases are 99.7%, 98.5%, and 97.2%, respectively. Due to specific architecture of GPU, modified Woodcock tracking algorithm performed inferior to the original one. ARCHER{sub RT} achieves a fast speed for PSF-based dose calculations. With a single M2090 card, the simulations cost about 60, 50, 80 s for three cases, respectively, with the 1% statistical error in the PTV. Using the latest K40 card, the simulations are 1.7–1.8 times faster. More impressively, six M2090 cards could finish the simulations in 8.9–13.4 s. For comparison, the same simulations on Intel E5-2620 (12 hyperthreading) cost about 500–800 s. Conclusions: ARCHER{sub RT} was developed successfully to perform fast and accurate MC dose calculation for radiotherapy using PSFs and patient CT phantoms.« less

Filopodial dynamics and growth cone stabilization in Drosophila visual circuit development

PubMed Central

Özel, Mehmet Neset; Langen, Marion; Hassan, Bassem A; Hiesinger, P Robin

2015-01-01

Filopodial dynamics are thought to control growth cone guidance, but the types and roles of growth cone dynamics underlying neural circuit assembly in a living brain are largely unknown. To address this issue, we have developed long-term, continuous, fast and high-resolution imaging of growth cone dynamics from axon growth to synapse formation in cultured Drosophila brains. Using R7 photoreceptor neurons as a model we show that >90% of the growth cone filopodia exhibit fast, stochastic dynamics that persist despite ongoing stepwise layer formation. Correspondingly, R7 growth cones stabilize early and change their final position by passive dislocation. N-Cadherin controls both fast filopodial dynamics and growth cone stabilization. Surprisingly, loss of N-Cadherin causes no primary targeting defects, but destabilizes R7 growth cones to jump between correct and incorrect layers. Hence, growth cone dynamics can influence wiring specificity without a direct role in target recognition and implement simple rules during circuit assembly. DOI: http://dx.doi.org/10.7554/eLife.10721.001 PMID:26512889

Guiding and focusing of fast electron beams produced by ultra-intense laser pulse using a double cone funnel target

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Wen-shuai; Cai, Hong-bo, E-mail: Cai-hongbo@iapcm.ac.cn; HEDPS, Center for Applied Physics and Technology, Peking University, Beijing 100871

A novel double cone funnel target design aiming at efficiently guiding and focusing fast electron beams produced in high intensity (>10{sup 19 }W/cm{sup 2}) laser-solid interactions is investigated via two-dimensional particle-in-cell simulations. The forward-going fast electron beams are shown to be directed and focused to a smaller size in comparison with the incident laser spot size. This plasma funnel attached on the cone target guides and focuses electrons in a manner akin to the control of liquid by a plastic funnel. Such device has the potential to add substantial design flexibility and prevent inefficiencies for important applications such as fast ignition.more » Two reasons account for the collimation of fast electron beams. First, the sheath electric fields and quasistatic magnetic fields inside the vacuum gap of the double cone provide confinement of the fast electrons in the laser-plasma interaction region. Second, the interface magnetic fields inside the beam collimator further guide and focus the fast electrons during the transport. The application of this technique to cone-guided fast ignition is considered, and it is shown that it can enhance the laser energy deposition in the compressed fuel plasma by a factor of 2 in comparison with the single cone target case.« less

An analytic linear accelerator source model for GPU-based Monte Carlo dose calculations.

PubMed

Tian, Zhen; Li, Yongbao; Folkerts, Michael; Shi, Feng; Jiang, Steve B; Jia, Xun

2015-10-21

Recently, there has been a lot of research interest in developing fast Monte Carlo (MC) dose calculation methods on graphics processing unit (GPU) platforms. A good linear accelerator (linac) source model is critical for both accuracy and efficiency considerations. In principle, an analytical source model should be more preferred for GPU-based MC dose engines than a phase-space file-based model, in that data loading and CPU-GPU data transfer can be avoided. In this paper, we presented an analytical field-independent source model specifically developed for GPU-based MC dose calculations, associated with a GPU-friendly sampling scheme. A key concept called phase-space-ring (PSR) was proposed. Each PSR contained a group of particles that were of the same type, close in energy and reside in a narrow ring on the phase-space plane located just above the upper jaws. The model parameterized the probability densities of particle location, direction and energy for each primary photon PSR, scattered photon PSR and electron PSR. Models of one 2D Gaussian distribution or multiple Gaussian components were employed to represent the particle direction distributions of these PSRs. A method was developed to analyze a reference phase-space file and derive corresponding model parameters. To efficiently use our model in MC dose calculations on GPU, we proposed a GPU-friendly sampling strategy, which ensured that the particles sampled and transported simultaneously are of the same type and close in energy to alleviate GPU thread divergences. To test the accuracy of our model, dose distributions of a set of open fields in a water phantom were calculated using our source model and compared to those calculated using the reference phase-space files. For the high dose gradient regions, the average distance-to-agreement (DTA) was within 1 mm and the maximum DTA within 2 mm. For relatively low dose gradient regions, the root-mean-square (RMS) dose difference was within 1.1% and the maximum dose difference within 1.7%. The maximum relative difference of output factors was within 0.5%. Over 98.5% passing rate was achieved in 3D gamma-index tests with 2%/2 mm criteria in both an IMRT prostate patient case and a head-and-neck case. These results demonstrated the efficacy of our model in terms of accurately representing a reference phase-space file. We have also tested the efficiency gain of our source model over our previously developed phase-space-let file source model. The overall efficiency of dose calculation was found to be improved by ~1.3-2.2 times in water and patient cases using our analytical model.

OpenGL in Multi-User Web-Based Applications

NASA Astrophysics Data System (ADS)

Szostek, K.; Piórkowski, A.

In this article construction and potential of OpenGL multi-user web-based application are presented. The most common technologies like: .NET ASP, Java and Mono were used with specific OpenGL libraries to visualize tree-dimensional medical data. The most important conclusion of this work is that server side applications can easily take advantage of fast GPU and produce efficient results of advanced computation just like the visualization.

Massive parallelization of a 3D finite difference electromagnetic forward solution using domain decomposition methods on multiple CUDA enabled GPUs

NASA Astrophysics Data System (ADS)

Schultz, A.

2010-12-01

3D forward solvers lie at the core of inverse formulations used to image the variation of electrical conductivity within the Earth's interior. This property is associated with variations in temperature, composition, phase, presence of volatiles, and in specific settings, the presence of groundwater, geothermal resources, oil/gas or minerals. The high cost of 3D solutions has been a stumbling block to wider adoption of 3D methods. Parallel algorithms for modeling frequency domain 3D EM problems have not achieved wide scale adoption, with emphasis on fairly coarse grained parallelism using MPI and similar approaches. The communications bandwidth as well as the latency required to send and receive network communication packets is a limiting factor in implementing fine grained parallel strategies, inhibiting wide adoption of these algorithms. Leading Graphics Processor Unit (GPU) companies now produce GPUs with hundreds of GPU processor cores per die. The footprint, in silicon, of the GPU's restricted instruction set is much smaller than the general purpose instruction set required of a CPU. Consequently, the density of processor cores on a GPU can be much greater than on a CPU. GPUs also have local memory, registers and high speed communication with host CPUs, usually through PCIe type interconnects. The extremely low cost and high computational power of GPUs provides the EM geophysics community with an opportunity to achieve fine grained (i.e. massive) parallelization of codes on low cost hardware. The current generation of GPUs (e.g. NVidia Fermi) provides 3 billion transistors per chip die, with nearly 500 processor cores and up to 6 GB of fast (DDR5) GPU memory. This latest generation of GPU supports fast hardware double precision (64 bit) floating point operations of the type required for frequency domain EM forward solutions. Each Fermi GPU board can sustain nearly 1 TFLOP in double precision, and multiple boards can be installed in the host computer system. We describe our ongoing efforts to achieve massive parallelization on a novel hybrid GPU testbed machine currently configured with 12 Intel Westmere Xeon CPU cores (or 24 parallel computational threads) with 96 GB DDR3 system memory, 4 GPU subsystems which in aggregate contain 960 NVidia Tesla GPU cores with 16 GB dedicated DDR3 GPU memory, and a second interleved bank of 4 GPU subsystems containing in aggregate 1792 NVidia Fermi GPU cores with 12 GB dedicated DDR5 GPU memory. We are applying domain decomposition methods to a modified version of Weiss' (2001) 3D frequency domain full physics EM finite difference code, an open source GPL licensed f90 code available for download from www.OpenEM.org. This will be the core of a new hybrid 3D inversion that parallelizes frequencies across CPUs and individual forward solutions across GPUs. We describe progress made in modifying the code to use direct solvers in GPU cores dedicated to each small subdomain, iteratively improving the solution by matching adjacent subdomain boundary solutions, rather than iterative Krylov space sparse solvers as currently applied to the whole domain.

MAGI: a Node.js web service for fast microRNA-Seq analysis in a GPU infrastructure.

PubMed

Kim, Jihoon; Levy, Eric; Ferbrache, Alex; Stepanowsky, Petra; Farcas, Claudiu; Wang, Shuang; Brunner, Stefan; Bath, Tyler; Wu, Yuan; Ohno-Machado, Lucila

2014-10-01

MAGI is a web service for fast MicroRNA-Seq data analysis in a graphics processing unit (GPU) infrastructure. Using just a browser, users have access to results as web reports in just a few hours->600% end-to-end performance improvement over state of the art. MAGI's salient features are (i) transfer of large input files in native FASTA with Qualities (FASTQ) format through drag-and-drop operations, (ii) rapid prediction of microRNA target genes leveraging parallel computing with GPU devices, (iii) all-in-one analytics with novel feature extraction, statistical test for differential expression and diagnostic plot generation for quality control and (iv) interactive visualization and exploration of results in web reports that are readily available for publication. MAGI relies on the Node.js JavaScript framework, along with NVIDIA CUDA C, PHP: Hypertext Preprocessor (PHP), Perl and R. It is freely available at http://magi.ucsd.edu. © The Author 2014. Published by Oxford University Press.

Dense GPU-enhanced surface reconstruction from stereo endoscopic images for intraoperative registration.

PubMed

Rohl, Sebastian; Bodenstedt, Sebastian; Suwelack, Stefan; Dillmann, Rudiger; Speidel, Stefanie; Kenngott, Hannes; Muller-Stich, Beat P

2012-03-01

In laparoscopic surgery, soft tissue deformations substantially change the surgical site, thus impeding the use of preoperative planning during intraoperative navigation. Extracting depth information from endoscopic images and building a surface model of the surgical field-of-view is one way to represent this constantly deforming environment. The information can then be used for intraoperative registration. Stereo reconstruction is a typical problem within computer vision. However, most of the available methods do not fulfill the specific requirements in a minimally invasive setting such as the need of real-time performance, the problem of view-dependent specular reflections and large curved areas with partly homogeneous or periodic textures and occlusions. In this paper, the authors present an approach toward intraoperative surface reconstruction based on stereo endoscopic images. The authors describe our answer to this problem through correspondence analysis, disparity correction and refinement, 3D reconstruction, point cloud smoothing and meshing. Real-time performance is achieved by implementing the algorithms on the gpu. The authors also present a new hybrid cpu-gpu algorithm that unifies the advantages of the cpu and the gpu version. In a comprehensive evaluation using in vivo data, in silico data from the literature and virtual data from a newly developed simulation environment, the cpu, the gpu, and the hybrid cpu-gpu versions of the surface reconstruction are compared to a cpu and a gpu algorithm from the literature. The recommended approach toward intraoperative surface reconstruction can be conducted in real-time depending on the image resolution (20 fps for the gpu and 14fps for the hybrid cpu-gpu version on resolution of 640 × 480). It is robust to homogeneous regions without texture, large image changes, noise or errors from camera calibration, and it reconstructs the surface down to sub millimeter accuracy. In all the experiments within the simulation environment, the mean distance to ground truth data is between 0.05 and 0.6 mm for the hybrid cpu-gpu version. The hybrid cpu-gpu algorithm shows a much more superior performance than its cpu and gpu counterpart (mean distance reduction 26% and 45%, respectively, for the experiments in the simulation environment). The recommended approach for surface reconstruction is fast, robust, and accurate. It can represent changes in the intraoperative environment and can be used to adapt a preoperative model within the surgical site by registration of these two models.

GGEMS-Brachy: GPU GEant4-based Monte Carlo simulation for brachytherapy applications

NASA Astrophysics Data System (ADS)

Lemaréchal, Yannick; Bert, Julien; Falconnet, Claire; Després, Philippe; Valeri, Antoine; Schick, Ulrike; Pradier, Olivier; Garcia, Marie-Paule; Boussion, Nicolas; Visvikis, Dimitris

2015-07-01

In brachytherapy, plans are routinely calculated using the AAPM TG43 formalism which considers the patient as a simple water object. An accurate modeling of the physical processes considering patient heterogeneity using Monte Carlo simulation (MCS) methods is currently too time-consuming and computationally demanding to be routinely used. In this work we implemented and evaluated an accurate and fast MCS on Graphics Processing Units (GPU) for brachytherapy low dose rate (LDR) applications. A previously proposed Geant4 based MCS framework implemented on GPU (GGEMS) was extended to include a hybrid GPU navigator, allowing navigation within voxelized patient specific images and analytically modeled 125I seeds used in LDR brachytherapy. In addition, dose scoring based on track length estimator including uncertainty calculations was incorporated. The implemented GGEMS-brachy platform was validated using a comparison with Geant4 simulations and reference datasets. Finally, a comparative dosimetry study based on the current clinical standard (TG43) and the proposed platform was performed on twelve prostate cancer patients undergoing LDR brachytherapy. Considering patient 3D CT volumes of 400  × 250  × 65 voxels and an average of 58 implanted seeds, the mean patient dosimetry study run time for a 2% dose uncertainty was 9.35 s (≈500 ms 10-6 simulated particles) and 2.5 s when using one and four GPUs, respectively. The performance of the proposed GGEMS-brachy platform allows envisaging the use of Monte Carlo simulation based dosimetry studies in brachytherapy compatible with clinical practice. Although the proposed platform was evaluated for prostate cancer, it is equally applicable to other LDR brachytherapy clinical applications. Future extensions will allow its application in high dose rate brachytherapy applications.

High performance cellular level agent-based simulation with FLAME for the GPU.

PubMed

Richmond, Paul; Walker, Dawn; Coakley, Simon; Romano, Daniela

2010-05-01

Driven by the availability of experimental data and ability to simulate a biological scale which is of immediate interest, the cellular scale is fast emerging as an ideal candidate for middle-out modelling. As with 'bottom-up' simulation approaches, cellular level simulations demand a high degree of computational power, which in large-scale simulations can only be achieved through parallel computing. The flexible large-scale agent modelling environment (FLAME) is a template driven framework for agent-based modelling (ABM) on parallel architectures ideally suited to the simulation of cellular systems. It is available for both high performance computing clusters (www.flame.ac.uk) and GPU hardware (www.flamegpu.com) and uses a formal specification technique that acts as a universal modelling format. This not only creates an abstraction from the underlying hardware architectures, but avoids the steep learning curve associated with programming them. In benchmarking tests and simulations of advanced cellular systems, FLAME GPU has reported massive improvement in performance over more traditional ABM frameworks. This allows the time spent in the development and testing stages of modelling to be drastically reduced and creates the possibility of real-time visualisation for simple visual face-validation.

ODYSSEY: A PUBLIC GPU-BASED CODE FOR GENERAL RELATIVISTIC RADIATIVE TRANSFER IN KERR SPACETIME

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pu, Hung-Yi; Yun, Kiyun; Yoon, Suk-Jin

General relativistic radiative transfer calculations coupled with the calculation of geodesics in the Kerr spacetime are an essential tool for determining the images, spectra, and light curves from matter in the vicinity of black holes. Such studies are especially important for ongoing and upcoming millimeter/submillimeter very long baseline interferometry observations of the supermassive black holes at the centers of Sgr A* and M87. To this end we introduce Odyssey, a graphics processing unit (GPU) based code for ray tracing and radiative transfer in the Kerr spacetime. On a single GPU, the performance of Odyssey can exceed 1 ns per photon, per Runge–Kutta integrationmore » step. Odyssey is publicly available, fast, accurate, and flexible enough to be modified to suit the specific needs of new users. Along with a Graphical User Interface powered by a video-accelerated display architecture, we also present an educational software tool, Odyssey-Edu, for showing in real time how null geodesics around a Kerr black hole vary as a function of black hole spin and angle of incidence onto the black hole.« less

Graphics processing unit accelerated phase field dislocation dynamics: Application to bi-metallic interfaces

DOE PAGES

Eghtesad, Adnan; Germaschewski, Kai; Beyerlein, Irene J.; ...

2017-10-14

We present the first high-performance computing implementation of the meso-scale phase field dislocation dynamics (PFDD) model on a graphics processing unit (GPU)-based platform. The implementation takes advantage of the portable OpenACC standard directive pragmas along with Nvidia's compute unified device architecture (CUDA) fast Fourier transform (FFT) library called CUFFT to execute the FFT computations within the PFDD formulation on the same GPU platform. The overall implementation is termed ACCPFDD-CUFFT. The package is entirely performance portable due to the use of OPENACC-CUDA inter-operability, in which calls to CUDA functions are replaced with the OPENACC data regions for a host central processingmore » unit (CPU) and device (GPU). A comprehensive benchmark study has been conducted, which compares a number of FFT routines, the Numerical Recipes FFT (FOURN), Fastest Fourier Transform in the West (FFTW), and the CUFFT. The last one exploits the advantages of the GPU hardware for FFT calculations. The novel ACCPFDD-CUFFT implementation is verified using the analytical solutions for the stress field around an infinite edge dislocation and subsequently applied to simulate the interaction and motion of dislocations through a bi-phase copper-nickel (Cu–Ni) interface. It is demonstrated that the ACCPFDD-CUFFT implementation on a single TESLA K80 GPU offers a 27.6X speedup relative to the serial version and a 5X speedup relative to the 22-multicore Intel Xeon CPU E5-2699 v4 @ 2.20 GHz version of the code.« less

Graphics processing unit accelerated phase field dislocation dynamics: Application to bi-metallic interfaces

DOE Office of Scientific and Technical Information (OSTI.GOV)

Eghtesad, Adnan; Germaschewski, Kai; Beyerlein, Irene J.

We present the first high-performance computing implementation of the meso-scale phase field dislocation dynamics (PFDD) model on a graphics processing unit (GPU)-based platform. The implementation takes advantage of the portable OpenACC standard directive pragmas along with Nvidia's compute unified device architecture (CUDA) fast Fourier transform (FFT) library called CUFFT to execute the FFT computations within the PFDD formulation on the same GPU platform. The overall implementation is termed ACCPFDD-CUFFT. The package is entirely performance portable due to the use of OPENACC-CUDA inter-operability, in which calls to CUDA functions are replaced with the OPENACC data regions for a host central processingmore » unit (CPU) and device (GPU). A comprehensive benchmark study has been conducted, which compares a number of FFT routines, the Numerical Recipes FFT (FOURN), Fastest Fourier Transform in the West (FFTW), and the CUFFT. The last one exploits the advantages of the GPU hardware for FFT calculations. The novel ACCPFDD-CUFFT implementation is verified using the analytical solutions for the stress field around an infinite edge dislocation and subsequently applied to simulate the interaction and motion of dislocations through a bi-phase copper-nickel (Cu–Ni) interface. It is demonstrated that the ACCPFDD-CUFFT implementation on a single TESLA K80 GPU offers a 27.6X speedup relative to the serial version and a 5X speedup relative to the 22-multicore Intel Xeon CPU E5-2699 v4 @ 2.20 GHz version of the code.« less

fast_protein_cluster: parallel and optimized clustering of large-scale protein modeling data.

PubMed

Hung, Ling-Hong; Samudrala, Ram

2014-06-15

fast_protein_cluster is a fast, parallel and memory efficient package used to cluster 60 000 sets of protein models (with up to 550 000 models per set) generated by the Nutritious Rice for the World project. fast_protein_cluster is an optimized and extensible toolkit that supports Root Mean Square Deviation after optimal superposition (RMSD) and Template Modeling score (TM-score) as metrics. RMSD calculations using a laptop CPU are 60× faster than qcprot and 3× faster than current graphics processing unit (GPU) implementations. New GPU code further increases the speed of RMSD and TM-score calculations. fast_protein_cluster provides novel k-means and hierarchical clustering methods that are up to 250× and 2000× faster, respectively, than Clusco, and identify significantly more accurate models than Spicker and Clusco. fast_protein_cluster is written in C++ using OpenMP for multi-threading support. Custom streaming Single Instruction Multiple Data (SIMD) extensions and advanced vector extension intrinsics code accelerate CPU calculations, and OpenCL kernels support AMD and Nvidia GPUs. fast_protein_cluster is available under the M.I.T. license. (http://software.compbio.washington.edu/fast_protein_cluster) © The Author 2014. Published by Oxford University Press.

Efficient Implementation of MrBayes on Multi-GPU

PubMed Central

Zhou, Jianfu; Liu, Xiaoguang; Wang, Gang

2013-01-01

MrBayes, using Metropolis-coupled Markov chain Monte Carlo (MCMCMC or (MC)3), is a popular program for Bayesian inference. As a leading method of using DNA data to infer phylogeny, the (MC)3 Bayesian algorithm and its improved and parallel versions are now not fast enough for biologists to analyze massive real-world DNA data. Recently, graphics processor unit (GPU) has shown its power as a coprocessor (or rather, an accelerator) in many fields. This article describes an efficient implementation a(MC)3 (aMCMCMC) for MrBayes (MC)3 on compute unified device architecture. By dynamically adjusting the task granularity to adapt to input data size and hardware configuration, it makes full use of GPU cores with different data sets. An adaptive method is also developed to split and combine DNA sequences to make full use of a large number of GPU cards. Furthermore, a new “node-by-node” task scheduling strategy is developed to improve concurrency, and several optimizing methods are used to reduce extra overhead. Experimental results show that a(MC)3 achieves up to 63× speedup over serial MrBayes on a single machine with one GPU card, and up to 170× speedup with four GPU cards, and up to 478× speedup with a 32-node GPU cluster. a(MC)3 is dramatically faster than all the previous (MC)3 algorithms and scales well to large GPU clusters. PMID:23493260

Efficient implementation of MrBayes on multi-GPU.

PubMed

Bao, Jie; Xia, Hongju; Zhou, Jianfu; Liu, Xiaoguang; Wang, Gang

2013-06-01

MrBayes, using Metropolis-coupled Markov chain Monte Carlo (MCMCMC or (MC)(3)), is a popular program for Bayesian inference. As a leading method of using DNA data to infer phylogeny, the (MC)(3) Bayesian algorithm and its improved and parallel versions are now not fast enough for biologists to analyze massive real-world DNA data. Recently, graphics processor unit (GPU) has shown its power as a coprocessor (or rather, an accelerator) in many fields. This article describes an efficient implementation a(MC)(3) (aMCMCMC) for MrBayes (MC)(3) on compute unified device architecture. By dynamically adjusting the task granularity to adapt to input data size and hardware configuration, it makes full use of GPU cores with different data sets. An adaptive method is also developed to split and combine DNA sequences to make full use of a large number of GPU cards. Furthermore, a new "node-by-node" task scheduling strategy is developed to improve concurrency, and several optimizing methods are used to reduce extra overhead. Experimental results show that a(MC)(3) achieves up to 63× speedup over serial MrBayes on a single machine with one GPU card, and up to 170× speedup with four GPU cards, and up to 478× speedup with a 32-node GPU cluster. a(MC)(3) is dramatically faster than all the previous (MC)(3) algorithms and scales well to large GPU clusters.

Fast quantum Monte Carlo on a GPU

NASA Astrophysics Data System (ADS)

Lutsyshyn, Y.

2015-02-01

We present a scheme for the parallelization of quantum Monte Carlo method on graphical processing units, focusing on variational Monte Carlo simulation of bosonic systems. We use asynchronous execution schemes with shared memory persistence, and obtain an excellent utilization of the accelerator. The CUDA code is provided along with a package that simulates liquid helium-4. The program was benchmarked on several models of Nvidia GPU, including Fermi GTX560 and M2090, and the Kepler architecture K20 GPU. Special optimization was developed for the Kepler cards, including placement of data structures in the register space of the Kepler GPUs. Kepler-specific optimization is discussed.

Accelerating Smith-Waterman Algorithm for Biological Database Search on CUDA-Compatible GPUs

NASA Astrophysics Data System (ADS)

Munekawa, Yuma; Ino, Fumihiko; Hagihara, Kenichi

This paper presents a fast method capable of accelerating the Smith-Waterman algorithm for biological database search on a cluster of graphics processing units (GPUs). Our method is implemented using compute unified device architecture (CUDA), which is available on the nVIDIA GPU. As compared with previous methods, our method has four major contributions. (1) The method efficiently uses on-chip shared memory to reduce the data amount being transferred between off-chip video memory and processing elements in the GPU. (2) It also reduces the number of data fetches by applying a data reuse technique to query and database sequences. (3) A pipelined method is also implemented to overlap GPU execution with database access. (4) Finally, a master/worker paradigm is employed to accelerate hundreds of database searches on a cluster system. In experiments, the peak performance on a GeForce GTX 280 card reaches 8.32 giga cell updates per second (GCUPS). We also find that our method reduces the amount of data fetches to 1/140, achieving approximately three times higher performance than a previous CUDA-based method. Our 32-node cluster version is approximately 28 times faster than a single GPU version. Furthermore, the effective performance reaches 75.6 giga instructions per second (GIPS) using 32 GeForce 8800 GTX cards.

GPU Lossless Hyperspectral Data Compression System for Space Applications

NASA Technical Reports Server (NTRS)

Keymeulen, Didier; Aranki, Nazeeh; Hopson, Ben; Kiely, Aaron; Klimesh, Matthew; Benkrid, Khaled

2012-01-01

On-board lossless hyperspectral data compression reduces data volume in order to meet NASA and DoD limited downlink capabilities. At JPL, a novel, adaptive and predictive technique for lossless compression of hyperspectral data, named the Fast Lossless (FL) algorithm, was recently developed. This technique uses an adaptive filtering method and achieves state-of-the-art performance in both compression effectiveness and low complexity. Because of its outstanding performance and suitability for real-time onboard hardware implementation, the FL compressor is being formalized as the emerging CCSDS Standard for Lossless Multispectral & Hyperspectral image compression. The FL compressor is well-suited for parallel hardware implementation. A GPU hardware implementation was developed for FL targeting the current state-of-the-art GPUs from NVIDIA(Trademark). The GPU implementation on a NVIDIA(Trademark) GeForce(Trademark) GTX 580 achieves a throughput performance of 583.08 Mbits/sec (44.85 MSamples/sec) and an acceleration of at least 6 times a software implementation running on a 3.47 GHz single core Intel(Trademark) Xeon(Trademark) processor. This paper describes the design and implementation of the FL algorithm on the GPU. The massively parallel implementation will provide in the future a fast and practical real-time solution for airborne and space applications.

Fast distributed large-pixel-count hologram computation using a GPU cluster.

PubMed

Pan, Yuechao; Xu, Xuewu; Liang, Xinan

2013-09-10

Large-pixel-count holograms are one essential part for big size holographic three-dimensional (3D) display, but the generation of such holograms is computationally demanding. In order to address this issue, we have built a graphics processing unit (GPU) cluster with 32.5 Tflop/s computing power and implemented distributed hologram computation on it with speed improvement techniques, such as shared memory on GPU, GPU level adaptive load balancing, and node level load distribution. Using these speed improvement techniques on the GPU cluster, we have achieved 71.4 times computation speed increase for 186M-pixel holograms. Furthermore, we have used the approaches of diffraction limits and subdivision of holograms to overcome the GPU memory limit in computing large-pixel-count holograms. 745M-pixel and 1.80G-pixel holograms were computed in 343 and 3326 s, respectively, for more than 2 million object points with RGB colors. Color 3D objects with 1.02M points were successfully reconstructed from 186M-pixel hologram computed in 8.82 s with all the above three speed improvement techniques. It is shown that distributed hologram computation using a GPU cluster is a promising approach to increase the computation speed of large-pixel-count holograms for large size holographic display.

Clinical implementation of a GPU-based simplified Monte Carlo method for a treatment planning system of proton beam therapy.

PubMed

Kohno, R; Hotta, K; Nishioka, S; Matsubara, K; Tansho, R; Suzuki, T

2011-11-21

We implemented the simplified Monte Carlo (SMC) method on graphics processing unit (GPU) architecture under the computer-unified device architecture platform developed by NVIDIA. The GPU-based SMC was clinically applied for four patients with head and neck, lung, or prostate cancer. The results were compared to those obtained by a traditional CPU-based SMC with respect to the computation time and discrepancy. In the CPU- and GPU-based SMC calculations, the estimated mean statistical errors of the calculated doses in the planning target volume region were within 0.5% rms. The dose distributions calculated by the GPU- and CPU-based SMCs were similar, within statistical errors. The GPU-based SMC showed 12.30-16.00 times faster performance than the CPU-based SMC. The computation time per beam arrangement using the GPU-based SMC for the clinical cases ranged 9-67 s. The results demonstrate the successful application of the GPU-based SMC to a clinical proton treatment planning.

Multi-GPU implementation of a VMAT treatment plan optimization algorithm

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tian, Zhen, E-mail: Zhen.Tian@UTSouthwestern.edu, E-mail: Xun.Jia@UTSouthwestern.edu, E-mail: Steve.Jiang@UTSouthwestern.edu; Folkerts, Michael; Tan, Jun

Purpose: Volumetric modulated arc therapy (VMAT) optimization is a computationally challenging problem due to its large data size, high degrees of freedom, and many hardware constraints. High-performance graphics processing units (GPUs) have been used to speed up the computations. However, GPU’s relatively small memory size cannot handle cases with a large dose-deposition coefficient (DDC) matrix in cases of, e.g., those with a large target size, multiple targets, multiple arcs, and/or small beamlet size. The main purpose of this paper is to report an implementation of a column-generation-based VMAT algorithm, previously developed in the authors’ group, on a multi-GPU platform tomore » solve the memory limitation problem. While the column-generation-based VMAT algorithm has been previously developed, the GPU implementation details have not been reported. Hence, another purpose is to present detailed techniques employed for GPU implementation. The authors also would like to utilize this particular problem as an example problem to study the feasibility of using a multi-GPU platform to solve large-scale problems in medical physics. Methods: The column-generation approach generates VMAT apertures sequentially by solving a pricing problem (PP) and a master problem (MP) iteratively. In the authors’ method, the sparse DDC matrix is first stored on a CPU in coordinate list format (COO). On the GPU side, this matrix is split into four submatrices according to beam angles, which are stored on four GPUs in compressed sparse row format. Computation of beamlet price, the first step in PP, is accomplished using multi-GPUs. A fast inter-GPU data transfer scheme is accomplished using peer-to-peer access. The remaining steps of PP and MP problems are implemented on CPU or a single GPU due to their modest problem scale and computational loads. Barzilai and Borwein algorithm with a subspace step scheme is adopted here to solve the MP problem. A head and neck (H and N) cancer case is then used to validate the authors’ method. The authors also compare their multi-GPU implementation with three different single GPU implementation strategies, i.e., truncating DDC matrix (S1), repeatedly transferring DDC matrix between CPU and GPU (S2), and porting computations involving DDC matrix to CPU (S3), in terms of both plan quality and computational efficiency. Two more H and N patient cases and three prostate cases are used to demonstrate the advantages of the authors’ method. Results: The authors’ multi-GPU implementation can finish the optimization process within ∼1 min for the H and N patient case. S1 leads to an inferior plan quality although its total time was 10 s shorter than the multi-GPU implementation due to the reduced matrix size. S2 and S3 yield the same plan quality as the multi-GPU implementation but take ∼4 and ∼6 min, respectively. High computational efficiency was consistently achieved for the other five patient cases tested, with VMAT plans of clinically acceptable quality obtained within 23–46 s. Conversely, to obtain clinically comparable or acceptable plans for all six of these VMAT cases that the authors have tested in this paper, the optimization time needed in a commercial TPS system on CPU was found to be in an order of several minutes. Conclusions: The results demonstrate that the multi-GPU implementation of the authors’ column-generation-based VMAT optimization can handle the large-scale VMAT optimization problem efficiently without sacrificing plan quality. The authors’ study may serve as an example to shed some light on other large-scale medical physics problems that require multi-GPU techniques.« less

Bin-Hash Indexing: A Parallel Method for Fast Query Processing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bethel, Edward W; Gosink, Luke J.; Wu, Kesheng

2008-06-27

This paper presents a new parallel indexing data structure for answering queries. The index, called Bin-Hash, offers extremely high levels of concurrency, and is therefore well-suited for the emerging commodity of parallel processors, such as multi-cores, cell processors, and general purpose graphics processing units (GPU). The Bin-Hash approach first bins the base data, and then partitions and separately stores the values in each bin as a perfect spatial hash table. To answer a query, we first determine whether or not a record satisfies the query conditions based on the bin boundaries. For the bins with records that can not bemore » resolved, we examine the spatial hash tables. The procedures for examining the bin numbers and the spatial hash tables offer the maximum possible level of concurrency; all records are able to be evaluated by our procedure independently in parallel. Additionally, our Bin-Hash procedures access much smaller amounts of data than similar parallel methods, such as the projection index. This smaller data footprint is critical for certain parallel processors, like GPUs, where memory resources are limited. To demonstrate the effectiveness of Bin-Hash, we implement it on a GPU using the data-parallel programming language CUDA. The concurrency offered by the Bin-Hash index allows us to fully utilize the GPU's massive parallelism in our work; over 12,000 records can be simultaneously evaluated at any one time. We show that our new query processing method is an order of magnitude faster than current state-of-the-art CPU-based indexing technologies. Additionally, we compare our performance to existing GPU-based projection index strategies.« less

WE-AB-204-11: Development of a Nuclear Medicine Dosimetry Module for the GPU-Based Monte Carlo Code ARCHER

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, T; Lin, H; Xu, X

Purpose: To develop a nuclear medicine dosimetry module for the GPU-based Monte Carlo code ARCHER. Methods: We have developed a nuclear medicine dosimetry module for the fast Monte Carlo code ARCHER. The coupled electron-photon Monte Carlo transport kernel included in ARCHER is built upon the Dose Planning Method code (DPM). The developed module manages the radioactive decay simulation by consecutively tracking several types of radiation on a per disintegration basis using the statistical sampling method. Optimization techniques such as persistent threads and prefetching are studied and implemented. The developed module is verified against the VIDA code, which is based onmore » Geant4 toolkit and has previously been verified against OLINDA/EXM. A voxelized geometry is used in the preliminary test: a sphere made of ICRP soft tissue is surrounded by a box filled with water. Uniform activity distribution of I-131 is assumed in the sphere. Results: The self-absorption dose factors (mGy/MBqs) of the sphere with varying diameters are calculated by ARCHER and VIDA respectively. ARCHER’s result is in agreement with VIDA’s that are obtained from a previous publication. VIDA takes hours of CPU time to finish the computation, while it takes ARCHER 4.31 seconds for the 12.4-cm uniform activity sphere case. For a fairer CPU-GPU comparison, more effort will be made to eliminate the algorithmic differences. Conclusion: The coupled electron-photon Monte Carlo code ARCHER has been extended to radioactive decay simulation for nuclear medicine dosimetry. The developed code exhibits good performance in our preliminary test. The GPU-based Monte Carlo code is developed with grant support from the National Institute of Biomedical Imaging and Bioengineering through an R01 grant (R01EB015478)« less

A fast image registration approach of neural activities in light-sheet fluorescence microscopy images

NASA Astrophysics Data System (ADS)

Meng, Hui; Hui, Hui; Hu, Chaoen; Yang, Xin; Tian, Jie

2017-03-01

The ability of fast and single-neuron resolution imaging of neural activities enables light-sheet fluorescence microscopy (LSFM) as a powerful imaging technique in functional neural connection applications. The state-of-art LSFM imaging system can record the neuronal activities of entire brain for small animal, such as zebrafish or C. elegans at single-neuron resolution. However, the stimulated and spontaneous movements in animal brain result in inconsistent neuron positions during recording process. It is time consuming to register the acquired large-scale images with conventional method. In this work, we address the problem of fast registration of neural positions in stacks of LSFM images. This is necessary to register brain structures and activities. To achieve fast registration of neural activities, we present a rigid registration architecture by implementation of Graphics Processing Unit (GPU). In this approach, the image stacks were preprocessed on GPU by mean stretching to reduce the computation effort. The present image was registered to the previous image stack that considered as reference. A fast Fourier transform (FFT) algorithm was used for calculating the shift of the image stack. The calculations for image registration were performed in different threads while the preparation functionality was refactored and called only once by the master thread. We implemented our registration algorithm on NVIDIA Quadro K4200 GPU under Compute Unified Device Architecture (CUDA) programming environment. The experimental results showed that the registration computation can speed-up to 550ms for a full high-resolution brain image. Our approach also has potential to be used for other dynamic image registrations in biomedical applications.

Monte Carlo dose calculations for high-dose-rate brachytherapy using GPU-accelerated processing.

PubMed

Tian, Z; Zhang, M; Hrycushko, B; Albuquerque, K; Jiang, S B; Jia, X

2016-01-01

Current clinical brachytherapy dose calculations are typically based on the Association of American Physicists in Medicine Task Group report 43 (TG-43) guidelines, which approximate patient geometry as an infinitely large water phantom. This ignores patient and applicator geometries and heterogeneities, causing dosimetric errors. Although Monte Carlo (MC) dose calculation is commonly recognized as the most accurate method, its associated long computational time is a major bottleneck for routine clinical applications. This article presents our recent developments of a fast MC dose calculation package for high-dose-rate (HDR) brachytherapy, gBMC, built on a graphics processing unit (GPU) platform. gBMC-simulated photon transport in voxelized geometry with physics in (192)Ir HDR brachytherapy energy range considered. A phase-space file was used as a source model. GPU-based parallel computation was used to simultaneously transport multiple photons, one on a GPU thread. We validated gBMC by comparing the dose calculation results in water with that computed TG-43. We also studied heterogeneous phantom cases and a patient case and compared gBMC results with Acuros BV results. Radial dose function in water calculated by gBMC showed <0.6% relative difference from that of the TG-43 data. Difference in anisotropy function was <1%. In two heterogeneous slab phantoms and one shielded cylinder applicator case, average dose discrepancy between gBMC and Acuros BV was <0.87%. For a tandem and ovoid patient case, good agreement between gBMC and Acruos BV results was observed in both isodose lines and dose-volume histograms. In terms of the efficiency, it took ∼47.5 seconds for gBMC to reach 0.15% statistical uncertainty within the 5% isodose line for the patient case. The accuracy and efficiency of a new GPU-based MC dose calculation package, gBMC, for HDR brachytherapy make it attractive for clinical applications. Copyright © 2016 American Brachytherapy Society. Published by Elsevier Inc. All rights reserved.

A fast three-dimensional gamma evaluation using a GPU utilizing texture memory for on-the-fly interpolations.

PubMed

Persoon, Lucas C G G; Podesta, Mark; van Elmpt, Wouter J C; Nijsten, Sebastiaan M J J G; Verhaegen, Frank

2011-07-01

A widely accepted method to quantify differences in dose distributions is the gamma (gamma) evaluation. Currently, almost all gamma implementations utilize the central processing unit (CPU). Recently, the graphics processing unit (GPU) has become a powerful platform for specific computing tasks. In this study, we describe the implementation of a 3D gamma evaluation using a GPU to improve calculation time. The gamma evaluation algorithm was implemented on an NVIDIA Tesla C2050 GPU using the compute unified device architecture (CUDA). First, several cubic virtual phantoms were simulated. These phantoms were tested with varying dose cube sizes and set-ups, introducing artificial dose differences. Second, to show applicability in clinical practice, five patient cases have been evaluated using the 3D dose distribution from a treatment planning system as the reference and the delivered dose determined during treatment as the comparison. A calculation time comparison between the CPU and GPU was made with varying thread-block sizes including the option of using texture or global memory. A GPU over CPU speed-up of 66 +/- 12 was achieved for the virtual phantoms. For the patient cases, a speed-up of 57 +/- 15 using the GPU was obtained. A thread-block size of 16 x 16 performed best in all cases. The use of texture memory improved the total calculation time, especially when interpolation was applied. Differences between the CPU and GPU gammas were negligible. The GPU and its features, such as texture memory, decreased the calculation time for gamma evaluations considerably without loss of accuracy.

WE-AB-207A-08: BEST IN PHYSICS (IMAGING): Advanced Scatter Correction and Iterative Reconstruction for Improved Cone-Beam CT Imaging On the TrueBeam Radiotherapy Machine

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, A; Paysan, P; Brehm, M

2016-06-15

Purpose: To improve CBCT image quality for image-guided radiotherapy by applying advanced reconstruction algorithms to overcome scatter, noise, and artifact limitations Methods: CBCT is used extensively for patient setup in radiotherapy. However, image quality generally falls short of diagnostic CT, limiting soft-tissue based positioning and potential applications such as adaptive radiotherapy. The conventional TrueBeam CBCT reconstructor uses a basic scatter correction and FDK reconstruction, resulting in residual scatter artifacts, suboptimal image noise characteristics, and other artifacts like cone-beam artifacts. We have developed an advanced scatter correction that uses a finite-element solver (AcurosCTS) to model the behavior of photons as theymore » pass (and scatter) through the object. Furthermore, iterative reconstruction is applied to the scatter-corrected projections, enforcing data consistency with statistical weighting and applying an edge-preserving image regularizer to reduce image noise. The combined algorithms have been implemented on a GPU. CBCT projections from clinically operating TrueBeam systems have been used to compare image quality between the conventional and improved reconstruction methods. Planning CT images of the same patients have also been compared. Results: The advanced scatter correction removes shading and inhomogeneity artifacts, reducing the scatter artifact from 99.5 HU to 13.7 HU in a typical pelvis case. Iterative reconstruction provides further benefit by reducing image noise and eliminating streak artifacts, thereby improving soft-tissue visualization. In a clinical head and pelvis CBCT, the noise was reduced by 43% and 48%, respectively, with no change in spatial resolution (assessed visually). Additional benefits include reduction of cone-beam artifacts and reduction of metal artifacts due to intrinsic downweighting of corrupted rays. Conclusion: The combination of an advanced scatter correction with iterative reconstruction substantially improves CBCT image quality. It is anticipated that clinically acceptable reconstruction times will result from a multi-GPU implementation (the algorithms are under active development and not yet commercially available). All authors are employees of and (may) own stock of Varian Medical Systems.« less

Ice-sheet modelling accelerated by graphics cards

NASA Astrophysics Data System (ADS)

Brædstrup, Christian Fredborg; Damsgaard, Anders; Egholm, David Lundbek

2014-11-01

Studies of glaciers and ice sheets have increased the demand for high performance numerical ice flow models over the past decades. When exploring the highly non-linear dynamics of fast flowing glaciers and ice streams, or when coupling multiple flow processes for ice, water, and sediment, researchers are often forced to use super-computing clusters. As an alternative to conventional high-performance computing hardware, the Graphical Processing Unit (GPU) is capable of massively parallel computing while retaining a compact design and low cost. In this study, we present a strategy for accelerating a higher-order ice flow model using a GPU. By applying the newest GPU hardware, we achieve up to 180× speedup compared to a similar but serial CPU implementation. Our results suggest that GPU acceleration is a competitive option for ice-flow modelling when compared to CPU-optimised algorithms parallelised by the OpenMP or Message Passing Interface (MPI) protocols.

3D fast adaptive correlation imaging for large-scale gravity data based on GPU computation

NASA Astrophysics Data System (ADS)

Chen, Z.; Meng, X.; Guo, L.; Liu, G.

2011-12-01

In recent years, large scale gravity data sets have been collected and employed to enhance gravity problem-solving abilities of tectonics studies in China. Aiming at the large scale data and the requirement of rapid interpretation, previous authors have carried out a lot of work, including the fast gradient module inversion and Euler deconvolution depth inversion ,3-D physical property inversion using stochastic subspaces and equivalent storage, fast inversion using wavelet transforms and a logarithmic barrier method. So it can be say that 3-D gravity inversion has been greatly improved in the last decade. Many authors added many different kinds of priori information and constraints to deal with nonuniqueness using models composed of a large number of contiguous cells of unknown property and obtained good results. However, due to long computation time, instability and other shortcomings, 3-D physical property inversion has not been widely applied to large-scale data yet. In order to achieve 3-D interpretation with high efficiency and precision for geological and ore bodies and obtain their subsurface distribution, there is an urgent need to find a fast and efficient inversion method for large scale gravity data. As an entirely new geophysical inversion method, 3D correlation has a rapid development thanks to the advantage of requiring no a priori information and demanding small amount of computer memory. This method was proposed to image the distribution of equivalent excess masses of anomalous geological bodies with high resolution both longitudinally and transversely. In order to tranform the equivalence excess masses into real density contrasts, we adopt the adaptive correlation imaging for gravity data. After each 3D correlation imaging, we change the equivalence into density contrasts according to the linear relationship, and then carry out forward gravity calculation for each rectangle cells. Next, we compare the forward gravity data with real data, and comtinue to perform 3D correlation imaging for the redisual gravity data. After several iterations, we can obtain a satisfactoy results. Newly developed general purpose computing technology from Nvidia GPU (Graphics Processing Unit) has been put into practice and received widespread attention in many areas. Based on the GPU programming mode and two parallel levels, five CPU loops for the main computation of 3D correlation imaging are converted into three loops in GPU kernel functions, thus achieving GPU/CPU collaborative computing. The two inner loops are defined as the dimensions of blocks and the three outer loops are defined as the dimensions of threads, thus realizing the double loop block calculation. Theoretical and real gravity data tests show that results are reliable and the computing time is greatly reduced. Acknowledgments We acknowledge the financial support of Sinoprobe project (201011039 and 201011049-03), the Fundamental Research Funds for the Central Universities (2010ZY26 and 2011PY0183), the National Natural Science Foundation of China (41074095) and the Open Project of State Key Laboratory of Geological Processes and Mineral Resources (GPMR0945).

A GPU-accelerated semi-implicit fractional-step method for numerical solutions of incompressible Navier-Stokes equations

NASA Astrophysics Data System (ADS)

Ha, Sanghyun; Park, Junshin; You, Donghyun

2018-01-01

Utility of the computational power of Graphics Processing Units (GPUs) is elaborated for solutions of incompressible Navier-Stokes equations which are integrated using a semi-implicit fractional-step method. The Alternating Direction Implicit (ADI) and the Fourier-transform-based direct solution methods used in the semi-implicit fractional-step method take advantage of multiple tridiagonal matrices whose inversion is known as the major bottleneck for acceleration on a typical multi-core machine. A novel implementation of the semi-implicit fractional-step method designed for GPU acceleration of the incompressible Navier-Stokes equations is presented. Aspects of the programing model of Compute Unified Device Architecture (CUDA), which are critical to the bandwidth-bound nature of the present method are discussed in detail. A data layout for efficient use of CUDA libraries is proposed for acceleration of tridiagonal matrix inversion and fast Fourier transform. OpenMP is employed for concurrent collection of turbulence statistics on a CPU while the Navier-Stokes equations are computed on a GPU. Performance of the present method using CUDA is assessed by comparing the speed of solving three tridiagonal matrices using ADI with the speed of solving one heptadiagonal matrix using a conjugate gradient method. An overall speedup of 20 times is achieved using a Tesla K40 GPU in comparison with a single-core Xeon E5-2660 v3 CPU in simulations of turbulent boundary-layer flow over a flat plate conducted on over 134 million grids. Enhanced performance of 48 times speedup is reached for the same problem using a Tesla P100 GPU.

Holographic video at 40 frames per second for 4-million object points.

PubMed

Tsang, Peter; Cheung, W-K; Poon, T-C; Zhou, C

2011-08-01

We propose a fast method for generating digital Fresnel holograms based on an interpolated wavefront-recording plane (IWRP) approach. Our method can be divided into two stages. First, a small, virtual IWRP is derived in a computational-free manner. Second, the IWRP is expanded into a Fresnel hologram with a pair of fast Fourier transform processes, which are realized with the graphic processing unit (GPU). We demonstrate state-of-the-art experimental results, capable of generating a 2048 x 2048 Fresnel hologram of around 4 × 10(6) object points at a rate of over 40 frames per second.

Real-time autocorrelator for fluorescence correlation spectroscopy based on graphical-processor-unit architecture: method, implementation, and comparative studies

NASA Astrophysics Data System (ADS)

Laracuente, Nicholas; Grossman, Carl

2013-03-01

We developed an algorithm and software to calculate autocorrelation functions from real-time photon-counting data using the fast, parallel capabilities of graphical processor units (GPUs). Recent developments in hardware and software have allowed for general purpose computing with inexpensive GPU hardware. These devices are more suited for emulating hardware autocorrelators than traditional CPU-based software applications by emphasizing parallel throughput over sequential speed. Incoming data are binned in a standard multi-tau scheme with configurable points-per-bin size and are mapped into a GPU memory pattern to reduce time-expensive memory access. Applications include dynamic light scattering (DLS) and fluorescence correlation spectroscopy (FCS) experiments. We ran the software on a 64-core graphics pci card in a 3.2 GHz Intel i5 CPU based computer running Linux. FCS measurements were made on Alexa-546 and Texas Red dyes in a standard buffer (PBS). Software correlations were compared to hardware correlator measurements on the same signals. Supported by HHMI and Swarthmore College

Fast, multi-channel real-time processing of signals with microsecond latency using graphics processing units.

PubMed

Rath, N; Kato, S; Levesque, J P; Mauel, M E; Navratil, G A; Peng, Q

2014-04-01

Fast, digital signal processing (DSP) has many applications. Typical hardware options for performing DSP are field-programmable gate arrays (FPGAs), application-specific integrated DSP chips, or general purpose personal computer systems. This paper presents a novel DSP platform that has been developed for feedback control on the HBT-EP tokamak device. The system runs all signal processing exclusively on a Graphics Processing Unit (GPU) to achieve real-time performance with latencies below 8 μs. Signals are transferred into and out of the GPU using PCI Express peer-to-peer direct-memory-access transfers without involvement of the central processing unit or host memory. Tests were performed on the feedback control system of the HBT-EP tokamak using forty 16-bit floating point inputs and outputs each and a sampling rate of up to 250 kHz. Signals were digitized by a D-TACQ ACQ196 module, processing done on an NVIDIA GTX 580 GPU programmed in CUDA, and analog output was generated by D-TACQ AO32CPCI modules.

High-Speed GPU-Based Fully Three-Dimensional Diffuse Optical Tomographic System

PubMed Central

Saikia, Manob Jyoti; Kanhirodan, Rajan; Mohan Vasu, Ram

2014-01-01

We have developed a graphics processor unit (GPU-) based high-speed fully 3D system for diffuse optical tomography (DOT). The reduction in execution time of 3D DOT algorithm, a severely ill-posed problem, is made possible through the use of (1) an algorithmic improvement that uses Broyden approach for updating the Jacobian matrix and thereby updating the parameter matrix and (2) the multinode multithreaded GPU and CUDA (Compute Unified Device Architecture) software architecture. Two different GPU implementations of DOT programs are developed in this study: (1) conventional C language program augmented by GPU CUDA and CULA routines (C GPU), (2) MATLAB program supported by MATLAB parallel computing toolkit for GPU (MATLAB GPU). The computation time of the algorithm on host CPU and the GPU system is presented for C and Matlab implementations. The forward computation uses finite element method (FEM) and the problem domain is discretized into 14610, 30823, and 66514 tetrahedral elements. The reconstruction time, so achieved for one iteration of the DOT reconstruction for 14610 elements, is 0.52 seconds for a C based GPU program for 2-plane measurements. The corresponding MATLAB based GPU program took 0.86 seconds. The maximum number of reconstructed frames so achieved is 2 frames per second. PMID:24891848

High-Speed GPU-Based Fully Three-Dimensional Diffuse Optical Tomographic System.

PubMed

Saikia, Manob Jyoti; Kanhirodan, Rajan; Mohan Vasu, Ram

2014-01-01

We have developed a graphics processor unit (GPU-) based high-speed fully 3D system for diffuse optical tomography (DOT). The reduction in execution time of 3D DOT algorithm, a severely ill-posed problem, is made possible through the use of (1) an algorithmic improvement that uses Broyden approach for updating the Jacobian matrix and thereby updating the parameter matrix and (2) the multinode multithreaded GPU and CUDA (Compute Unified Device Architecture) software architecture. Two different GPU implementations of DOT programs are developed in this study: (1) conventional C language program augmented by GPU CUDA and CULA routines (C GPU), (2) MATLAB program supported by MATLAB parallel computing toolkit for GPU (MATLAB GPU). The computation time of the algorithm on host CPU and the GPU system is presented for C and Matlab implementations. The forward computation uses finite element method (FEM) and the problem domain is discretized into 14610, 30823, and 66514 tetrahedral elements. The reconstruction time, so achieved for one iteration of the DOT reconstruction for 14610 elements, is 0.52 seconds for a C based GPU program for 2-plane measurements. The corresponding MATLAB based GPU program took 0.86 seconds. The maximum number of reconstructed frames so achieved is 2 frames per second.

Porting ONETEP to graphical processing unit-based coprocessors. 1. FFT box operations.

PubMed

Wilkinson, Karl; Skylaris, Chris-Kriton

2013-10-30

We present the first graphical processing unit (GPU) coprocessor-enabled version of the Order-N Electronic Total Energy Package (ONETEP) code for linear-scaling first principles quantum mechanical calculations on materials. This work focuses on porting to the GPU the parts of the code that involve atom-localized fast Fourier transform (FFT) operations. These are among the most computationally intensive parts of the code and are used in core algorithms such as the calculation of the charge density, the local potential integrals, the kinetic energy integrals, and the nonorthogonal generalized Wannier function gradient. We have found that direct porting of the isolated FFT operations did not provide any benefit. Instead, it was necessary to tailor the port to each of the aforementioned algorithms to optimize data transfer to and from the GPU. A detailed discussion of the methods used and tests of the resulting performance are presented, which show that individual steps in the relevant algorithms are accelerated by a significant amount. However, the transfer of data between the GPU and host machine is a significant bottleneck in the reported version of the code. In addition, an initial investigation into a dynamic precision scheme for the ONETEP energy calculation has been performed to take advantage of the enhanced single precision capabilities of GPUs. The methods used here result in no disruption to the existing code base. Furthermore, as the developments reported here concern the core algorithms, they will benefit the full range of ONETEP functionality. Our use of a directive-based programming model ensures portability to other forms of coprocessors and will allow this work to form the basis of future developments to the code designed to support emerging high-performance computing platforms. Copyright © 2013 Wiley Periodicals, Inc.

Tempest: GPU-CPU computing for high-throughput database spectral matching.

PubMed

Milloy, Jeffrey A; Faherty, Brendan K; Gerber, Scott A

2012-07-06

Modern mass spectrometers are now capable of producing hundreds of thousands of tandem (MS/MS) spectra per experiment, making the translation of these fragmentation spectra into peptide matches a common bottleneck in proteomics research. When coupled with experimental designs that enrich for post-translational modifications such as phosphorylation and/or include isotopically labeled amino acids for quantification, additional burdens are placed on this computational infrastructure by shotgun sequencing. To address this issue, we have developed a new database searching program that utilizes the massively parallel compute capabilities of a graphical processing unit (GPU) to produce peptide spectral matches in a very high throughput fashion. Our program, named Tempest, combines efficient database digestion and MS/MS spectral indexing on a CPU with fast similarity scoring on a GPU. In our implementation, the entire similarity score, including the generation of full theoretical peptide candidate fragmentation spectra and its comparison to experimental spectra, is conducted on the GPU. Although Tempest uses the classical SEQUEST XCorr score as a primary metric for evaluating similarity for spectra collected at unit resolution, we have developed a new "Accelerated Score" for MS/MS spectra collected at high resolution that is based on a computationally inexpensive dot product but exhibits scoring accuracy similar to that of the classical XCorr. In our experience, Tempest provides compute-cluster level performance in an affordable desktop computer.

A simple method to prevent hard X-ray-induced preheating effects inside the cone tip in indirect-drive fast ignition implosions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Dongxiao; Shan, Lianqiang; Zhou, Weimin

During fast-ignition implosions, preheating of inside the cone tip caused by hard X-rays can strongly affect the generation and transport of hot electrons in the cone. Although indirect-drive implosions have a higher implosion symmetry, they cause stronger preheating effects than direct-drive implosions. To control the preheating of the cone tip, we propose the use of indirect-drive fast-ignition targets with thicker tips. Experiments carried out at the ShenGuang-III prototype laser facility confirmed that thicker tips are effective for controlling preheating. Moreover, these results were consistent with those of 1D radiation hydrodynamic simulations.

Accelerated rescaling of single Monte Carlo simulation runs with the Graphics Processing Unit (GPU).

PubMed

Yang, Owen; Choi, Bernard

2013-01-01

To interpret fiber-based and camera-based measurements of remitted light from biological tissues, researchers typically use analytical models, such as the diffusion approximation to light transport theory, or stochastic models, such as Monte Carlo modeling. To achieve rapid (ideally real-time) measurement of tissue optical properties, especially in clinical situations, there is a critical need to accelerate Monte Carlo simulation runs. In this manuscript, we report on our approach using the Graphics Processing Unit (GPU) to accelerate rescaling of single Monte Carlo runs to calculate rapidly diffuse reflectance values for different sets of tissue optical properties. We selected MATLAB to enable non-specialists in C and CUDA-based programming to use the generated open-source code. We developed a software package with four abstraction layers. To calculate a set of diffuse reflectance values from a simulated tissue with homogeneous optical properties, our rescaling GPU-based approach achieves a reduction in computation time of several orders of magnitude as compared to other GPU-based approaches. Specifically, our GPU-based approach generated a diffuse reflectance value in 0.08ms. The transfer time from CPU to GPU memory currently is a limiting factor with GPU-based calculations. However, for calculation of multiple diffuse reflectance values, our GPU-based approach still can lead to processing that is ~3400 times faster than other GPU-based approaches.

Fast skin dose estimation system for interventional radiology

PubMed Central

Takata, Takeshi; Kotoku, Jun’ichi; Maejima, Hideyuki; Kumagai, Shinobu; Arai, Norikazu; Kobayashi, Takenori; Shiraishi, Kenshiro; Yamamoto, Masayoshi; Kondo, Hiroshi; Furui, Shigeru

2018-01-01

Abstract To minimise the radiation dermatitis related to interventional radiology (IR), rapid and accurate dose estimation has been sought for all procedures. We propose a technique for estimating the patient skin dose rapidly and accurately using Monte Carlo (MC) simulation with a graphical processing unit (GPU, GTX 1080; Nvidia Corp.). The skin dose distribution is simulated based on an individual patient’s computed tomography (CT) dataset for fluoroscopic conditions after the CT dataset has been segmented into air, water and bone based on pixel values. The skin is assumed to be one layer at the outer surface of the body. Fluoroscopic conditions are obtained from a log file of a fluoroscopic examination. Estimating the absorbed skin dose distribution requires calibration of the dose simulated by our system. For this purpose, a linear function was used to approximate the relation between the simulated dose and the measured dose using radiophotoluminescence (RPL) glass dosimeters in a water-equivalent phantom. Differences of maximum skin dose between our system and the Particle and Heavy Ion Transport code System (PHITS) were as high as 6.1%. The relative statistical error (2 σ) for the simulated dose obtained using our system was ≤3.5%. Using a GPU, the simulation on the chest CT dataset aiming at the heart was within 3.49 s on average: the GPU is 122 times faster than a CPU (Core i7–7700K; Intel Corp.). Our system (using the GPU, the log file, and the CT dataset) estimated the skin dose more rapidly and more accurately than conventional methods. PMID:29136194

Fast skin dose estimation system for interventional radiology.

PubMed

Takata, Takeshi; Kotoku, Jun'ichi; Maejima, Hideyuki; Kumagai, Shinobu; Arai, Norikazu; Kobayashi, Takenori; Shiraishi, Kenshiro; Yamamoto, Masayoshi; Kondo, Hiroshi; Furui, Shigeru

2018-03-01

To minimise the radiation dermatitis related to interventional radiology (IR), rapid and accurate dose estimation has been sought for all procedures. We propose a technique for estimating the patient skin dose rapidly and accurately using Monte Carlo (MC) simulation with a graphical processing unit (GPU, GTX 1080; Nvidia Corp.). The skin dose distribution is simulated based on an individual patient's computed tomography (CT) dataset for fluoroscopic conditions after the CT dataset has been segmented into air, water and bone based on pixel values. The skin is assumed to be one layer at the outer surface of the body. Fluoroscopic conditions are obtained from a log file of a fluoroscopic examination. Estimating the absorbed skin dose distribution requires calibration of the dose simulated by our system. For this purpose, a linear function was used to approximate the relation between the simulated dose and the measured dose using radiophotoluminescence (RPL) glass dosimeters in a water-equivalent phantom. Differences of maximum skin dose between our system and the Particle and Heavy Ion Transport code System (PHITS) were as high as 6.1%. The relative statistical error (2 σ) for the simulated dose obtained using our system was ≤3.5%. Using a GPU, the simulation on the chest CT dataset aiming at the heart was within 3.49 s on average: the GPU is 122 times faster than a CPU (Core i7-7700K; Intel Corp.). Our system (using the GPU, the log file, and the CT dataset) estimated the skin dose more rapidly and more accurately than conventional methods.

Real-time optical flow estimation on a GPU for a skied-steered mobile robot

NASA Astrophysics Data System (ADS)

Kniaz, V. V.

2016-04-01

Accurate egomotion estimation is required for mobile robot navigation. Often the egomotion is estimated using optical flow algorithms. For an accurate estimation of optical flow most of modern algorithms require high memory resources and processor speed. However simple single-board computers that control the motion of the robot usually do not provide such resources. On the other hand, most of modern single-board computers are equipped with an embedded GPU that could be used in parallel with a CPU to improve the performance of the optical flow estimation algorithm. This paper presents a new Z-flow algorithm for efficient computation of an optical flow using an embedded GPU. The algorithm is based on the phase correlation optical flow estimation and provide a real-time performance on a low cost embedded GPU. The layered optical flow model is used. Layer segmentation is performed using graph-cut algorithm with a time derivative based energy function. Such approach makes the algorithm both fast and robust in low light and low texture conditions. The algorithm implementation for a Raspberry Pi Model B computer is discussed. For evaluation of the algorithm the computer was mounted on a Hercules mobile skied-steered robot equipped with a monocular camera. The evaluation was performed using a hardware-in-the-loop simulation and experiments with Hercules mobile robot. Also the algorithm was evaluated using KITTY Optical Flow 2015 dataset. The resulting endpoint error of the optical flow calculated with the developed algorithm was low enough for navigation of the robot along the desired trajectory.

GPU Lossless Hyperspectral Data Compression System

NASA Technical Reports Server (NTRS)

Aranki, Nazeeh I.; Keymeulen, Didier; Kiely, Aaron B.; Klimesh, Matthew A.

2014-01-01

Hyperspectral imaging systems onboard aircraft or spacecraft can acquire large amounts of data, putting a strain on limited downlink and storage resources. Onboard data compression can mitigate this problem but may require a system capable of a high throughput. In order to achieve a high throughput with a software compressor, a graphics processing unit (GPU) implementation of a compressor was developed targeting the current state-of-the-art GPUs from NVIDIA(R). The implementation is based on the fast lossless (FL) compression algorithm reported in "Fast Lossless Compression of Multispectral-Image Data" (NPO- 42517), NASA Tech Briefs, Vol. 30, No. 8 (August 2006), page 26, which operates on hyperspectral data and achieves excellent compression performance while having low complexity. The FL compressor uses an adaptive filtering method and achieves state-of-the-art performance in both compression effectiveness and low complexity. The new Consultative Committee for Space Data Systems (CCSDS) Standard for Lossless Multispectral & Hyperspectral image compression (CCSDS 123) is based on the FL compressor. The software makes use of the highly-parallel processing capability of GPUs to achieve a throughput at least six times higher than that of a software implementation running on a single-core CPU. This implementation provides a practical real-time solution for compression of data from airborne hyperspectral instruments.

A GPU-Accelerated Approach for Feature Tracking in Time-Varying Imagery Datasets.

PubMed

Peng, Chao; Sahani, Sandip; Rushing, John

2017-10-01

We propose a novel parallel connected component labeling (CCL) algorithm along with efficient out-of-core data management to detect and track feature regions of large time-varying imagery datasets. Our approach contributes to the big data field with parallel algorithms tailored for GPU architectures. We remove the data dependency between frames and achieve pixel-level parallelism. Due to the large size, the entire dataset cannot fit into cached memory. Frames have to be streamed through the memory hierarchy (disk to CPU main memory and then to GPU memory), partitioned, and processed as batches, where each batch is small enough to fit into the GPU. To reconnect the feature regions that are separated due to data partitioning, we present a novel batch merging algorithm to extract the region connection information across multiple batches in a parallel fashion. The information is organized in a memory-efficient structure and supports fast indexing on the GPU. Our experiment uses a commodity workstation equipped with a single GPU. The results show that our approach can efficiently process a weather dataset composed of terabytes of time-varying radar images. The advantages of our approach are demonstrated by comparing to the performance of an efficient CPU cluster implementation which is being used by the weather scientists.

Accelerating large-scale simulation of seismic wave propagation by multi-GPUs and three-dimensional domain decomposition

NASA Astrophysics Data System (ADS)

Okamoto, Taro; Takenaka, Hiroshi; Nakamura, Takeshi; Aoki, Takayuki

2010-12-01

We adopted the GPU (graphics processing unit) to accelerate the large-scale finite-difference simulation of seismic wave propagation. The simulation can benefit from the high-memory bandwidth of GPU because it is a "memory intensive" problem. In a single-GPU case we achieved a performance of about 56 GFlops, which was about 45-fold faster than that achieved by a single core of the host central processing unit (CPU). We confirmed that the optimized use of fast shared memory and registers were essential for performance. In the multi-GPU case with three-dimensional domain decomposition, the non-contiguous memory alignment in the ghost zones was found to impose quite long time in data transfer between GPU and the host node. This problem was solved by using contiguous memory buffers for ghost zones. We achieved a performance of about 2.2 TFlops by using 120 GPUs and 330 GB of total memory: nearly (or more than) 2200 cores of host CPUs would be required to achieve the same performance. The weak scaling was nearly proportional to the number of GPUs. We therefore conclude that GPU computing for large-scale simulation of seismic wave propagation is a promising approach as a faster simulation is possible with reduced computational resources compared to CPUs.

FastMag: Fast micromagnetic simulator for complex magnetic structures (invited)

NASA Astrophysics Data System (ADS)

Chang, R.; Li, S.; Lubarda, M. V.; Livshitz, B.; Lomakin, V.

2011-04-01

A fast micromagnetic simulator (FastMag) for general problems is presented. FastMag solves the Landau-Lifshitz-Gilbert equation and can handle multiscale problems with a high computational efficiency. The simulator derives its high performance from efficient methods for evaluating the effective field and from implementations on massively parallel graphics processing unit (GPU) architectures. FastMag discretizes the computational domain into tetrahedral elements and therefore is highly flexible for general problems. The magnetostatic field is computed via the superposition principle for both volume and surface parts of the computational domain. This is accomplished by implementing efficient quadrature rules and analytical integration for overlapping elements in which the integral kernel is singular. Thus, discretized superposition integrals are computed using a nonuniform grid interpolation method, which evaluates the field from N sources at N collocated observers in O(N) operations. This approach allows handling objects of arbitrary shape, allows easily calculating of the field outside the magnetized domains, does not require solving a linear system of equations, and requires little memory. FastMag is implemented on GPUs with ?> GPU-central processing unit speed-ups of 2 orders of magnitude. Simulations are shown of a large array of magnetic dots and a recording head fully discretized down to the exchange length, with over a hundred million tetrahedral elements on an inexpensive desktop computer.

Petascale turbulence simulation using a highly parallel fast multipole method on GPUs

NASA Astrophysics Data System (ADS)

Yokota, Rio; Barba, L. A.; Narumi, Tetsu; Yasuoka, Kenji

2013-03-01

This paper reports large-scale direct numerical simulations of homogeneous-isotropic fluid turbulence, achieving sustained performance of 1.08 petaflop/s on GPU hardware using single precision. The simulations use a vortex particle method to solve the Navier-Stokes equations, with a highly parallel fast multipole method (FMM) as numerical engine, and match the current record in mesh size for this application, a cube of 40963 computational points solved with a spectral method. The standard numerical approach used in this field is the pseudo-spectral method, relying on the FFT algorithm as the numerical engine. The particle-based simulations presented in this paper quantitatively match the kinetic energy spectrum obtained with a pseudo-spectral method, using a trusted code. In terms of parallel performance, weak scaling results show the FMM-based vortex method achieving 74% parallel efficiency on 4096 processes (one GPU per MPI process, 3 GPUs per node of the TSUBAME-2.0 system). The FFT-based spectral method is able to achieve just 14% parallel efficiency on the same number of MPI processes (using only CPU cores), due to the all-to-all communication pattern of the FFT algorithm. The calculation time for one time step was 108 s for the vortex method and 154 s for the spectral method, under these conditions. Computing with 69 billion particles, this work exceeds by an order of magnitude the largest vortex-method calculations to date.

General purpose molecular dynamics simulations fully implemented on graphics processing units

NASA Astrophysics Data System (ADS)

Anderson, Joshua A.; Lorenz, Chris D.; Travesset, A.

2008-05-01

Graphics processing units (GPUs), originally developed for rendering real-time effects in computer games, now provide unprecedented computational power for scientific applications. In this paper, we develop a general purpose molecular dynamics code that runs entirely on a single GPU. It is shown that our GPU implementation provides a performance equivalent to that of fast 30 processor core distributed memory cluster. Our results show that GPUs already provide an inexpensive alternative to such clusters and discuss implications for the future.

GPU-accelerated regularized iterative reconstruction for few-view cone beam CT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Matenine, Dmitri, E-mail: dmitri.matenine.1@ulaval.ca; Goussard, Yves, E-mail: yves.goussard@polymtl.ca; Després, Philippe, E-mail: philippe.despres@phy.ulaval.ca

2015-04-15

Purpose: The present work proposes an iterative reconstruction technique designed for x-ray transmission computed tomography (CT). The main objective is to provide a model-based solution to the cone-beam CT reconstruction problem, yielding accurate low-dose images via few-views acquisitions in clinically acceptable time frames. Methods: The proposed technique combines a modified ordered subsets convex (OSC) algorithm and the total variation minimization (TV) regularization technique and is called OSC-TV. The number of subsets of each OSC iteration follows a reduction pattern in order to ensure the best performance of the regularization method. Considering the high computational cost of the algorithm, it ismore » implemented on a graphics processing unit, using parallelization to accelerate computations. Results: The reconstructions were performed on computer-simulated as well as human pelvic cone-beam CT projection data and image quality was assessed. In terms of convergence and image quality, OSC-TV performs well in reconstruction of low-dose cone-beam CT data obtained via a few-view acquisition protocol. It compares favorably to the few-view TV-regularized projections onto convex sets (POCS-TV) algorithm. It also appears to be a viable alternative to full-dataset filtered backprojection. Execution times are of 1–2 min and are compatible with the typical clinical workflow for nonreal-time applications. Conclusions: Considering the image quality and execution times, this method may be useful for reconstruction of low-dose clinical acquisitions. It may be of particular benefit to patients who undergo multiple acquisitions by reducing the overall imaging radiation dose and associated risks.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Clemente, F; Perez, C

Purpose: Redundant treatment verifications in conformal and intensity-modulated radiation therapy techniques are traditionally performed with single point calculations. New solutions can replace these checks with 3D treatment plan verifications. This work describes a software tool (Mobius3D, Mobius Medical Systems) that uses a GPU-accelerated collapsed cone algorithm to perform 3D independent verifications of TPS calculations. Methods: Mobius3D comes with reference beam models for common linear accelerators. The system uses an independently developed collapsed cone algorithm updated with recent enhancements. 144 isotropically-spaced cones are used for each voxel for calculations. These complex calculations can be sped up by using GPUs. Mobius3D calculatemore » dose using DICOM information coming from TPS (CT, RT Struct, RT Plan RT Dose). DVH-metrics and 3D gamma tests can be used to compare both TPS and secondary calculations. 170 patients treated with all common techniques as 3DCFRT (including wedged), static and dynamic IMRT and VMAT have been successfully verified with this solution. Results: Calculation times are between 3–5 minutes for 3DCFRT treatments and 15–20 for most complex dMLC and VMAT plans. For all PTVs mean dose and 90% coverage differences are (1.12±0.97)% and (0.68±1.19)%, respectively. Mean dose discrepancies for all OARs is (0.64±1.00)%. 3D gamma (global, 3%/3 mm) analysis shows a mean passing rate of (97.8 ± 3.0)% for PTVs and (99.0±3.0)% for OARs. 3D gamma pasing rate for all voxels in CT has a mean value of (98.5±1.6)%. Conclusion: Mobius3D is a powerful tool to verify all modalities of radiation therapy treatments. Dose discrepancies calculated by this system are in good agreement with TPS. The use of reference beam data results in time savings and can be used to avoid the propagation of errors in original beam data into our QA system. GPU calculations permit enhanced collapsed cone calculations with reasonable calculation times.« less

Graphics Processing Unit-Accelerated Nonrigid Registration of MR Images to CT Images During CT-Guided Percutaneous Liver Tumor Ablations.

PubMed

Tokuda, Junichi; Plishker, William; Torabi, Meysam; Olubiyi, Olutayo I; Zaki, George; Tatli, Servet; Silverman, Stuart G; Shekher, Raj; Hata, Nobuhiko

2015-06-01

Accuracy and speed are essential for the intraprocedural nonrigid magnetic resonance (MR) to computed tomography (CT) image registration in the assessment of tumor margins during CT-guided liver tumor ablations. Although both accuracy and speed can be improved by limiting the registration to a region of interest (ROI), manual contouring of the ROI prolongs the registration process substantially. To achieve accurate and fast registration without the use of an ROI, we combined a nonrigid registration technique on the basis of volume subdivision with hardware acceleration using a graphics processing unit (GPU). We compared the registration accuracy and processing time of GPU-accelerated volume subdivision-based nonrigid registration technique to the conventional nonrigid B-spline registration technique. Fourteen image data sets of preprocedural MR and intraprocedural CT images for percutaneous CT-guided liver tumor ablations were obtained. Each set of images was registered using the GPU-accelerated volume subdivision technique and the B-spline technique. Manual contouring of ROI was used only for the B-spline technique. Registration accuracies (Dice similarity coefficient [DSC] and 95% Hausdorff distance [HD]) and total processing time including contouring of ROIs and computation were compared using a paired Student t test. Accuracies of the GPU-accelerated registrations and B-spline registrations, respectively, were 88.3 ± 3.7% versus 89.3 ± 4.9% (P = .41) for DSC and 13.1 ± 5.2 versus 11.4 ± 6.3 mm (P = .15) for HD. Total processing time of the GPU-accelerated registration and B-spline registration techniques was 88 ± 14 versus 557 ± 116 seconds (P < .000000002), respectively; there was no significant difference in computation time despite the difference in the complexity of the algorithms (P = .71). The GPU-accelerated volume subdivision technique was as accurate as the B-spline technique and required significantly less processing time. The GPU-accelerated volume subdivision technique may enable the implementation of nonrigid registration into routine clinical practice. Copyright © 2015 AUR. Published by Elsevier Inc. All rights reserved.

Accelerating a three-dimensional eco-hydrological cellular automaton on GPGPU with OpenCL

NASA Astrophysics Data System (ADS)

Senatore, Alfonso; D'Ambrosio, Donato; De Rango, Alessio; Rongo, Rocco; Spataro, William; Straface, Salvatore; Mendicino, Giuseppe

2016-10-01

This work presents an effective implementation of a numerical model for complete eco-hydrological Cellular Automata modeling on Graphical Processing Units (GPU) with OpenCL (Open Computing Language) for heterogeneous computation (i.e., on CPUs and/or GPUs). Different types of parallel implementations were carried out (e.g., use of fast local memory, loop unrolling, etc), showing increasing performance improvements in terms of speedup, adopting also some original optimizations strategies. Moreover, numerical analysis of results (i.e., comparison of CPU and GPU outcomes in terms of rounding errors) have proven to be satisfactory. Experiments were carried out on a workstation with two CPUs (Intel Xeon E5440 at 2.83GHz), one GPU AMD R9 280X and one GPU nVIDIA Tesla K20c. Results have been extremely positive, but further testing should be performed to assess the functionality of the adopted strategies on other complete models and their ability to fruitfully exploit parallel systems resources.

Real-Time Compressive Sensing MRI Reconstruction Using GPU Computing and Split Bregman Methods

PubMed Central

Smith, David S.; Gore, John C.; Yankeelov, Thomas E.; Welch, E. Brian

2012-01-01

Compressive sensing (CS) has been shown to enable dramatic acceleration of MRI acquisition in some applications. Being an iterative reconstruction technique, CS MRI reconstructions can be more time-consuming than traditional inverse Fourier reconstruction. We have accelerated our CS MRI reconstruction by factors of up to 27 by using a split Bregman solver combined with a graphics processing unit (GPU) computing platform. The increases in speed we find are similar to those we measure for matrix multiplication on this platform, suggesting that the split Bregman methods parallelize efficiently. We demonstrate that the combination of the rapid convergence of the split Bregman algorithm and the massively parallel strategy of GPU computing can enable real-time CS reconstruction of even acquisition data matrices of dimension 40962 or more, depending on available GPU VRAM. Reconstruction of two-dimensional data matrices of dimension 10242 and smaller took ~0.3 s or less, showing that this platform also provides very fast iterative reconstruction for small-to-moderate size images. PMID:22481908

Real-Time Compressive Sensing MRI Reconstruction Using GPU Computing and Split Bregman Methods.

PubMed

Smith, David S; Gore, John C; Yankeelov, Thomas E; Welch, E Brian

2012-01-01

Compressive sensing (CS) has been shown to enable dramatic acceleration of MRI acquisition in some applications. Being an iterative reconstruction technique, CS MRI reconstructions can be more time-consuming than traditional inverse Fourier reconstruction. We have accelerated our CS MRI reconstruction by factors of up to 27 by using a split Bregman solver combined with a graphics processing unit (GPU) computing platform. The increases in speed we find are similar to those we measure for matrix multiplication on this platform, suggesting that the split Bregman methods parallelize efficiently. We demonstrate that the combination of the rapid convergence of the split Bregman algorithm and the massively parallel strategy of GPU computing can enable real-time CS reconstruction of even acquisition data matrices of dimension 4096(2) or more, depending on available GPU VRAM. Reconstruction of two-dimensional data matrices of dimension 1024(2) and smaller took ~0.3 s or less, showing that this platform also provides very fast iterative reconstruction for small-to-moderate size images.

SU-E-T-36: A GPU-Accelerated Monte-Carlo Dose Calculation Platform and Its Application Toward Validating a ViewRay Beam Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Y; Mazur, T; Green, O

Purpose: To build a fast, accurate and easily-deployable research platform for Monte-Carlo dose calculations. We port the dose calculation engine PENELOPE to C++, and accelerate calculations using GPU acceleration. Simulations of a Co-60 beam model provided by ViewRay demonstrate the capabilities of the platform. Methods: We built software that incorporates a beam model interface, CT-phantom model, GPU-accelerated PENELOPE engine, and GUI front-end. We rewrote the PENELOPE kernel in C++ (from Fortran) and accelerated the code on a GPU. We seamlessly integrated a Co-60 beam model (obtained from ViewRay) into our platform. Simulations of various field sizes and SSDs using amore » homogeneous water phantom generated PDDs, dose profiles, and output factors that were compared to experiment data. Results: With GPU acceleration using a dated graphics card (Nvidia Tesla C2050), a highly accurate simulation – including 100*100*100 grid, 3×3×3 mm3 voxels, <1% uncertainty, and 4.2×4.2 cm2 field size – runs 24 times faster (20 minutes versus 8 hours) than when parallelizing on 8 threads across a new CPU (Intel i7-4770). Simulated PDDs, profiles and output ratios for the commercial system agree well with experiment data measured using radiographic film or ionization chamber. Based on our analysis, this beam model is precise enough for general applications. Conclusions: Using a beam model for a Co-60 system provided by ViewRay, we evaluate a dose calculation platform that we developed. Comparison to measurements demonstrates the promise of our software for use as a research platform for dose calculations, with applications including quality assurance and treatment plan verification.« less

SU-D-206-02: Evaluation of Partial Storage of the System Matrix for Cone Beam Computed Tomography Using a GPU Platform

DOE Office of Scientific and Technical Information (OSTI.GOV)

Matenine, D; Cote, G; Mascolo-Fortin, J

2016-06-15

Purpose: Iterative reconstruction algorithms in computed tomography (CT) require a fast method for computing the intersections between the photons’ trajectories and the object, also called ray-tracing or system matrix computation. This work evaluates different ways to store the system matrix, aiming to reconstruct dense image grids in reasonable time. Methods: We propose an optimized implementation of the Siddon’s algorithm using graphics processing units (GPUs) with a novel data storage scheme. The algorithm computes a part of the system matrix on demand, typically, for one projection angle. The proposed method was enhanced with accelerating options: storage of larger subsets of themore » system matrix, systematic reuse of data via geometric symmetries, an arithmetic-rich parallel code and code configuration via machine learning. It was tested on geometries mimicking a cone beam CT acquisition of a human head. To realistically assess the execution time, the ray-tracing routines were integrated into a regularized Poisson-based reconstruction algorithm. The proposed scheme was also compared to a different approach, where the system matrix is fully pre-computed and loaded at reconstruction time. Results: Fast ray-tracing of realistic acquisition geometries, which often lack spatial symmetry properties, was enabled via the proposed method. Ray-tracing interleaved with projection and backprojection operations required significant additional time. In most cases, ray-tracing was shown to use about 66 % of the total reconstruction time. In absolute terms, tracing times varied from 3.6 s to 7.5 min, depending on the problem size. The presence of geometrical symmetries allowed for non-negligible ray-tracing and reconstruction time reduction. Arithmetic-rich parallel code and machine learning permitted a modest reconstruction time reduction, in the order of 1 %. Conclusion: Partial system matrix storage permitted the reconstruction of higher 3D image grid sizes and larger projection datasets at the cost of additional time, when compared to the fully pre-computed approach. This work was supported in part by the Fonds de recherche du Quebec - Nature et technologies (FRQ-NT). The authors acknowledge partial support by the CREATE Medical Physics Research Training Network grant of the Natural Sciences and Engineering Research Council of Canada (Grant No. 432290).« less

Cone Beam X-Ray Luminescence Tomography Imaging Based on KA-FEM Method for Small Animals.

PubMed

Chen, Dongmei; Meng, Fanzhen; Zhao, Fengjun; Xu, Cao

2016-01-01

Cone beam X-ray luminescence tomography can realize fast X-ray luminescence tomography imaging with relatively low scanning time compared with narrow beam X-ray luminescence tomography. However, cone beam X-ray luminescence tomography suffers from an ill-posed reconstruction problem. First, the feasibility of experiments with different penetration and multispectra in small animal has been tested using nanophosphor material. Then, the hybrid reconstruction algorithm with KA-FEM method has been applied in cone beam X-ray luminescence tomography for small animals to overcome the ill-posed reconstruction problem, whose advantage and property have been demonstrated in fluorescence tomography imaging. The in vivo mouse experiment proved the feasibility of the proposed method.

Fast data preprocessing with Graphics Processing Units for inverse problem solving in light-scattering measurements

NASA Astrophysics Data System (ADS)

Derkachov, G.; Jakubczyk, T.; Jakubczyk, D.; Archer, J.; Woźniak, M.

2017-07-01

Utilising Compute Unified Device Architecture (CUDA) platform for Graphics Processing Units (GPUs) enables significant reduction of computation time at a moderate cost, by means of parallel computing. In the paper [Jakubczyk et al., Opto-Electron. Rev., 2016] we reported using GPU for Mie scattering inverse problem solving (up to 800-fold speed-up). Here we report the development of two subroutines utilising GPU at data preprocessing stages for the inversion procedure: (i) A subroutine, based on ray tracing, for finding spherical aberration correction function. (ii) A subroutine performing the conversion of an image to a 1D distribution of light intensity versus azimuth angle (i.e. scattering diagram), fed from a movie-reading CPU subroutine running in parallel. All subroutines are incorporated in PikeReader application, which we make available on GitHub repository. PikeReader returns a sequence of intensity distributions versus a common azimuth angle vector, corresponding to the recorded movie. We obtained an overall ∼ 400 -fold speed-up of calculations at data preprocessing stages using CUDA codes running on GPU in comparison to single thread MATLAB-only code running on CPU.

Ultrafast superpixel segmentation of large 3D medical datasets

NASA Astrophysics Data System (ADS)

Leblond, Antoine; Kauffmann, Claude

2016-03-01

Even with recent hardware improvements, superpixel segmentation of large 3D medical images at interactive speed (<500 ms) remains a challenge. We will describe methods to achieve such performances using a GPU based hybrid framework implementing wavefront propagation and cellular automata resolution. Tasks will be scheduled in blocks (work units) using a wavefront propagation strategy, therefore allowing sparse scheduling. Because work units has been designed as spatially cohesive, the fast Thread Group Shared Memory can be used and reused through a Gauss-Seidel like acceleration. The work unit partitioning scheme will however vary on odd- and even-numbered iterations to reduce convergence barriers. Synchronization will be ensured by an 8-step 3D variant of the traditional Red Black Ordering scheme. An attack model and early termination will also be described and implemented as additional acceleration techniques. Using our hybrid framework and typical operating parameters, we were able to compute the superpixels of a high-resolution 512x512x512 aortic angioCT scan in 283 ms using a AMD R9 290X GPU. We achieved a 22.3X speed-up factor compared to the published reference GPU implementation.

A GPU-Accelerated 3-D Coupled Subsample Estimation Algorithm for Volumetric Breast Strain Elastography.

PubMed

Peng, Bo; Wang, Yuqi; Hall, Timothy J; Jiang, Jingfeng

2017-04-01

Our primary objective of this paper was to extend a previously published 2-D coupled subsample tracking algorithm for 3-D speckle tracking in the framework of ultrasound breast strain elastography. In order to overcome heavy computational cost, we investigated the use of a graphic processing unit (GPU) to accelerate the 3-D coupled subsample speckle tracking method. The performance of the proposed GPU implementation was tested using a tissue-mimicking phantom and in vivo breast ultrasound data. The performance of this 3-D subsample tracking algorithm was compared with the conventional 3-D quadratic subsample estimation algorithm. On the basis of these evaluations, we concluded that the GPU implementation of this 3-D subsample estimation algorithm can provide high-quality strain data (i.e., high correlation between the predeformation and the motion-compensated postdeformation radio frequency echo data and high contrast-to-noise ratio strain images), as compared with the conventional 3-D quadratic subsample algorithm. Using the GPU implementation of the 3-D speckle tracking algorithm, volumetric strain data can be achieved relatively fast (approximately 20 s per volume [2.5 cm ×2.5 cm ×2.5 cm]).

4D cone-beam CT reconstruction using multi-organ meshes for sliding motion modeling

NASA Astrophysics Data System (ADS)

Zhong, Zichun; Gu, Xuejun; Mao, Weihua; Wang, Jing

2016-02-01

A simultaneous motion estimation and image reconstruction (SMEIR) strategy was proposed for 4D cone-beam CT (4D-CBCT) reconstruction and showed excellent results in both phantom and lung cancer patient studies. In the original SMEIR algorithm, the deformation vector field (DVF) was defined on voxel grid and estimated by enforcing a global smoothness regularization term on the motion fields. The objective of this work is to improve the computation efficiency and motion estimation accuracy of SMEIR for 4D-CBCT through developing a multi-organ meshing model. Feature-based adaptive meshes were generated to reduce the number of unknowns in the DVF estimation and accurately capture the organ shapes and motion. Additionally, the discontinuity in the motion fields between different organs during respiration was explicitly considered in the multi-organ mesh model. This will help with the accurate visualization and motion estimation of the tumor on the organ boundaries in 4D-CBCT. To further improve the computational efficiency, a GPU-based parallel implementation was designed. The performance of the proposed algorithm was evaluated on a synthetic sliding motion phantom, a 4D NCAT phantom, and four lung cancer patients. The proposed multi-organ mesh based strategy outperformed the conventional Feldkamp-Davis-Kress, iterative total variation minimization, original SMEIR and single meshing method based on both qualitative and quantitative evaluations.

4D cone-beam CT reconstruction using multi-organ meshes for sliding motion modeling.

PubMed

Zhong, Zichun; Gu, Xuejun; Mao, Weihua; Wang, Jing

2016-02-07

A simultaneous motion estimation and image reconstruction (SMEIR) strategy was proposed for 4D cone-beam CT (4D-CBCT) reconstruction and showed excellent results in both phantom and lung cancer patient studies. In the original SMEIR algorithm, the deformation vector field (DVF) was defined on voxel grid and estimated by enforcing a global smoothness regularization term on the motion fields. The objective of this work is to improve the computation efficiency and motion estimation accuracy of SMEIR for 4D-CBCT through developing a multi-organ meshing model. Feature-based adaptive meshes were generated to reduce the number of unknowns in the DVF estimation and accurately capture the organ shapes and motion. Additionally, the discontinuity in the motion fields between different organs during respiration was explicitly considered in the multi-organ mesh model. This will help with the accurate visualization and motion estimation of the tumor on the organ boundaries in 4D-CBCT. To further improve the computational efficiency, a GPU-based parallel implementation was designed. The performance of the proposed algorithm was evaluated on a synthetic sliding motion phantom, a 4D NCAT phantom, and four lung cancer patients. The proposed multi-organ mesh based strategy outperformed the conventional Feldkamp-Davis-Kress, iterative total variation minimization, original SMEIR and single meshing method based on both qualitative and quantitative evaluations.

4D cone-beam CT reconstruction using multi-organ meshes for sliding motion modeling

PubMed Central

Zhong, Zichun; Gu, Xuejun; Mao, Weihua; Wang, Jing

2016-01-01

A simultaneous motion estimation and image reconstruction (SMEIR) strategy was proposed for 4D cone-beam CT (4D-CBCT) reconstruction and showed excellent results in both phantom and lung cancer patient studies. In the original SMEIR algorithm, the deformation vector field (DVF) was defined on voxel grid and estimated by enforcing a global smoothness regularization term on the motion fields. The objective of this work is to improve the computation efficiency and motion estimation accuracy of SMEIR for 4D-CBCT through developing a multi-organ meshing model. Feature-based adaptive meshes were generated to reduce the number of unknowns in the DVF estimation and accurately capture the organ shapes and motion. Additionally, the discontinuity in the motion fields between different organs during respiration was explicitly considered in the multi-organ mesh model. This will help with the accurate visualization and motion estimation of the tumor on the organ boundaries in 4D-CBCT. To further improve the computational efficiency, a GPU-based parallel implementation was designed. The performance of the proposed algorithm was evaluated on a synthetic sliding motion phantom, a 4D NCAT phantom, and four lung cancer patients. The proposed multi-organ mesh based strategy outperformed the conventional Feldkamp–Davis–Kress, iterative total variation minimization, original SMEIR and single meshing method based on both qualitative and quantitative evaluations. PMID:26758496

Proton emission from cone-in-shell fast-ignition experiments at Omega

NASA Astrophysics Data System (ADS)

Sinenian, N.; Theobald, W.; Frenje, J. A.; Stoeckl, C.; Séguin, F. H.; Li, C. K.; Petrasso, R. D.; Stephens, R. B.

2012-11-01

Measurements of energetic protons from cone-in-shell fast-igniton implosions at Omega have been conducted. In these experiments, charged-particle spectrometers were used to measure a significant population (>1013) of energetic protons (7.5 MeV max.), indicating the presence of strong electric fields. These energetic protons, observed in directions both transverse and forward relative to the direction of the short-pulse laser beam, have been used to study aspects of coupling efficiency of the petawatt fast-ignitior beam. Approximately 5% of the laser energy coupled to hot electrons was lost to fast ions. Forward going protons were less energetic and showed no dependence on laser intensity or whether the cone tip was intact when the short-pulse laser was fired. Maximum energies of protons emitted transverse to the cone-in-shell target scale with incident on-target laser intensity (2-6×1018W-cm-2), as described by the ponderomotive scaling (∝I1/2). It is shown that these protons are accelerated from the entire cone, rather than from the cone tip alone. These protons were used to estimate the lower limit on the hot-electron temperature, which was found to be hotter than the ponderomotive scaling by factors of 2-3.

FPGA Implementation of the Coupled Filtering Method and the Affine Warping Method.

PubMed

Zhang, Chen; Liang, Tianzhu; Mok, Philip K T; Yu, Weichuan

2017-07-01

In ultrasound image analysis, the speckle tracking methods are widely applied to study the elasticity of body tissue. However, "feature-motion decorrelation" still remains as a challenge for the speckle tracking methods. Recently, a coupled filtering method and an affine warping method were proposed to accurately estimate strain values, when the tissue deformation is large. The major drawback of these methods is the high computational complexity. Even the graphics processing unit (GPU)-based program requires a long time to finish the analysis. In this paper, we propose field-programmable gate array (FPGA)-based implementations of both methods for further acceleration. The capability of FPGAs on handling different image processing components in these methods is discussed. A fast and memory-saving image warping approach is proposed. The algorithms are reformulated to build a highly efficient pipeline on FPGA. The final implementations on a Xilinx Virtex-7 FPGA are at least 13 times faster than the GPU implementation on the NVIDIA graphic card (GeForce GTX 580).

VLITE-Fast: A Real-time, 350 MHz Commensal VLA Survey for Fast Transients

NASA Astrophysics Data System (ADS)

Kerr, Matthew; Ray, Paul S.; Kassim, Namir E.; Clarke, Tracy; Deneva, Julia; Polisensky, Emil

2018-01-01

The VLITE (VLA Low Band Ionosphere and Transient Experiment; http://vlite.nrao.edu) program operates commensally during all Very Large Array observations, collecting data from 320 to 384 MHz. Recently expanded to include 16 antennas, the large field of view and huge time on sky offer good coverage of the transient, low-frequency sky. We describe the VLITE-Fast system, a GPU-based signal processor capable of detecting short (<1s) transients in real time and triggering recording of baseband voltage for offline imaging. In the case of Fast Radio Bursts, this offers the opportunity for discovering host galaxies of non-repeating FRBs, and in the case of single pulses, the identification of pulsar positions for dedicated follow-up. We describe the observing system, techniques for mitigating interference, and initial results from searches for FRBs.

Vanishing points detection using combination of fast Hough transform and deep learning

NASA Astrophysics Data System (ADS)

Sheshkus, Alexander; Ingacheva, Anastasia; Nikolaev, Dmitry

2018-04-01

In this paper we propose a novel method for vanishing points detection based on convolutional neural network (CNN) approach and fast Hough transform algorithm. We show how to determine fast Hough transform neural network layer and how to use it in order to increase usability of the neural network approach to the vanishing point detection task. Our algorithm includes CNN with consequence of convolutional and fast Hough transform layers. We are building estimator for distribution of possible vanishing points in the image. This distribution can be used to find candidates of vanishing point. We provide experimental results from tests of suggested method using images collected from videos of road trips. Our approach shows stable result on test images with different projective distortions and noise. Described approach can be effectively implemented for mobile GPU and CPU.

GPU and APU computations of Finite Time Lyapunov Exponent fields

NASA Astrophysics Data System (ADS)

Conti, Christian; Rossinelli, Diego; Koumoutsakos, Petros

2012-03-01

We present GPU and APU accelerated computations of Finite-Time Lyapunov Exponent (FTLE) fields. The calculation of FTLEs is a computationally intensive process, as in order to obtain the sharp ridges associated with the Lagrangian Coherent Structures an extensive resampling of the flow field is required. The computational performance of this resampling is limited by the memory bandwidth of the underlying computer architecture. The present technique harnesses data-parallel execution of many-core architectures and relies on fast and accurate evaluations of moment conserving functions for the mesh to particle interpolations. We demonstrate how the computation of FTLEs can be efficiently performed on a GPU and on an APU through OpenCL and we report over one order of magnitude improvements over multi-threaded executions in FTLE computations of bluff body flows.

Acceleration of fluoro-CT reconstruction for a mobile C-Arm on GPU and FPGA hardware: a simulation study

NASA Astrophysics Data System (ADS)

Xue, Xinwei; Cheryauka, Arvi; Tubbs, David

2006-03-01

CT imaging in interventional and minimally-invasive surgery requires high-performance computing solutions that meet operational room demands, healthcare business requirements, and the constraints of a mobile C-arm system. The computational requirements of clinical procedures using CT-like data are increasing rapidly, mainly due to the need for rapid access to medical imagery during critical surgical procedures. The highly parallel nature of Radon transform and CT algorithms enables embedded computing solutions utilizing a parallel processing architecture to realize a significant gain of computational intensity with comparable hardware and program coding/testing expenses. In this paper, using a sample 2D and 3D CT problem, we explore the programming challenges and the potential benefits of embedded computing using commodity hardware components. The accuracy and performance results obtained on three computational platforms: a single CPU, a single GPU, and a solution based on FPGA technology have been analyzed. We have shown that hardware-accelerated CT image reconstruction can be achieved with similar levels of noise and clarity of feature when compared to program execution on a CPU, but gaining a performance increase at one or more orders of magnitude faster. 3D cone-beam or helical CT reconstruction and a variety of volumetric image processing applications will benefit from similar accelerations.

Opticks : GPU Optical Photon Simulation for Particle Physics using NVIDIA® OptiX™

NASA Astrophysics Data System (ADS)

C, Blyth Simon

2017-10-01

Opticks is an open source project that integrates the NVIDIA OptiX GPU ray tracing engine with Geant4 toolkit based simulations. Massive parallelism brings drastic performance improvements with optical photon simulation speedup expected to exceed 1000 times Geant4 when using workstation GPUs. Optical photon simulation time becomes effectively zero compared to the rest of the simulation. Optical photons from scintillation and Cherenkov processes are allocated, generated and propagated entirely on the GPU, minimizing transfer overheads and allowing CPU memory usage to be restricted to optical photons that hit photomultiplier tubes or other photon detectors. Collecting hits into standard Geant4 hit collections then allows the rest of the simulation chain to proceed unmodified. Optical physics processes of scattering, absorption, scintillator reemission and boundary processes are implemented in CUDA OptiX programs based on the Geant4 implementations. Wavelength dependent material and surface properties as well as inverse cumulative distribution functions for reemission are interleaved into GPU textures providing fast interpolated property lookup or wavelength generation. Geometry is provided to OptiX in the form of CUDA programs that return bounding boxes for each primitive and ray geometry intersection positions. Some critical parts of the geometry such as photomultiplier tubes have been implemented analytically with the remainder being tessellated. OptiX handles the creation and application of a choice of acceleration structures such as boundary volume hierarchies and the transparent use of multiple GPUs. OptiX supports interoperation with OpenGL and CUDA Thrust that has enabled unprecedented visualisations of photon propagations to be developed using OpenGL geometry shaders to provide interactive time scrubbing and CUDA Thrust photon indexing to enable interactive history selection.

GPU-accelerated iterative reconstruction for limited-data tomography in CBCT systems.

PubMed

de Molina, Claudia; Serrano, Estefania; Garcia-Blas, Javier; Carretero, Jesus; Desco, Manuel; Abella, Monica

2018-05-15

Standard cone-beam computed tomography (CBCT) involves the acquisition of at least 360 projections rotating through 360 degrees. Nevertheless, there are cases in which only a few projections can be taken in a limited angular span, such as during surgery, where rotation of the source-detector pair is limited to less than 180 degrees. Reconstruction of limited data with the conventional method proposed by Feldkamp, Davis and Kress (FDK) results in severe artifacts. Iterative methods may compensate for the lack of data by including additional prior information, although they imply a high computational burden and memory consumption. We present an accelerated implementation of an iterative method for CBCT following the Split Bregman formulation, which reduces computational time through GPU-accelerated kernels. The implementation enables the reconstruction of large volumes (>1024 3 pixels) using partitioning strategies in forward- and back-projection operations. We evaluated the algorithm on small-animal data for different scenarios with different numbers of projections, angular span, and projection size. Reconstruction time varied linearly with the number of projections and quadratically with projection size but remained almost unchanged with angular span. Forward- and back-projection operations represent 60% of the total computational burden. Efficient implementation using parallel processing and large-memory management strategies together with GPU kernels enables the use of advanced reconstruction approaches which are needed in limited-data scenarios. Our GPU implementation showed a significant time reduction (up to 48 ×) compared to a CPU-only implementation, resulting in a total reconstruction time from several hours to few minutes.

WE-G-18A-01: JUNIOR INVESTIGATOR WINNER - Low-Dose C-Arm Cone-Beam CT with Model-Based Image Reconstruction for High-Quality Guidance of Neurosurgical Intervention

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, A; Stayman, J; Otake, Y

Purpose: To address the challenges of image quality, radiation dose, and reconstruction speed in intraoperative cone-beam CT (CBCT) for neurosurgery by combining model-based image reconstruction (MBIR) with accelerated algorithmic and computational methods. Methods: Preclinical studies involved a mobile C-arm for CBCT imaging of two anthropomorphic head phantoms that included simulated imaging targets (ventricles, soft-tissue structures/bleeds) and neurosurgical procedures (deep brain stimulation (DBS) electrode insertion) for assessment of image quality. The penalized likelihood (PL) framework was used for MBIR, incorporating a statistical model with image regularization via an edgepreserving penalty. To accelerate PL reconstruction, the ordered-subset, separable quadratic surrogates (OS-SQS) algorithmmore » was modified to incorporate Nesterov's method and implemented on a multi-GPU system. A fair comparison of image quality between PL and conventional filtered backprojection (FBP) was performed by selecting reconstruction parameters that provided matched low-contrast spatial resolution. Results: CBCT images of the head phantoms demonstrated that PL reconstruction improved image quality (∼28% higher CNR) even at half the radiation dose (3.3 mGy) compared to FBP. A combination of Nesterov's method and fast projectors yielded a PL reconstruction run-time of 251 sec (cf., 5729 sec for OS-SQS, 13 sec for FBP). Insertion of a DBS electrode resulted in severe metal artifact streaks in FBP reconstructions, whereas PL was intrinsically robust against metal artifact. The combination of noise and artifact was reduced from 32.2 HU in FBP to 9.5 HU in PL, thereby providing better assessment of device placement and potential complications. Conclusion: The methods can be applied to intraoperative CBCT for guidance and verification of neurosurgical procedures (DBS electrode insertion, biopsy, tumor resection) and detection of complications (intracranial hemorrhage). Significant improvement in image quality, dose reduction, and reconstruction time of ∼4 min will enable practical deployment of low-dose C-arm CBCT within the operating room. AAPM Research Seed Funding (2013-2014); NIH Fellowship F32EB017571; Siemens Healthcare (XP Division)« less

gpuPOM: a GPU-based Princeton Ocean Model

NASA Astrophysics Data System (ADS)

Xu, S.; Huang, X.; Zhang, Y.; Fu, H.; Oey, L.-Y.; Xu, F.; Yang, G.

2014-11-01

Rapid advances in the performance of the graphics processing unit (GPU) have made the GPU a compelling solution for a series of scientific applications. However, most existing GPU acceleration works for climate models are doing partial code porting for certain hot spots, and can only achieve limited speedup for the entire model. In this work, we take the mpiPOM (a parallel version of the Princeton Ocean Model) as our starting point, design and implement a GPU-based Princeton Ocean Model. By carefully considering the architectural features of the state-of-the-art GPU devices, we rewrite the full mpiPOM model from the original Fortran version into a new Compute Unified Device Architecture C (CUDA-C) version. We take several accelerating methods to further improve the performance of gpuPOM, including optimizing memory access in a single GPU, overlapping communication and boundary operations among multiple GPUs, and overlapping input/output (I/O) between the hybrid Central Processing Unit (CPU) and the GPU. Our experimental results indicate that the performance of the gpuPOM on a workstation containing 4 GPUs is comparable to a powerful cluster with 408 CPU cores and it reduces the energy consumption by 6.8 times.

Fast online Monte Carlo-based IMRT planning for the MRI linear accelerator

NASA Astrophysics Data System (ADS)

Bol, G. H.; Hissoiny, S.; Lagendijk, J. J. W.; Raaymakers, B. W.

2012-03-01

The MRI accelerator, a combination of a 6 MV linear accelerator with a 1.5 T MRI, facilitates continuous patient anatomy updates regarding translations, rotations and deformations of targets and organs at risk. Accounting for these demands high speed, online intensity-modulated radiotherapy (IMRT) re-optimization. In this paper, a fast IMRT optimization system is described which combines a GPU-based Monte Carlo dose calculation engine for online beamlet generation and a fast inverse dose optimization algorithm. Tightly conformal IMRT plans are generated for four phantom cases and two clinical cases (cervix and kidney) in the presence of the magnetic fields of 0 and 1.5 T. We show that for the presented cases the beamlet generation and optimization routines are fast enough for online IMRT planning. Furthermore, there is no influence of the magnetic field on plan quality and complexity, and equal optimization constraints at 0 and 1.5 T lead to almost identical dose distributions.

High-throughput Bayesian Network Learning using Heterogeneous Multicore Computers

PubMed Central

Linderman, Michael D.; Athalye, Vivek; Meng, Teresa H.; Asadi, Narges Bani; Bruggner, Robert; Nolan, Garry P.

2017-01-01

Aberrant intracellular signaling plays an important role in many diseases. The causal structure of signal transduction networks can be modeled as Bayesian Networks (BNs), and computationally learned from experimental data. However, learning the structure of Bayesian Networks (BNs) is an NP-hard problem that, even with fast heuristics, is too time consuming for large, clinically important networks (20–50 nodes). In this paper, we present a novel graphics processing unit (GPU)-accelerated implementation of a Monte Carlo Markov Chain-based algorithm for learning BNs that is up to 7.5-fold faster than current general-purpose processor (GPP)-based implementations. The GPU-based implementation is just one of several implementations within the larger application, each optimized for a different input or machine configuration. We describe the methodology we use to build an extensible application, assembled from these variants, that can target a broad range of heterogeneous systems, e.g., GPUs, multicore GPPs. Specifically we show how we use the Merge programming model to efficiently integrate, test and intelligently select among the different potential implementations. PMID:28819655

SU-E-T-423: Fast Photon Convolution Calculation with a 3D-Ideal Kernel On the GPU

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moriya, S; Sato, M; Tachibana, H

Purpose: The calculation time is a trade-off for improving the accuracy of convolution dose calculation with fine calculation spacing of the KERMA kernel. We investigated to accelerate the convolution calculation using an ideal kernel on the Graphic Processing Units (GPU). Methods: The calculation was performed on the AMD graphics hardware of Dual FirePro D700 and our algorithm was implemented using the Aparapi that convert Java bytecode to OpenCL. The process of dose calculation was separated with the TERMA and KERMA steps. The dose deposited at the coordinate (x, y, z) was determined in the process. In the dose calculation runningmore » on the central processing unit (CPU) of Intel Xeon E5, the calculation loops were performed for all calculation points. On the GPU computation, all of the calculation processes for the points were sent to the GPU and the multi-thread computation was done. In this study, the dose calculation was performed in a water equivalent homogeneous phantom with 150{sup 3} voxels (2 mm calculation grid) and the calculation speed on the GPU to that on the CPU and the accuracy of PDD were compared. Results: The calculation time for the GPU and the CPU were 3.3 sec and 4.4 hour, respectively. The calculation speed for the GPU was 4800 times faster than that for the CPU. The PDD curve for the GPU was perfectly matched to that for the CPU. Conclusion: The convolution calculation with the ideal kernel on the GPU was clinically acceptable for time and may be more accurate in an inhomogeneous region. Intensity modulated arc therapy needs dose calculations for different gantry angles at many control points. Thus, it would be more practical that the kernel uses a coarse spacing technique if the calculation is faster while keeping the similar accuracy to a current treatment planning system.« less

GPU accelerated cell-based adaptive mesh refinement on unstructured quadrilateral grid

NASA Astrophysics Data System (ADS)

Luo, Xisheng; Wang, Luying; Ran, Wei; Qin, Fenghua

2016-10-01

A GPU accelerated inviscid flow solver is developed on an unstructured quadrilateral grid in the present work. For the first time, the cell-based adaptive mesh refinement (AMR) is fully implemented on GPU for the unstructured quadrilateral grid, which greatly reduces the frequency of data exchange between GPU and CPU. Specifically, the AMR is processed with atomic operations to parallelize list operations, and null memory recycling is realized to improve the efficiency of memory utilization. It is found that results obtained by GPUs agree very well with the exact or experimental results in literature. An acceleration ratio of 4 is obtained between the parallel code running on the old GPU GT9800 and the serial code running on E3-1230 V2. With the optimization of configuring a larger L1 cache and adopting Shared Memory based atomic operations on the newer GPU C2050, an acceleration ratio of 20 is achieved. The parallelized cell-based AMR processes have achieved 2x speedup on GT9800 and 18x on Tesla C2050, which demonstrates that parallel running of the cell-based AMR method on GPU is feasible and efficient. Our results also indicate that the new development of GPU architecture benefits the fluid dynamics computing significantly.

GPU-Based Point Cloud Superpositioning for Structural Comparisons of Protein Binding Sites.

PubMed

Leinweber, Matthias; Fober, Thomas; Freisleben, Bernd

2018-01-01

In this paper, we present a novel approach to solve the labeled point cloud superpositioning problem for performing structural comparisons of protein binding sites. The solution is based on a parallel evolution strategy that operates on large populations and runs on GPU hardware. The proposed evolution strategy reduces the likelihood of getting stuck in a local optimum of the multimodal real-valued optimization problem represented by labeled point cloud superpositioning. The performance of the GPU-based parallel evolution strategy is compared to a previously proposed CPU-based sequential approach for labeled point cloud superpositioning, indicating that the GPU-based parallel evolution strategy leads to qualitatively better results and significantly shorter runtimes, with speed improvements of up to a factor of 1,500 for large populations. Binary classification tests based on the ATP, NADH, and FAD protein subsets of CavBase, a database containing putative binding sites, show average classification rate improvements from about 92 percent (CPU) to 96 percent (GPU). Further experiments indicate that the proposed GPU-based labeled point cloud superpositioning approach can be superior to traditional protein comparison approaches based on sequence alignments.

Spectra of laser generated relativistic electrons using cone-wire targets

NASA Astrophysics Data System (ADS)

Sawada, Hiroshi

2012-10-01

We report on the characterization of the in situ energy spectrum of fast electrons generated by ultra-intense (I˜10^19 W cm-2) short pulse (τ˜0.7 and 10 ps) laser-plasma interactions using the TITAN and OMEGA EP lasers. That in situ spectrum is a key component of ignition efficiency for the Fast Ignition (FI) Inertial Confinement Fusion (ICF) concept. It is challenging to model and, until now, has resisted direct experimental characterization; other techniques have very large error bars or measure the modified spectrum of escaped electrons. This technique also gives an indication of the forward coupling efficiency of the laser to fast electrons. This information is derived from the measurement of Cu Kα x-rays emitted from a 1.5 mm long Cu wire attached to the tip of Au or Al cone targets. Fast electrons, generated in the cone, transport through the cone tip with a fraction of coupling to the wire. Electrons in the wire excite fluorescence measured by a monochromatic imager and an absolutely calibrated HOPG spectrometer. An implicit hybrid-PIC code, LSP, is applied to deduce electron parameters from the Kα measurements. Experiments on the TITAN laser with Au cones attached to wires show an increase in pre-pulse energy from 17 to 1000 mJ, decreases the fast electron fraction entering the wire from 8.4% to 2.5%. On OMEGA EP with Al cones attached to wires, total Kα yield, normalized to laser energy, drops ˜30% for laser pulse length increasing from 1 to 10 ps, indicative of a saturation mechanism. For Au cones, Kα yields were 50% of that measured for Al cones indicating a strong material dependence. In all cases, the spatial distribution can only be fit with a two-temperature electron energy distribution, the relative fractions depending on prepulse level. These results are being used to develop an optimum cone design for integrated FI experiments. This work was performed under the auspices of the USDOE by LLNL under Contract DE-AC52-07NA27344 and DE-FG-02-05ER54834.

An Analysis Model for Water Cone Subsidence in Bottom Water Drive Reservoirs

NASA Astrophysics Data System (ADS)

Wang, Jianjun; Xu, Hui; Wu, Shucheng; Yang, Chao; Kong, lingxiao; Zeng, Baoquan; Xu, Haixia; Qu, Tailai

2017-12-01

Water coning in bottom water drive reservoirs, which will result in earlier water breakthrough, rapid increase in water cut and low recovery level, has drawn tremendous attention in petroleum engineering field. As one simple and effective method to inhibit bottom water coning, shut-in coning control is usually preferred in oilfield to control the water cone and furthermore to enhance economic performance. However, most of the water coning researchers just have been done on investigation of the coning behavior as it grows up, the reported studies for water cone subsidence are very scarce. The goal of this work is to present an analytical model for water cone subsidence to analyze the subsidence of water cone when the well shut in. Based on Dupuit critical oil production rate formula, an analytical model is developed to estimate the initial water cone shape at the point of critical drawdown. Then, with the initial water cone shape equation, we propose an analysis model for water cone subsidence in bottom water reservoir reservoirs. Model analysis and several sensitivity studies are conducted. This work presents accurate and fast analytical model to perform the water cone subsidence in bottom water drive reservoirs. To consider the recent interests in development of bottom drive reservoirs, our approach provides a promising technique for better understanding the subsidence of water cone.

TU-AB-202-05: GPU-Based 4D Deformable Image Registration Using Adaptive Tetrahedral Mesh Modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhong, Z; Zhuang, L; Gu, X

Purpose: Deformable image registration (DIR) has been employed today as an automated and effective segmentation method to transfer tumor or organ contours from the planning image to daily images, instead of manual segmentation. However, the computational time and accuracy of current DIR approaches are still insufficient for online adaptive radiation therapy (ART), which requires real-time and high-quality image segmentation, especially in a large datasets of 4D-CT images. The objective of this work is to propose a new DIR algorithm, with fast computational speed and high accuracy, by using adaptive feature-based tetrahedral meshing and GPU-based parallelization. Methods: The first step ismore » to generate the adaptive tetrahedral mesh based on the image features of a reference phase of 4D-CT, so that the deformation can be well captured and accurately diffused from the mesh vertices to voxels of the image volume. Subsequently, the deformation vector fields (DVF) and other phases of 4D-CT can be obtained by matching each phase of the target 4D-CT images with the corresponding deformed reference phase. The proposed 4D DIR method is implemented on GPU, resulting in significantly increasing the computational efficiency due to its parallel computing ability. Results: A 4D NCAT digital phantom was used to test the efficiency and accuracy of our method. Both the image and DVF results show that the fine structures and shapes of lung are well preserved, and the tumor position is well captured, i.e., 3D distance error is 1.14 mm. Compared to the previous voxel-based CPU implementation of DIR, such as demons, the proposed method is about 160x faster for registering a 10-phase 4D-CT with a phase dimension of 256×256×150. Conclusion: The proposed 4D DIR method uses feature-based mesh and GPU-based parallelism, which demonstrates the capability to compute both high-quality image and motion results, with significant improvement on the computational speed.« less

A real-time spike sorting method based on the embedded GPU.

PubMed

Zelan Yang; Kedi Xu; Xiang Tian; Shaomin Zhang; Xiaoxiang Zheng

2017-07-01

Microelectrode arrays with hundreds of channels have been widely used to acquire neuron population signals in neuroscience studies. Online spike sorting is becoming one of the most important challenges for high-throughput neural signal acquisition systems. Graphic processing unit (GPU) with high parallel computing capability might provide an alternative solution for increasing real-time computational demands on spike sorting. This study reported a method of real-time spike sorting through computing unified device architecture (CUDA) which was implemented on an embedded GPU (NVIDIA JETSON Tegra K1, TK1). The sorting approach is based on the principal component analysis (PCA) and K-means. By analyzing the parallelism of each process, the method was further optimized in the thread memory model of GPU. Our results showed that the GPU-based classifier on TK1 is 37.92 times faster than the MATLAB-based classifier on PC while their accuracies were the same with each other. The high-performance computing features of embedded GPU demonstrated in our studies suggested that the embedded GPU provide a promising platform for the real-time neural signal processing.

The Expansion and Radial Speeds of Coronal Mass Ejections

NASA Astrophysics Data System (ADS)

Gopalswamy, N.; Dal Lago, A.; Yashiro, S.; Akiyama, S.

We show the relation between radial (V_{rad}) and expansion (V_{exp}) speeds of coronal mass ejections (CMEs) depends on the CME width. As CME width increases, {V_{rad}/V_{exp}} decreases from a value >1 to <1. For widths approaching 180°, the ratio approaches 0 if the cone has a flat base, while it approaches 0.5 if the base has a bulge (ice cream cone). The speed difference between the limb and disk halos and the spherical expansion of super fast CMEs can be explained by the width dependence.

A GPU-accelerated 3D Coupled Sub-sample Estimation Algorithm for Volumetric Breast Strain Elastography

PubMed Central

Peng, Bo; Wang, Yuqi; Hall, Timothy J; Jiang, Jingfeng

2017-01-01

Our primary objective of this work was to extend a previously published 2D coupled sub-sample tracking algorithm for 3D speckle tracking in the framework of ultrasound breast strain elastography. In order to overcome heavy computational cost, we investigated the use of a graphic processing unit (GPU) to accelerate the 3D coupled sub-sample speckle tracking method. The performance of the proposed GPU implementation was tested using a tissue-mimicking (TM) phantom and in vivo breast ultrasound data. The performance of this 3D sub-sample tracking algorithm was compared with the conventional 3D quadratic sub-sample estimation algorithm. On the basis of these evaluations, we concluded that the GPU implementation of this 3D sub-sample estimation algorithm can provide high-quality strain data (i.e. high correlation between the pre- and the motion-compensated post-deformation RF echo data and high contrast-to-noise ratio strain images), as compared to the conventional 3D quadratic sub-sample algorithm. Using the GPU implementation of the 3D speckle tracking algorithm, volumetric strain data can be achieved relatively fast (approximately 20 seconds per volume [2.5 cm × 2.5 cm × 2.5 cm]). PMID:28166493

GPU.proton.DOCK: Genuine Protein Ultrafast proton equilibria consistent DOCKing.

PubMed

Kantardjiev, Alexander A

2011-07-01

GPU.proton.DOCK (Genuine Protein Ultrafast proton equilibria consistent DOCKing) is a state of the art service for in silico prediction of protein-protein interactions via rigorous and ultrafast docking code. It is unique in providing stringent account of electrostatic interactions self-consistency and proton equilibria mutual effects of docking partners. GPU.proton.DOCK is the first server offering such a crucial supplement to protein docking algorithms--a step toward more reliable and high accuracy docking results. The code (especially the Fast Fourier Transform bottleneck and electrostatic fields computation) is parallelized to run on a GPU supercomputer. The high performance will be of use for large-scale structural bioinformatics and systems biology projects, thus bridging physics of the interactions with analysis of molecular networks. We propose workflows for exploring in silico charge mutagenesis effects. Special emphasis is given to the interface-intuitive and user-friendly. The input is comprised of the atomic coordinate files in PDB format. The advanced user is provided with a special input section for addition of non-polypeptide charges, extra ionogenic groups with intrinsic pK(a) values or fixed ions. The output is comprised of docked complexes in PDB format as well as interactive visualization in a molecular viewer. GPU.proton.DOCK server can be accessed at http://gpudock.orgchm.bas.bg/.

catsHTM: A Tool for Fast Accessing and Cross-matching Large Astronomical Catalogs

NASA Astrophysics Data System (ADS)

Soumagnac, Maayane T.; Ofek, Eran O.

2018-07-01

Fast access to large catalogs is required for some astronomical applications. Here we introduce the catsHTM tool, consisting of several large catalogs reformatted into HDF5-based file format, which can be downloaded and used locally. To allow fast access, the catalogs are partitioned into hierarchical triangular meshes and stored in HDF5 files. Several tools are provided to perform efficient cone searches at resolutions spanning from a few arc-seconds to degrees, within a few milliseconds time. The first released version includes the following catalogs (by alphabetical order): 2MASS, 2MASS extended sources, AKARI, APASS, Cosmos, DECaLS/DR5, FIRST, GAIA/DR1, GAIA/DR2, GALEX/DR6Plus7, HSC/v2, IPHAS/DR2, NED redshifts, NVSS, Pan-STARRS1/DR1, PTF photometric catalog, ROSAT faint source, SDSS sources, SDSS/DR14 spectroscopy, SkyMapper, Spitzer/SAGE, Spitzer/IRAC galactic center, UCAC4, UKIDSS/DR10, VST/ATLAS/DR3, VST/KiDS/DR3, WISE and XMM. We provide Python code that allows to perform cone searches, as well as MATLAB code for performing cone searches, catalog cross-matching, general searches, as well as load and create these catalogs.

Real-time Graphics Processing Unit Based Fourier Domain Optical Coherence Tomography and Surgical Applications

NASA Astrophysics Data System (ADS)

Zhang, Kang

2011-12-01

In this dissertation, real-time Fourier domain optical coherence tomography (FD-OCT) capable of multi-dimensional micrometer-resolution imaging targeted specifically for microsurgical intervention applications was developed and studied. As a part of this work several ultra-high speed real-time FD-OCT imaging and sensing systems were proposed and developed. A real-time 4D (3D+time) OCT system platform using the graphics processing unit (GPU) to accelerate OCT signal processing, the imaging reconstruction, visualization, and volume rendering was developed. Several GPU based algorithms such as non-uniform fast Fourier transform (NUFFT), numerical dispersion compensation, and multi-GPU implementation were developed to improve the impulse response, SNR roll-off and stability of the system. Full-range complex-conjugate-free FD-OCT was also implemented on the GPU architecture to achieve doubled image range and improved SNR. These technologies overcome the imaging reconstruction and visualization bottlenecks widely exist in current ultra-high speed FD-OCT systems and open the way to interventional OCT imaging for applications in guided microsurgery. A hand-held common-path optical coherence tomography (CP-OCT) distance-sensor based microsurgical tool was developed and validated. Through real-time signal processing, edge detection and feed-back control, the tool was shown to be capable of track target surface and compensate motion. The micro-incision test using a phantom was performed using a CP-OCT-sensor integrated hand-held tool, which showed an incision error less than +/-5 microns, comparing to >100 microns error by free-hand incision. The CP-OCT distance sensor has also been utilized to enhance the accuracy and safety of optical nerve stimulation. Finally, several experiments were conducted to validate the system for surgical applications. One of them involved 4D OCT guided micro-manipulation using a phantom. Multiple volume renderings of one 3D data set were performed with different view angles to allow accurate monitoring of the micro-manipulation, and the user to clearly monitor tool-to-target spatial relation in real-time. The system was also validated by imaging multiple biological samples, such as human fingerprint, human cadaver head and small animals. Compared to conventional surgical microscopes, GPU-based real-time FD-OCT can provide the surgeons with a real-time comprehensive spatial view of the microsurgical region and accurate depth perception.

SU-E-J-91: FFT Based Medical Image Registration Using a Graphics Processing Unit (GPU).

PubMed

Luce, J; Hoggarth, M; Lin, J; Block, A; Roeske, J

2012-06-01

To evaluate the efficiency gains obtained from using a Graphics Processing Unit (GPU) to perform a Fourier Transform (FT) based image registration. Fourier-based image registration involves obtaining the FT of the component images, and analyzing them in Fourier space to determine the translations and rotations of one image set relative to another. An important property of FT registration is that by enlarging the images (adding additional pixels), one can obtain translations and rotations with sub-pixel resolution. The expense, however, is an increased computational time. GPUs may decrease the computational time associated with FT image registration by taking advantage of their parallel architecture to perform matrix computations much more efficiently than a Central Processor Unit (CPU). In order to evaluate the computational gains produced by a GPU, images with known translational shifts were utilized. A program was written in the Interactive Data Language (IDL; Exelis, Boulder, CO) to performCPU-based calculations. Subsequently, the program was modified using GPU bindings (Tech-X, Boulder, CO) to perform GPU-based computation on the same system. Multiple image sizes were used, ranging from 256×256 to 2304×2304. The time required to complete the full algorithm by the CPU and GPU were benchmarked and the speed increase was defined as the ratio of the CPU-to-GPU computational time. The ratio of the CPU-to- GPU time was greater than 1.0 for all images, which indicates the GPU is performing the algorithm faster than the CPU. The smallest improvement, a 1.21 ratio, was found with the smallest image size of 256×256, and the largest speedup, a 4.25 ratio, was observed with the largest image size of 2304×2304. GPU programming resulted in a significant decrease in computational time associated with a FT image registration algorithm. The inclusion of the GPU may provide near real-time, sub-pixel registration capability. © 2012 American Association of Physicists in Medicine.

SU-E-T-58: A Novel Monte Carlo Photon Transport Simulation Scheme and Its Application in Cone Beam CT Projection Simulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xu, Y; Southern Medical University, Guangzhou; Tian, Z

Purpose: Monte Carlo (MC) simulation is an important tool to solve radiotherapy and medical imaging problems. Low computational efficiency hinders its wide applications. Conventionally, MC is performed in a particle-by -particle fashion. The lack of control on particle trajectory is a main cause of low efficiency in some applications. Take cone beam CT (CBCT) projection simulation as an example, significant amount of computations were wasted on transporting photons that do not reach the detector. To solve this problem, we propose an innovative MC simulation scheme with a path-by-path sampling method. Methods: Consider a photon path starting at the x-ray source.more » After going through a set of interactions, it ends at the detector. In the proposed scheme, we sampled an entire photon path each time. Metropolis-Hasting algorithm was employed to accept/reject a sampled path based on a calculated acceptance probability, in order to maintain correct relative probabilities among different paths, which are governed by photon transport physics. We developed a package gMMC on GPU with this new scheme implemented. The performance of gMMC was tested in a sample problem of CBCT projection simulation for a homogeneous object. The results were compared to those obtained using gMCDRR, a GPU-based MC tool with the conventional particle-by-particle simulation scheme. Results: Calculated scattered photon signals in gMMC agreed with those from gMCDRR with a relative difference of 3%. It took 3.1 hr. for gMCDRR to simulate 7.8e11 photons and 246.5 sec for gMMC to simulate 1.4e10 paths. Under this setting, both results attained the same ∼2% statistical uncertainty. Hence, a speed-up factor of ∼45.3 was achieved by this new path-by-path simulation scheme, where all the computations were spent on those photons contributing to the detector signal. Conclusion: We innovatively proposed a novel path-by-path simulation scheme that enabled a significant efficiency enhancement for MC particle transport simulations.« less

Employing multi-GPU power for molecular dynamics simulation: an extension of GALAMOST

NASA Astrophysics Data System (ADS)

Zhu, You-Liang; Pan, Deng; Li, Zhan-Wei; Liu, Hong; Qian, Hu-Jun; Zhao, Yang; Lu, Zhong-Yuan; Sun, Zhao-Yan

2018-04-01

We describe the algorithm of employing multi-GPU power on the basis of Message Passing Interface (MPI) domain decomposition in a molecular dynamics code, GALAMOST, which is designed for the coarse-grained simulation of soft matters. The code of multi-GPU version is developed based on our previous single-GPU version. In multi-GPU runs, one GPU takes charge of one domain and runs single-GPU code path. The communication between neighbouring domains takes a similar algorithm of CPU-based code of LAMMPS, but is optimised specifically for GPUs. We employ a memory-saving design which can enlarge maximum system size at the same device condition. An optimisation algorithm is employed to prolong the update period of neighbour list. We demonstrate good performance of multi-GPU runs on the simulation of Lennard-Jones liquid, dissipative particle dynamics liquid, polymer and nanoparticle composite, and two-patch particles on workstation. A good scaling of many nodes on cluster for two-patch particles is presented.

Accelerating population balance-Monte Carlo simulation for coagulation dynamics from the Markov jump model, stochastic algorithm and GPU parallel computing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xu, Zuwei; Zhao, Haibo, E-mail: klinsmannzhb@163.com; Zheng, Chuguang

2015-01-15

This paper proposes a comprehensive framework for accelerating population balance-Monte Carlo (PBMC) simulation of particle coagulation dynamics. By combining Markov jump model, weighted majorant kernel and GPU (graphics processing unit) parallel computing, a significant gain in computational efficiency is achieved. The Markov jump model constructs a coagulation-rule matrix of differentially-weighted simulation particles, so as to capture the time evolution of particle size distribution with low statistical noise over the full size range and as far as possible to reduce the number of time loopings. Here three coagulation rules are highlighted and it is found that constructing appropriate coagulation rule providesmore » a route to attain the compromise between accuracy and cost of PBMC methods. Further, in order to avoid double looping over all simulation particles when considering the two-particle events (typically, particle coagulation), the weighted majorant kernel is introduced to estimate the maximum coagulation rates being used for acceptance–rejection processes by single-looping over all particles, and meanwhile the mean time-step of coagulation event is estimated by summing the coagulation kernels of rejected and accepted particle pairs. The computational load of these fast differentially-weighted PBMC simulations (based on the Markov jump model) is reduced greatly to be proportional to the number of simulation particles in a zero-dimensional system (single cell). Finally, for a spatially inhomogeneous multi-dimensional (multi-cell) simulation, the proposed fast PBMC is performed in each cell, and multiple cells are parallel processed by multi-cores on a GPU that can implement the massively threaded data-parallel tasks to obtain remarkable speedup ratio (comparing with CPU computation, the speedup ratio of GPU parallel computing is as high as 200 in a case of 100 cells with 10 000 simulation particles per cell). These accelerating approaches of PBMC are demonstrated in a physically realistic Brownian coagulation case. The computational accuracy is validated with benchmark solution of discrete-sectional method. The simulation results show that the comprehensive approach can attain very favorable improvement in cost without sacrificing computational accuracy.« less

WE-AB-303-09: Rapid Projection Computations for On-Board Digital Tomosynthesis in Radiation Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Iliopoulos, AS; Sun, X; Pitsianis, N

2015-06-15

Purpose: To facilitate fast and accurate iterative volumetric image reconstruction from limited-angle on-board projections. Methods: Intrafraction motion hinders the clinical applicability of modern radiotherapy techniques, such as lung stereotactic body radiation therapy (SBRT). The LIVE system may impact clinical practice by recovering volumetric information via Digital Tomosynthesis (DTS), thus entailing low time and radiation dose for image acquisition during treatment. The DTS is estimated as a deformation of prior CT via iterative registration with on-board images; this shifts the challenge to the computational domain, owing largely to repeated projection computations across iterations. We address this issue by composing efficient digitalmore » projection operators from their constituent parts. This allows us to separate the static (projection geometry) and dynamic (volume/image data) parts of projection operations by means of pre-computations, enabling fast on-board processing, while also relaxing constraints on underlying numerical models (e.g. regridding interpolation kernels). Further decoupling the projectors into simpler ones ensures the incurred memory overhead remains low, within the capacity of a single GPU. These operators depend only on the treatment plan and may be reused across iterations and patients. The dynamic processing load is kept to a minimum and maps well to the GPU computational model. Results: We have integrated efficient, pre-computable modules for volumetric ray-casting and FDK-based back-projection with the LIVE processing pipeline. Our results show a 60x acceleration of the DTS computations, compared to the previous version, using a single GPU; presently, reconstruction is attained within a couple of minutes. The present implementation allows for significant flexibility in terms of the numerical and operational projection model; we are investigating the benefit of further optimizations and accurate digital projection sub-kernels. Conclusion: Composable projection operators constitute a versatile research tool which can greatly accelerate iterative registration algorithms and may be conducive to the clinical applicability of LIVE. National Institutes of Health Grant No. R01-CA184173; GPU donation by NVIDIA Corporation.« less

TH-AB-BRA-07: PENELOPE-Based GPU-Accelerated Dose Calculation System Applied to MRI-Guided Radiation Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Y; Mazur, T; Green, O

Purpose: The clinical commissioning of IMRT subject to a magnetic field is challenging. The purpose of this work is to develop a GPU-accelerated Monte Carlo dose calculation platform based on PENELOPE and then use the platform to validate a vendor-provided MRIdian head model toward quality assurance of clinical IMRT treatment plans subject to a 0.35 T magnetic field. Methods: We first translated PENELOPE from FORTRAN to C++ and validated that the translation produced equivalent results. Then we adapted the C++ code to CUDA in a workflow optimized for GPU architecture. We expanded upon the original code to include voxelized transportmore » boosted by Woodcock tracking, faster electron/positron propagation in a magnetic field, and several features that make gPENELOPE highly user-friendly. Moreover, we incorporated the vendor-provided MRIdian head model into the code. We performed a set of experimental measurements on MRIdian to examine the accuracy of both the head model and gPENELOPE, and then applied gPENELOPE toward independent validation of patient doses calculated by MRIdian’s KMC. Results: We achieve an average acceleration factor of 152 compared to the original single-thread FORTRAN implementation with the original accuracy preserved. For 16 treatment plans including stomach (4), lung (2), liver (3), adrenal gland (2), pancreas (2), spleen (1), mediastinum (1) and breast (1), the MRIdian dose calculation engine agrees with gPENELOPE with a mean gamma passing rate of 99.1% ± 0.6% (2%/2 mm). Conclusions: We developed a Monte Carlo simulation platform based on a GPU-accelerated version of PENELOPE. We validated that both the vendor provided head model and fast Monte Carlo engine used by the MRIdian system are accurate in modeling radiation transport in a patient using 2%/2 mm gamma criteria. Future applications of this platform will include dose validation and accumulation, IMRT optimization, and dosimetry system modeling for next generation MR-IGRT systems.« less

Time-resolved compression of a capsule with a cone to high density for fast-ignition laser fusion

DOE PAGES

Theobald, W.; Solodov, A. A.; Stoeckl, C.; ...

2014-12-12

The advent of high-intensity lasers enables us to recreate and study the behaviour of matter under the extreme densities and pressures that exist in many astrophysical objects. It may also enable us to develop a power source based on laser-driven nuclear fusion. Achieving such conditions usually requires a target that is highly uniform and spherically symmetric. Here we show that it is possible to generate high densities in a so-called fast-ignition target that consists of a thin shell whose spherical symmetry is interrupted by the inclusion of a metal cone. Using picosecond-time-resolved X-ray radiography, we show that we can achievemore » areal densities in excess of 300 mg cm -2 with a nanosecond-duration compression pulse -- the highest areal density ever reported for a cone-in-shell target. Such densities are high enough to stop MeV electrons, which is necessary for igniting the fuel with a subsequent picosecond pulse focused into the resulting plasma.« less

Time-resolved compression of a capsule with a cone to high density for fast-ignition laser fusion.

PubMed

Theobald, W; Solodov, A A; Stoeckl, C; Anderson, K S; Beg, F N; Epstein, R; Fiksel, G; Giraldez, E M; Glebov, V Yu; Habara, H; Ivancic, S; Jarrott, L C; Marshall, F J; McKiernan, G; McLean, H S; Mileham, C; Nilson, P M; Patel, P K; Pérez, F; Sangster, T C; Santos, J J; Sawada, H; Shvydky, A; Stephens, R B; Wei, M S

2014-12-12

The advent of high-intensity lasers enables us to recreate and study the behaviour of matter under the extreme densities and pressures that exist in many astrophysical objects. It may also enable us to develop a power source based on laser-driven nuclear fusion. Achieving such conditions usually requires a target that is highly uniform and spherically symmetric. Here we show that it is possible to generate high densities in a so-called fast-ignition target that consists of a thin shell whose spherical symmetry is interrupted by the inclusion of a metal cone. Using picosecond-time-resolved X-ray radiography, we show that we can achieve areal densities in excess of 300 mg cm(-2) with a nanosecond-duration compression pulse--the highest areal density ever reported for a cone-in-shell target. Such densities are high enough to stop MeV electrons, which is necessary for igniting the fuel with a subsequent picosecond pulse focused into the resulting plasma.

GPU-Accelerated Voxelwise Hepatic Perfusion Quantification

PubMed Central

Wang, H; Cao, Y

2012-01-01

Voxelwise quantification of hepatic perfusion parameters from dynamic contrast enhanced (DCE) imaging greatly contributes to assessment of liver function in response to radiation therapy. However, the efficiency of the estimation of hepatic perfusion parameters voxel-by-voxel in the whole liver using a dual-input single-compartment model requires substantial improvement for routine clinical applications. In this paper, we utilize the parallel computation power of a graphics processing unit (GPU) to accelerate the computation, while maintaining the same accuracy as the conventional method. Using CUDA-GPU, the hepatic perfusion computations over multiple voxels are run across the GPU blocks concurrently but independently. At each voxel, non-linear least squares fitting the time series of the liver DCE data to the compartmental model is distributed to multiple threads in a block, and the computations of different time points are performed simultaneously and synchronically. An efficient fast Fourier transform in a block is also developed for the convolution computation in the model. The GPU computations of the voxel-by-voxel hepatic perfusion images are compared with ones by the CPU using the simulated DCE data and the experimental DCE MR images from patients. The computation speed is improved by 30 times using a NVIDIA Tesla C2050 GPU compared to a 2.67 GHz Intel Xeon CPU processor. To obtain liver perfusion maps with 626400 voxels in a patient’s liver, it takes 0.9 min with the GPU-accelerated voxelwise computation, compared to 110 min with the CPU, while both methods result in perfusion parameters differences less than 10−6. The method will be useful for generating liver perfusion images in clinical settings. PMID:22892645

OCTGRAV: Sparse Octree Gravitational N-body Code on Graphics Processing Units

NASA Astrophysics Data System (ADS)

Gaburov, Evghenii; Bédorf, Jeroen; Portegies Zwart, Simon

2010-10-01

Octgrav is a very fast tree-code which runs on massively parallel Graphical Processing Units (GPU) with NVIDIA CUDA architecture. The algorithms are based on parallel-scan and sort methods. The tree-construction and calculation of multipole moments is carried out on the host CPU, while the force calculation which consists of tree walks and evaluation of interaction list is carried out on the GPU. In this way, a sustained performance of about 100GFLOP/s and data transfer rates of about 50GB/s is achieved. It takes about a second to compute forces on a million particles with an opening angle of heta approx 0.5. To test the performance and feasibility, we implemented the algorithms in CUDA in the form of a gravitational tree-code which completely runs on the GPU. The tree construction and traverse algorithms are portable to many-core devices which have support for CUDA or OpenCL programming languages. The gravitational tree-code outperforms tuned CPU code during the tree-construction and shows a performance improvement of more than a factor 20 overall, resulting in a processing rate of more than 2.8 million particles per second. The code has a convenient user interface and is freely available for use.

Efficient Irregular Wavefront Propagation Algorithms on Hybrid CPU-GPU Machines

PubMed Central

Teodoro, George; Pan, Tony; Kurc, Tahsin; Kong, Jun; Cooper, Lee; Saltz, Joel

2013-01-01

We address the problem of efficient execution of a computation pattern, referred to here as the irregular wavefront propagation pattern (IWPP), on hybrid systems with multiple CPUs and GPUs. The IWPP is common in several image processing operations. In the IWPP, data elements in the wavefront propagate waves to their neighboring elements on a grid if a propagation condition is satisfied. Elements receiving the propagated waves become part of the wavefront. This pattern results in irregular data accesses and computations. We develop and evaluate strategies for efficient computation and propagation of wavefronts using a multi-level queue structure. This queue structure improves the utilization of fast memories in a GPU and reduces synchronization overheads. We also develop a tile-based parallelization strategy to support execution on multiple CPUs and GPUs. We evaluate our approaches on a state-of-the-art GPU accelerated machine (equipped with 3 GPUs and 2 multicore CPUs) using the IWPP implementations of two widely used image processing operations: morphological reconstruction and euclidean distance transform. Our results show significant performance improvements on GPUs. The use of multiple CPUs and GPUs cooperatively attains speedups of 50× and 85× with respect to single core CPU executions for morphological reconstruction and euclidean distance transform, respectively. PMID:23908562

Fast Realistic MRI Simulations Based on Generalized Multi-Pool Exchange Tissue Model.

PubMed

Liu, Fang; Velikina, Julia V; Block, Walter F; Kijowski, Richard; Samsonov, Alexey A

2017-02-01

We present MRiLab, a new comprehensive simulator for large-scale realistic MRI simulations on a regular PC equipped with a modern graphical processing unit (GPU). MRiLab combines realistic tissue modeling with numerical virtualization of an MRI system and scanning experiment to enable assessment of a broad range of MRI approaches including advanced quantitative MRI methods inferring microstructure on a sub-voxel level. A flexible representation of tissue microstructure is achieved in MRiLab by employing the generalized tissue model with multiple exchanging water and macromolecular proton pools rather than a system of independent proton isochromats typically used in previous simulators. The computational power needed for simulation of the biologically relevant tissue models in large 3D objects is gained using parallelized execution on GPU. Three simulated and one actual MRI experiments were performed to demonstrate the ability of the new simulator to accommodate a wide variety of voxel composition scenarios and demonstrate detrimental effects of simplified treatment of tissue micro-organization adapted in previous simulators. GPU execution allowed  ∼ 200× improvement in computational speed over standard CPU. As a cross-platform, open-source, extensible environment for customizing virtual MRI experiments, MRiLab streamlines the development of new MRI methods, especially those aiming to infer quantitatively tissue composition and microstructure.

Fast Realistic MRI Simulations Based on Generalized Multi-Pool Exchange Tissue Model

PubMed Central

Velikina, Julia V.; Block, Walter F.; Kijowski, Richard; Samsonov, Alexey A.

2017-01-01

We present MRiLab, a new comprehensive simulator for large-scale realistic MRI simulations on a regular PC equipped with a modern graphical processing unit (GPU). MRiLab combines realistic tissue modeling with numerical virtualization of an MRI system and scanning experiment to enable assessment of a broad range of MRI approaches including advanced quantitative MRI methods inferring microstructure on a sub-voxel level. A flexibl representation of tissue microstructure is achieved in MRiLab by employing the generalized tissue model with multiple exchanging water and macromolecular proton pools rather than a system of independent proton isochromats typically used in previous simulators. The computational power needed for simulation of the biologically relevant tissue models in large 3D objects is gained using parallelized execution on GPU. Three simulated and one actual MRI experiments were performed to demonstrate the ability of the new simulator to accommodate a wide variety of voxel composition scenarios and demonstrate detrimental effects of simplifie treatment of tissue micro-organization adapted in previous simulators. GPU execution allowed ∼200× improvement in computational speed over standard CPU. As a cross-platform, open-source, extensible environment for customizing virtual MRI experiments, MRiLab streamlines the development of new MRI methods, especially those aiming to infer quantitatively tissue composition and microstructure. PMID:28113746

Acoustic reverse-time migration using GPU card and POSIX thread based on the adaptive optimal finite-difference scheme and the hybrid absorbing boundary condition

NASA Astrophysics Data System (ADS)

Cai, Xiaohui; Liu, Yang; Ren, Zhiming

2018-06-01

Reverse-time migration (RTM) is a powerful tool for imaging geologically complex structures such as steep-dip and subsalt. However, its implementation is quite computationally expensive. Recently, as a low-cost solution, the graphic processing unit (GPU) was introduced to improve the efficiency of RTM. In the paper, we develop three ameliorative strategies to implement RTM on GPU card. First, given the high accuracy and efficiency of the adaptive optimal finite-difference (FD) method based on least squares (LS) on central processing unit (CPU), we study the optimal LS-based FD method on GPU. Second, we develop the CPU-based hybrid absorbing boundary condition (ABC) to the GPU-based one by addressing two issues of the former when introduced to GPU card: time-consuming and chaotic threads. Third, for large-scale data, the combinatorial strategy for optimal checkpointing and efficient boundary storage is introduced for the trade-off between memory and recomputation. To save the time of communication between host and disk, the portable operating system interface (POSIX) thread is utilized to create the other CPU core at the checkpoints. Applications of the three strategies on GPU with the compute unified device architecture (CUDA) programming language in RTM demonstrate their efficiency and validity.

Validation of GPU based TomoTherapy dose calculation engine.

PubMed

Chen, Quan; Lu, Weiguo; Chen, Yu; Chen, Mingli; Henderson, Douglas; Sterpin, Edmond

2012-04-01

The graphic processing unit (GPU) based TomoTherapy convolution/superposition(C/S) dose engine (GPU dose engine) achieves a dramatic performance improvement over the traditional CPU-cluster based TomoTherapy dose engine (CPU dose engine). Besides the architecture difference between the GPU and CPU, there are several algorithm changes from the CPU dose engine to the GPU dose engine. These changes made the GPU dose slightly different from the CPU-cluster dose. In order for the commercial release of the GPU dose engine, its accuracy has to be validated. Thirty eight TomoTherapy phantom plans and 19 patient plans were calculated with both dose engines to evaluate the equivalency between the two dose engines. Gamma indices (Γ) were used for the equivalency evaluation. The GPU dose was further verified with the absolute point dose measurement with ion chamber and film measurements for phantom plans. Monte Carlo calculation was used as a reference for both dose engines in the accuracy evaluation in heterogeneous phantom and actual patients. The GPU dose engine showed excellent agreement with the current CPU dose engine. The majority of cases had over 99.99% of voxels with Γ(1%, 1 mm) < 1. The worst case observed in the phantom had 0.22% voxels violating the criterion. In patient cases, the worst percentage of voxels violating the criterion was 0.57%. For absolute point dose verification, all cases agreed with measurement to within ±3% with average error magnitude within 1%. All cases passed the acceptance criterion that more than 95% of the pixels have Γ(3%, 3 mm) < 1 in film measurement, and the average passing pixel percentage is 98.5%-99%. The GPU dose engine also showed similar degree of accuracy in heterogeneous media as the current TomoTherapy dose engine. It is verified and validated that the ultrafast TomoTherapy GPU dose engine can safely replace the existing TomoTherapy cluster based dose engine without degradation in dose accuracy.

Performance of Point and Range Queries for In-memory Databases using Radix Trees on GPUs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Alam, Maksudul; Yoginath, Srikanth B; Perumalla, Kalyan S

In in-memory database systems augmented by hardware accelerators, accelerating the index searching operations can greatly increase the runtime performance of database queries. Recently, adaptive radix trees (ART) have been shown to provide very fast index search implementation on the CPU. Here, we focus on an accelerator-based implementation of ART. We present a detailed performance study of our GPU-based adaptive radix tree (GRT) implementation over a variety of key distributions, synthetic benchmarks, and actual keys from music and book data sets. The performance is also compared with other index-searching schemes on the GPU. GRT on modern GPUs achieves some of themore » highest rates of index searches reported in the literature. For point queries, a throughput of up to 106 million and 130 million lookups per second is achieved for sparse and dense keys, respectively. For range queries, GRT yields 600 million and 1000 million lookups per second for sparse and dense keys, respectively, on a large dataset of 64 million 32-bit keys.« less

Rapid processing of PET list-mode data for efficient uncertainty estimation and data analysis

NASA Astrophysics Data System (ADS)

Markiewicz, P. J.; Thielemans, K.; Schott, J. M.; Atkinson, D.; Arridge, S. R.; Hutton, B. F.; Ourselin, S.

2016-07-01

In this technical note we propose a rapid and scalable software solution for the processing of PET list-mode data, which allows the efficient integration of list mode data processing into the workflow of image reconstruction and analysis. All processing is performed on the graphics processing unit (GPU), making use of streamed and concurrent kernel execution together with data transfers between disk and CPU memory as well as CPU and GPU memory. This approach leads to fast generation of multiple bootstrap realisations, and when combined with fast image reconstruction and analysis, it enables assessment of uncertainties of any image statistic and of any component of the image generation process (e.g. random correction, image processing) within reasonable time frames (e.g. within five minutes per realisation). This is of particular value when handling complex chains of image generation and processing. The software outputs the following: (1) estimate of expected random event data for noise reduction; (2) dynamic prompt and random sinograms of span-1 and span-11 and (3) variance estimates based on multiple bootstrap realisations of (1) and (2) assuming reasonable count levels for acceptable accuracy. In addition, the software produces statistics and visualisations for immediate quality control and crude motion detection, such as: (1) count rate curves; (2) centre of mass plots of the radiodistribution for motion detection; (3) video of dynamic projection views for fast visual list-mode skimming and inspection; (4) full normalisation factor sinograms. To demonstrate the software, we present an example of the above processing for fast uncertainty estimation of regional SUVR (standard uptake value ratio) calculation for a single PET scan of 18F-florbetapir using the Siemens Biograph mMR scanner.

Rapid scatter estimation for CBCT using the Boltzmann transport equation

NASA Astrophysics Data System (ADS)

Sun, Mingshan; Maslowski, Alex; Davis, Ian; Wareing, Todd; Failla, Gregory; Star-Lack, Josh

2014-03-01

Scatter in cone-beam computed tomography (CBCT) is a significant problem that degrades image contrast, uniformity and CT number accuracy. One means of estimating and correcting for detected scatter is through an iterative deconvolution process known as scatter kernel superposition (SKS). While the SKS approach is efficient, clinically significant errors on the order 2-4% (20-40 HU) still remain. We have previously shown that the kernel method can be improved by perturbing the kernel parameters based on reference data provided by limited Monte Carlo simulations of a first-pass reconstruction. In this work, we replace the Monte Carlo modeling with a deterministic Boltzmann solver (AcurosCTS) to generate the reference scatter data in a dramatically reduced time. In addition, the algorithm is improved so that instead of adjusting kernel parameters, we directly perturb the SKS scatter estimates. Studies were conducted on simulated data and on a large pelvis phantom scanned on a tabletop system. The new method reduced average reconstruction errors (relative to a reference scan) from 2.5% to 1.8%, and significantly improved visualization of low contrast objects. In total, 24 projections were simulated with an AcurosCTS execution time of 22 sec/projection using an 8-core computer. We have ported AcurosCTS to the GPU, and current run-times are approximately 4 sec/projection using two GPU's running in parallel.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jarrott, L. C.; McGuffey, C.; Beg, F. N.

Fast electron transport and spatial energy deposition are investigated in integrated cone-guided Fast Ignition experiments by measuring fast electron induced copper K-shell emission using a copper tracer added to deuterated plastic shells with a geometrically reentrant gold cone. Experiments were carried out at the Laboratory for Laser Energetics on the OMEGA/OMEGA-EP Laser where the plastic shells were imploded using 54 of the 60 OMEGA60 beams (3ω, 20 kJ), while the high intensity OMEGA-EP (BL2) beam (1 ω, 10 ps, 500 J, I peak > 10 19 W/cm 2) was focused onto the inner cone tip. Here, a retrograde analysis usingmore » the hybrid-PIC electron transport code, ZUMA, is performed to examine the sensitivity of the copper Kα spatial profile on the laser-produced fast electrons, facilitating the optimization of new target point designs and laser configurations to improve the compressed core areal density by a factor of 4 and the fast electron energy coupling by a factor of 3.5.« less

GeauxDock: Accelerating Structure-Based Virtual Screening with Heterogeneous Computing

PubMed Central

Fang, Ye; Ding, Yun; Feinstein, Wei P.; Koppelman, David M.; Moreno, Juana; Jarrell, Mark; Ramanujam, J.; Brylinski, Michal

2016-01-01

Computational modeling of drug binding to proteins is an integral component of direct drug design. Particularly, structure-based virtual screening is often used to perform large-scale modeling of putative associations between small organic molecules and their pharmacologically relevant protein targets. Because of a large number of drug candidates to be evaluated, an accurate and fast docking engine is a critical element of virtual screening. Consequently, highly optimized docking codes are of paramount importance for the effectiveness of virtual screening methods. In this communication, we describe the implementation, tuning and performance characteristics of GeauxDock, a recently developed molecular docking program. GeauxDock is built upon the Monte Carlo algorithm and features a novel scoring function combining physics-based energy terms with statistical and knowledge-based potentials. Developed specifically for heterogeneous computing platforms, the current version of GeauxDock can be deployed on modern, multi-core Central Processing Units (CPUs) as well as massively parallel accelerators, Intel Xeon Phi and NVIDIA Graphics Processing Unit (GPU). First, we carried out a thorough performance tuning of the high-level framework and the docking kernel to produce a fast serial code, which was then ported to shared-memory multi-core CPUs yielding a near-ideal scaling. Further, using Xeon Phi gives 1.9× performance improvement over a dual 10-core Xeon CPU, whereas the best GPU accelerator, GeForce GTX 980, achieves a speedup as high as 3.5×. On that account, GeauxDock can take advantage of modern heterogeneous architectures to considerably accelerate structure-based virtual screening applications. GeauxDock is open-sourced and publicly available at www.brylinski.org/geauxdock and https://figshare.com/articles/geauxdock_tar_gz/3205249. PMID:27420300

GeauxDock: Accelerating Structure-Based Virtual Screening with Heterogeneous Computing.

PubMed

Fang, Ye; Ding, Yun; Feinstein, Wei P; Koppelman, David M; Moreno, Juana; Jarrell, Mark; Ramanujam, J; Brylinski, Michal

2016-01-01

Computational modeling of drug binding to proteins is an integral component of direct drug design. Particularly, structure-based virtual screening is often used to perform large-scale modeling of putative associations between small organic molecules and their pharmacologically relevant protein targets. Because of a large number of drug candidates to be evaluated, an accurate and fast docking engine is a critical element of virtual screening. Consequently, highly optimized docking codes are of paramount importance for the effectiveness of virtual screening methods. In this communication, we describe the implementation, tuning and performance characteristics of GeauxDock, a recently developed molecular docking program. GeauxDock is built upon the Monte Carlo algorithm and features a novel scoring function combining physics-based energy terms with statistical and knowledge-based potentials. Developed specifically for heterogeneous computing platforms, the current version of GeauxDock can be deployed on modern, multi-core Central Processing Units (CPUs) as well as massively parallel accelerators, Intel Xeon Phi and NVIDIA Graphics Processing Unit (GPU). First, we carried out a thorough performance tuning of the high-level framework and the docking kernel to produce a fast serial code, which was then ported to shared-memory multi-core CPUs yielding a near-ideal scaling. Further, using Xeon Phi gives 1.9× performance improvement over a dual 10-core Xeon CPU, whereas the best GPU accelerator, GeForce GTX 980, achieves a speedup as high as 3.5×. On that account, GeauxDock can take advantage of modern heterogeneous architectures to considerably accelerate structure-based virtual screening applications. GeauxDock is open-sourced and publicly available at www.brylinski.org/geauxdock and https://figshare.com/articles/geauxdock_tar_gz/3205249.

High-throughput sequence alignment using Graphics Processing Units

PubMed Central

Schatz, Michael C; Trapnell, Cole; Delcher, Arthur L; Varshney, Amitabh

2007-01-01

Background The recent availability of new, less expensive high-throughput DNA sequencing technologies has yielded a dramatic increase in the volume of sequence data that must be analyzed. These data are being generated for several purposes, including genotyping, genome resequencing, metagenomics, and de novo genome assembly projects. Sequence alignment programs such as MUMmer have proven essential for analysis of these data, but researchers will need ever faster, high-throughput alignment tools running on inexpensive hardware to keep up with new sequence technologies. Results This paper describes MUMmerGPU, an open-source high-throughput parallel pairwise local sequence alignment program that runs on commodity Graphics Processing Units (GPUs) in common workstations. MUMmerGPU uses the new Compute Unified Device Architecture (CUDA) from nVidia to align multiple query sequences against a single reference sequence stored as a suffix tree. By processing the queries in parallel on the highly parallel graphics card, MUMmerGPU achieves more than a 10-fold speedup over a serial CPU version of the sequence alignment kernel, and outperforms the exact alignment component of MUMmer on a high end CPU by 3.5-fold in total application time when aligning reads from recent sequencing projects using Solexa/Illumina, 454, and Sanger sequencing technologies. Conclusion MUMmerGPU is a low cost, ultra-fast sequence alignment program designed to handle the increasing volume of data produced by new, high-throughput sequencing technologies. MUMmerGPU demonstrates that even memory-intensive applications can run significantly faster on the relatively low-cost GPU than on the CPU. PMID:18070356

GPU-based optimal control for RWM feedback in tokamaks

DOE PAGES

Clement, Mitchell; Hanson, Jeremy; Bialek, Jim; ...

2017-08-23

The design and implementation of a Graphics Processing Unit (GPU) based Resistive Wall Mode (RWM) controller to perform feedback control on the RWM using Linear Quadratic Gaussian (LQG) control is reported herein. Also, the control algorithm is based on a simplified DIII-D VALEN model. By using NVIDIA’s GPUDirect RDMA framework, the digitizer and output module are able to write and read directly to and from GPU memory, eliminating memory transfers between host and GPU. In conclusion, the system and algorithm was able to reduce plasma response excited by externally applied fields by 32% during development experiments.

GPU-based optimal control for RWM feedback in tokamaks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Clement, Mitchell; Hanson, Jeremy; Bialek, Jim

The design and implementation of a Graphics Processing Unit (GPU) based Resistive Wall Mode (RWM) controller to perform feedback control on the RWM using Linear Quadratic Gaussian (LQG) control is reported herein. Also, the control algorithm is based on a simplified DIII-D VALEN model. By using NVIDIA’s GPUDirect RDMA framework, the digitizer and output module are able to write and read directly to and from GPU memory, eliminating memory transfers between host and GPU. In conclusion, the system and algorithm was able to reduce plasma response excited by externally applied fields by 32% during development experiments.

Transport and spatial energy deposition of relativistic electrons in copper-doped fast ignition plasmas

DOE PAGES

Jarrott, L. C.; McGuffey, C.; Beg, F. N.; ...

2017-10-24

Fast electron transport and spatial energy deposition are investigated in integrated cone-guided Fast Ignition experiments by measuring fast electron induced copper K-shell emission using a copper tracer added to deuterated plastic shells with a geometrically reentrant gold cone. Experiments were carried out at the Laboratory for Laser Energetics on the OMEGA/OMEGA-EP Laser where the plastic shells were imploded using 54 of the 60 OMEGA60 beams (3ω, 20 kJ), while the high intensity OMEGA-EP (BL2) beam (1 ω, 10 ps, 500 J, I peak > 10 19 W/cm 2) was focused onto the inner cone tip. Here, a retrograde analysis usingmore » the hybrid-PIC electron transport code, ZUMA, is performed to examine the sensitivity of the copper Kα spatial profile on the laser-produced fast electrons, facilitating the optimization of new target point designs and laser configurations to improve the compressed core areal density by a factor of 4 and the fast electron energy coupling by a factor of 3.5.« less

GPU-Accelerated Forward and Back-Projections with Spatially Varying Kernels for 3D DIRECT TOF PET Reconstruction.

PubMed

Ha, S; Matej, S; Ispiryan, M; Mueller, K

2013-02-01

We describe a GPU-accelerated framework that efficiently models spatially (shift) variant system response kernels and performs forward- and back-projection operations with these kernels for the DIRECT (Direct Image Reconstruction for TOF) iterative reconstruction approach. Inherent challenges arise from the poor memory cache performance at non-axis aligned TOF directions. Focusing on the GPU memory access patterns, we utilize different kinds of GPU memory according to these patterns in order to maximize the memory cache performance. We also exploit the GPU instruction-level parallelism to efficiently hide long latencies from the memory operations. Our experiments indicate that our GPU implementation of the projection operators has slightly faster or approximately comparable time performance than FFT-based approaches using state-of-the-art FFTW routines. However, most importantly, our GPU framework can also efficiently handle any generic system response kernels, such as spatially symmetric and shift-variant as well as spatially asymmetric and shift-variant, both of which an FFT-based approach cannot cope with.

GPU-Accelerated Forward and Back-Projections With Spatially Varying Kernels for 3D DIRECT TOF PET Reconstruction

NASA Astrophysics Data System (ADS)

Ha, S.; Matej, S.; Ispiryan, M.; Mueller, K.

2013-02-01

We describe a GPU-accelerated framework that efficiently models spatially (shift) variant system response kernels and performs forward- and back-projection operations with these kernels for the DIRECT (Direct Image Reconstruction for TOF) iterative reconstruction approach. Inherent challenges arise from the poor memory cache performance at non-axis aligned TOF directions. Focusing on the GPU memory access patterns, we utilize different kinds of GPU memory according to these patterns in order to maximize the memory cache performance. We also exploit the GPU instruction-level parallelism to efficiently hide long latencies from the memory operations. Our experiments indicate that our GPU implementation of the projection operators has slightly faster or approximately comparable time performance than FFT-based approaches using state-of-the-art FFTW routines. However, most importantly, our GPU framework can also efficiently handle any generic system response kernels, such as spatially symmetric and shift-variant as well as spatially asymmetric and shift-variant, both of which an FFT-based approach cannot cope with.

GPU-based parallel algorithm for blind image restoration using midfrequency-based methods

NASA Astrophysics Data System (ADS)

Xie, Lang; Luo, Yi-han; Bao, Qi-liang

2013-08-01

GPU-based general-purpose computing is a new branch of modern parallel computing, so the study of parallel algorithms specially designed for GPU hardware architecture is of great significance. In order to solve the problem of high computational complexity and poor real-time performance in blind image restoration, the midfrequency-based algorithm for blind image restoration was analyzed and improved in this paper. Furthermore, a midfrequency-based filtering method is also used to restore the image hardly with any recursion or iteration. Combining the algorithm with data intensiveness, data parallel computing and GPU execution model of single instruction and multiple threads, a new parallel midfrequency-based algorithm for blind image restoration is proposed in this paper, which is suitable for stream computing of GPU. In this algorithm, the GPU is utilized to accelerate the estimation of class-G point spread functions and midfrequency-based filtering. Aiming at better management of the GPU threads, the threads in a grid are scheduled according to the decomposition of the filtering data in frequency domain after the optimization of data access and the communication between the host and the device. The kernel parallelism structure is determined by the decomposition of the filtering data to ensure the transmission rate to get around the memory bandwidth limitation. The results show that, with the new algorithm, the operational speed is significantly increased and the real-time performance of image restoration is effectively improved, especially for high-resolution images.

2005 6th Annual Science and Engineering Technology Conference

DTIC Science & Technology

2005-04-21

BioFAC VBAIDS Hybrid: PCR/Immuno Fast PCR Fast Immunoassay Mass Spec (Pyrolysis) SIBS UV -LIF IR Fluorochrome Charge Detect. BioCADS Trigger Advanced...Weights Beam forming Signal Processing mapped to GPU architecture Vector Processor STAP (STAP-BOY) GaN High Frequency Transistor (WBG-RF) UV Laser...Service anti- counterfeiting • Embedded security strips Technology Limitations and Barriers • Training and cost (training intensive) Land Borders North Land

Distributed GPU Computing in GIScience

NASA Astrophysics Data System (ADS)

Jiang, Y.; Yang, C.; Huang, Q.; Li, J.; Sun, M.

2013-12-01

Geoscientists strived to discover potential principles and patterns hidden inside ever-growing Big Data for scientific discoveries. To better achieve this objective, more capable computing resources are required to process, analyze and visualize Big Data (Ferreira et al., 2003; Li et al., 2013). Current CPU-based computing techniques cannot promptly meet the computing challenges caused by increasing amount of datasets from different domains, such as social media, earth observation, environmental sensing (Li et al., 2013). Meanwhile CPU-based computing resources structured as cluster or supercomputer is costly. In the past several years with GPU-based technology matured in both the capability and performance, GPU-based computing has emerged as a new computing paradigm. Compare to traditional computing microprocessor, the modern GPU, as a compelling alternative microprocessor, has outstanding high parallel processing capability with cost-effectiveness and efficiency(Owens et al., 2008), although it is initially designed for graphical rendering in visualization pipe. This presentation reports a distributed GPU computing framework for integrating GPU-based computing within distributed environment. Within this framework, 1) for each single computer, computing resources of both GPU-based and CPU-based can be fully utilized to improve the performance of visualizing and processing Big Data; 2) within a network environment, a variety of computers can be used to build up a virtual super computer to support CPU-based and GPU-based computing in distributed computing environment; 3) GPUs, as a specific graphic targeted device, are used to greatly improve the rendering efficiency in distributed geo-visualization, especially for 3D/4D visualization. Key words: Geovisualization, GIScience, Spatiotemporal Studies Reference : 1. Ferreira de Oliveira, M. C., & Levkowitz, H. (2003). From visual data exploration to visual data mining: A survey. Visualization and Computer Graphics, IEEE Transactions on, 9(3), 378-394. 2. Li, J., Jiang, Y., Yang, C., Huang, Q., & Rice, M. (2013). Visualizing 3D/4D Environmental Data Using Many-core Graphics Processing Units (GPUs) and Multi-core Central Processing Units (CPUs). Computers & Geosciences, 59(9), 78-89. 3. Owens, J. D., Houston, M., Luebke, D., Green, S., Stone, J. E., & Phillips, J. C. (2008). GPU computing. Proceedings of the IEEE, 96(5), 879-899.

Fast, Accurate and Shift-Varying Line Projections for Iterative Reconstruction Using the GPU

PubMed Central

Pratx, Guillem; Chinn, Garry; Olcott, Peter D.; Levin, Craig S.

2013-01-01

List-mode processing provides an efficient way to deal with sparse projections in iterative image reconstruction for emission tomography. An issue often reported is the tremendous amount of computation required by such algorithm. Each recorded event requires several back- and forward line projections. We investigated the use of the programmable graphics processing unit (GPU) to accelerate the line-projection operations and implement fully-3D list-mode ordered-subsets expectation-maximization for positron emission tomography (PET). We designed a reconstruction approach that incorporates resolution kernels, which model the spatially-varying physical processes associated with photon emission, transport and detection. Our development is particularly suitable for applications where the projection data is sparse, such as high-resolution, dynamic, and time-of-flight PET reconstruction. The GPU approach runs more than 50 times faster than an equivalent CPU implementation while image quality and accuracy are virtually identical. This paper describes in details how the GPU can be used to accelerate the line projection operations, even when the lines-of-response have arbitrary endpoint locations and shift-varying resolution kernels are used. A quantitative evaluation is included to validate the correctness of this new approach. PMID:19244015

Compiler-based code generation and autotuning for geometric multigrid on GPU-accelerated supercomputers

DOE PAGES

Basu, Protonu; Williams, Samuel; Van Straalen, Brian; ...

2017-04-05

GPUs, with their high bandwidths and computational capabilities are an increasingly popular target for scientific computing. Unfortunately, to date, harnessing the power of the GPU has required use of a GPU-specific programming model like CUDA, OpenCL, or OpenACC. Thus, in order to deliver portability across CPU-based and GPU-accelerated supercomputers, programmers are forced to write and maintain two versions of their applications or frameworks. In this paper, we explore the use of a compiler-based autotuning framework based on CUDA-CHiLL to deliver not only portability, but also performance portability across CPU- and GPU-accelerated platforms for the geometric multigrid linear solvers found inmore » many scientific applications. We also show that with autotuning we can attain near Roofline (a performance bound for a computation and target architecture) performance across the key operations in the miniGMG benchmark for both CPU- and GPU-based architectures as well as for a multiple stencil discretizations and smoothers. We show that our technology is readily interoperable with MPI resulting in performance at scale equal to that obtained via hand-optimized MPI+CUDA implementation.« less

Compiler-based code generation and autotuning for geometric multigrid on GPU-accelerated supercomputers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Basu, Protonu; Williams, Samuel; Van Straalen, Brian

GPUs, with their high bandwidths and computational capabilities are an increasingly popular target for scientific computing. Unfortunately, to date, harnessing the power of the GPU has required use of a GPU-specific programming model like CUDA, OpenCL, or OpenACC. Thus, in order to deliver portability across CPU-based and GPU-accelerated supercomputers, programmers are forced to write and maintain two versions of their applications or frameworks. In this paper, we explore the use of a compiler-based autotuning framework based on CUDA-CHiLL to deliver not only portability, but also performance portability across CPU- and GPU-accelerated platforms for the geometric multigrid linear solvers found inmore » many scientific applications. We also show that with autotuning we can attain near Roofline (a performance bound for a computation and target architecture) performance across the key operations in the miniGMG benchmark for both CPU- and GPU-based architectures as well as for a multiple stencil discretizations and smoothers. We show that our technology is readily interoperable with MPI resulting in performance at scale equal to that obtained via hand-optimized MPI+CUDA implementation.« less

Accelerating Spaceborne SAR Imaging Using Multiple CPU/GPU Deep Collaborative Computing

PubMed Central

Zhang, Fan; Li, Guojun; Li, Wei; Hu, Wei; Hu, Yuxin

2016-01-01

With the development of synthetic aperture radar (SAR) technologies in recent years, the huge amount of remote sensing data brings challenges for real-time imaging processing. Therefore, high performance computing (HPC) methods have been presented to accelerate SAR imaging, especially the GPU based methods. In the classical GPU based imaging algorithm, GPU is employed to accelerate image processing by massive parallel computing, and CPU is only used to perform the auxiliary work such as data input/output (IO). However, the computing capability of CPU is ignored and underestimated. In this work, a new deep collaborative SAR imaging method based on multiple CPU/GPU is proposed to achieve real-time SAR imaging. Through the proposed tasks partitioning and scheduling strategy, the whole image can be generated with deep collaborative multiple CPU/GPU computing. In the part of CPU parallel imaging, the advanced vector extension (AVX) method is firstly introduced into the multi-core CPU parallel method for higher efficiency. As for the GPU parallel imaging, not only the bottlenecks of memory limitation and frequent data transferring are broken, but also kinds of optimized strategies are applied, such as streaming, parallel pipeline and so on. Experimental results demonstrate that the deep CPU/GPU collaborative imaging method enhances the efficiency of SAR imaging on single-core CPU by 270 times and realizes the real-time imaging in that the imaging rate outperforms the raw data generation rate. PMID:27070606

Accelerating Spaceborne SAR Imaging Using Multiple CPU/GPU Deep Collaborative Computing.

PubMed

Zhang, Fan; Li, Guojun; Li, Wei; Hu, Wei; Hu, Yuxin

2016-04-07

With the development of synthetic aperture radar (SAR) technologies in recent years, the huge amount of remote sensing data brings challenges for real-time imaging processing. Therefore, high performance computing (HPC) methods have been presented to accelerate SAR imaging, especially the GPU based methods. In the classical GPU based imaging algorithm, GPU is employed to accelerate image processing by massive parallel computing, and CPU is only used to perform the auxiliary work such as data input/output (IO). However, the computing capability of CPU is ignored and underestimated. In this work, a new deep collaborative SAR imaging method based on multiple CPU/GPU is proposed to achieve real-time SAR imaging. Through the proposed tasks partitioning and scheduling strategy, the whole image can be generated with deep collaborative multiple CPU/GPU computing. In the part of CPU parallel imaging, the advanced vector extension (AVX) method is firstly introduced into the multi-core CPU parallel method for higher efficiency. As for the GPU parallel imaging, not only the bottlenecks of memory limitation and frequent data transferring are broken, but also kinds of optimized strategies are applied, such as streaming, parallel pipeline and so on. Experimental results demonstrate that the deep CPU/GPU collaborative imaging method enhances the efficiency of SAR imaging on single-core CPU by 270 times and realizes the real-time imaging in that the imaging rate outperforms the raw data generation rate.

Acceleration for 2D time-domain elastic full waveform inversion using a single GPU card

NASA Astrophysics Data System (ADS)

Jiang, Jinpeng; Zhu, Peimin

2018-05-01

Full waveform inversion (FWI) is a challenging procedure due to the high computational cost related to the modeling, especially for the elastic case. The graphics processing unit (GPU) has become a popular device for the high-performance computing (HPC). To reduce the long computation time, we design and implement the GPU-based 2D elastic FWI (EFWI) in time domain using a single GPU card. We parallelize the forward modeling and gradient calculations using the CUDA programming language. To overcome the limitation of relatively small global memory on GPU, the boundary saving strategy is exploited to reconstruct the forward wavefield. Moreover, the L-BFGS optimization method used in the inversion increases the convergence of the misfit function. A multiscale inversion strategy is performed in the workflow to obtain the accurate inversion results. In our tests, the GPU-based implementations using a single GPU device achieve >15 times speedup in forward modeling, and about 12 times speedup in gradient calculation, compared with the eight-core CPU implementations optimized by OpenMP. The test results from the GPU implementations are verified to have enough accuracy by comparing the results obtained from the CPU implementations.

Simulation of reaction diffusion processes over biologically relevant size and time scales using multi-GPU workstations

PubMed Central

Hallock, Michael J.; Stone, John E.; Roberts, Elijah; Fry, Corey; Luthey-Schulten, Zaida

2014-01-01

Simulation of in vivo cellular processes with the reaction-diffusion master equation (RDME) is a computationally expensive task. Our previous software enabled simulation of inhomogeneous biochemical systems for small bacteria over long time scales using the MPD-RDME method on a single GPU. Simulations of larger eukaryotic systems exceed the on-board memory capacity of individual GPUs, and long time simulations of modest-sized cells such as yeast are impractical on a single GPU. We present a new multi-GPU parallel implementation of the MPD-RDME method based on a spatial decomposition approach that supports dynamic load balancing for workstations containing GPUs of varying performance and memory capacity. We take advantage of high-performance features of CUDA for peer-to-peer GPU memory transfers and evaluate the performance of our algorithms on state-of-the-art GPU devices. We present parallel e ciency and performance results for simulations using multiple GPUs as system size, particle counts, and number of reactions grow. We also demonstrate multi-GPU performance in simulations of the Min protein system in E. coli. Moreover, our multi-GPU decomposition and load balancing approach can be generalized to other lattice-based problems. PMID:24882911

Simulation of reaction diffusion processes over biologically relevant size and time scales using multi-GPU workstations.

PubMed

Hallock, Michael J; Stone, John E; Roberts, Elijah; Fry, Corey; Luthey-Schulten, Zaida

2014-05-01

Simulation of in vivo cellular processes with the reaction-diffusion master equation (RDME) is a computationally expensive task. Our previous software enabled simulation of inhomogeneous biochemical systems for small bacteria over long time scales using the MPD-RDME method on a single GPU. Simulations of larger eukaryotic systems exceed the on-board memory capacity of individual GPUs, and long time simulations of modest-sized cells such as yeast are impractical on a single GPU. We present a new multi-GPU parallel implementation of the MPD-RDME method based on a spatial decomposition approach that supports dynamic load balancing for workstations containing GPUs of varying performance and memory capacity. We take advantage of high-performance features of CUDA for peer-to-peer GPU memory transfers and evaluate the performance of our algorithms on state-of-the-art GPU devices. We present parallel e ciency and performance results for simulations using multiple GPUs as system size, particle counts, and number of reactions grow. We also demonstrate multi-GPU performance in simulations of the Min protein system in E. coli . Moreover, our multi-GPU decomposition and load balancing approach can be generalized to other lattice-based problems.

GPU-based High-Performance Computing for Radiation Therapy

PubMed Central

Jia, Xun; Ziegenhein, Peter; Jiang, Steve B.

2014-01-01

Recent developments in radiotherapy therapy demand high computation powers to solve challenging problems in a timely fashion in a clinical environment. Graphics processing unit (GPU), as an emerging high-performance computing platform, has been introduced to radiotherapy. It is particularly attractive due to its high computational power, small size, and low cost for facility deployment and maintenance. Over the past a few years, GPU-based high-performance computing in radiotherapy has experienced rapid developments. A tremendous amount of studies have been conducted, in which large acceleration factors compared with the conventional CPU platform have been observed. In this article, we will first give a brief introduction to the GPU hardware structure and programming model. We will then review the current applications of GPU in major imaging-related and therapy-related problems encountered in radiotherapy. A comparison of GPU with other platforms will also be presented. PMID:24486639

Analysis and Implementation of Particle-to-Particle (P2P) Graphics Processor Unit (GPU) Kernel for Black-Box Adaptive Fast Multipole Method

DTIC Science & Technology

2015-06-01

5110P and 16 dx360M4 nodes each with one NVIDIA Kepler K20M/K40M GPU. Each node contained dual Intel Xeon E5-2670 (Sandy Bridge) central processing...kernel and as such does not employ multiple processors. This work makes use of a single processing core and a single NVIDIA Kepler K40 GK110...bandwidth (2 × 16 slot), 7.877 GFloat/s; Kepler K40 peak, 4,290 × 1 billion floating-point operations (GFLOPs), and 288 GB/s Kepler K40 memory

Controlling dynamics of imploded core plasma for fast ignition

NASA Astrophysics Data System (ADS)

Nagatomo, H.; Johzaki, T.; Sunahara, A.; Shiraga, H.; Sakagami, H.; Cai, H.; Mima, K.

2010-08-01

In the Fast ignition, formation of highly compressed core plasma is one of critical issue. In this work, the effect hydrodynamic instability in cone-guided shell implosion is studied. Two-dimensional radiation hydrodynamic simulations are carried out where realistic seeds of Rayleigh-Taylor instability are imposed. Preliminary results suggest that the instability reduces implosion performance, such as implosion velocity, areal density, and maximum density. In perturbed target implosion, the break-up time of the tip of the cone is earlier than that of ideal unperturbed target implosion case. This is crucial matter for the Fast ignition because the pass for the heating laser is filled with plasma before the shot of heating laser. A sophisticated implosion design of stable and low in-flight aspect ratio is necessary for cone-guided shell implosion.

WE-EF-207-05: Monte Carlo Dosimetry for a Dedicated Cone-Beam CT Head Scanner

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sisniega, A; Zbijewski, W; Xu, J

Purpose: Cone-Beam CT (CBCT) is an attractive platform for point-of-care imaging of traumatic brain injury and intracranial hemorrhage. This work implements and evaluates a fast Monte-Carlo (MC) dose estimation engine for development of a dedicated head CBCT scanner, optimization of acquisition protocols, geometry, bowtie filter designs, and patient-specific dosimetry. Methods: Dose scoring with a GPU-based MC CBCT simulator was validated on an imaging bench using a modified 16 cm CTDI phantom with 7 ion chamber shafts along the central ray for 80–100 kVp (+2 mm Al, +0.2 mm Cu). Dose distributions were computed in a segmented CBCT reconstruction of anmore » anthropomorphic head phantom with 4×10{sup 5} tracked photons per scan (5 min runtime). Circular orbits with angular span ranging from short scan (180° + fan angle) to full rotation (360°) were considered for fixed total mAs per scan. Two aluminum filters were investigated: aggressive bowtie, and moderate bowtie (matched to 16 cm and 32 cm water cylinder, respectively). Results: MC dose estimates showed strong agreement with measurements (RMSE<0.001 mGy/mAs). A moderate (aggressive) bowtie reduced the dose, per total mAs, by 20% (30%) at the center of the head, by 40% (50%) at the eye lens, and by 70% (80%) at the posterior skin entrance. For the no bowtie configuration, a short scan reduced the eye lens dose by 62% (from 0.08 mGy/mAs to 0.03 mGy/mAs) compared to full scan, although the dose to spinal bone marrow increased by 40%. For both bowties, the short scan resulted in a similar 40% increase in bone marrow dose, but the reduction in the eye lens was more pronounced: 70% (90%) for the moderate (aggressive) bowtie. Conclusions: Dose maps obtained with validated MC simulation demonstrated dose reduction in sensitive structures (eye lens and bone marrow) through combination of short-scan trajectories and bowtie filters. Xiaohui Wang and David Foos are employees of Carestream Health.« less

Accelerated Monte Carlo Simulation on the Chemical Stage in Water Radiolysis using GPU

PubMed Central

Tian, Zhen; Jiang, Steve B.; Jia, Xun

2018-01-01

The accurate simulation of water radiolysis is an important step to understand the mechanisms of radiobiology and quantitatively test some hypotheses regarding radiobiological effects. However, the simulation of water radiolysis is highly time consuming, taking hours or even days to be completed by a conventional CPU processor. This time limitation hinders cell-level simulations for a number of research studies. We recently initiated efforts to develop gMicroMC, a GPU-based fast microscopic MC simulation package for water radiolysis. The first step of this project focused on accelerating the simulation of the chemical stage, the most time consuming stage in the entire water radiolysis process. A GPU-friendly parallelization strategy was designed to address the highly correlated many-body simulation problem caused by the mutual competitive chemical reactions between the radiolytic molecules. Two cases were tested, using a 750 keV electron and a 5 MeV proton incident in pure water, respectively. The time-dependent yields of all the radiolytic species during the chemical stage were used to evaluate the accuracy of the simulation. The relative differences between our simulation and the Geant4-DNA simulation were on average 5.3% and 4.4% for the two cases. Our package, executed on an Nvidia Titan black GPU card, successfully completed the chemical stage simulation of the two cases within 599.2 s and 489.0 s. As compared with Geant4-DNA that was executed on an Intel i7-5500U CPU processor and needed 28.6 h and 26.8 h for the two cases using a single CPU core, our package achieved a speed-up factor of 171.1-197.2. PMID:28323637

Accelerated Monte Carlo simulation on the chemical stage in water radiolysis using GPU

NASA Astrophysics Data System (ADS)

Tian, Zhen; Jiang, Steve B.; Jia, Xun

2017-04-01

The accurate simulation of water radiolysis is an important step to understand the mechanisms of radiobiology and quantitatively test some hypotheses regarding radiobiological effects. However, the simulation of water radiolysis is highly time consuming, taking hours or even days to be completed by a conventional CPU processor. This time limitation hinders cell-level simulations for a number of research studies. We recently initiated efforts to develop gMicroMC, a GPU-based fast microscopic MC simulation package for water radiolysis. The first step of this project focused on accelerating the simulation of the chemical stage, the most time consuming stage in the entire water radiolysis process. A GPU-friendly parallelization strategy was designed to address the highly correlated many-body simulation problem caused by the mutual competitive chemical reactions between the radiolytic molecules. Two cases were tested, using a 750 keV electron and a 5 MeV proton incident in pure water, respectively. The time-dependent yields of all the radiolytic species during the chemical stage were used to evaluate the accuracy of the simulation. The relative differences between our simulation and the Geant4-DNA simulation were on average 5.3% and 4.4% for the two cases. Our package, executed on an Nvidia Titan black GPU card, successfully completed the chemical stage simulation of the two cases within 599.2 s and 489.0 s. As compared with Geant4-DNA that was executed on an Intel i7-5500U CPU processor and needed 28.6 h and 26.8 h for the two cases using a single CPU core, our package achieved a speed-up factor of 171.1-197.2.

Accelerated Monte Carlo simulation on the chemical stage in water radiolysis using GPU.

PubMed

Tian, Zhen; Jiang, Steve B; Jia, Xun

2017-04-21

The accurate simulation of water radiolysis is an important step to understand the mechanisms of radiobiology and quantitatively test some hypotheses regarding radiobiological effects. However, the simulation of water radiolysis is highly time consuming, taking hours or even days to be completed by a conventional CPU processor. This time limitation hinders cell-level simulations for a number of research studies. We recently initiated efforts to develop gMicroMC, a GPU-based fast microscopic MC simulation package for water radiolysis. The first step of this project focused on accelerating the simulation of the chemical stage, the most time consuming stage in the entire water radiolysis process. A GPU-friendly parallelization strategy was designed to address the highly correlated many-body simulation problem caused by the mutual competitive chemical reactions between the radiolytic molecules. Two cases were tested, using a 750 keV electron and a 5 MeV proton incident in pure water, respectively. The time-dependent yields of all the radiolytic species during the chemical stage were used to evaluate the accuracy of the simulation. The relative differences between our simulation and the Geant4-DNA simulation were on average 5.3% and 4.4% for the two cases. Our package, executed on an Nvidia Titan black GPU card, successfully completed the chemical stage simulation of the two cases within 599.2 s and 489.0 s. As compared with Geant4-DNA that was executed on an Intel i7-5500U CPU processor and needed 28.6 h and 26.8 h for the two cases using a single CPU core, our package achieved a speed-up factor of 171.1-197.2.

Towards real-time photon Monte Carlo dose calculation in the cloud

NASA Astrophysics Data System (ADS)

Ziegenhein, Peter; Kozin, Igor N.; Kamerling, Cornelis Ph; Oelfke, Uwe

2017-06-01

Near real-time application of Monte Carlo (MC) dose calculation in clinic and research is hindered by the long computational runtimes of established software. Currently, fast MC software solutions are available utilising accelerators such as graphical processing units (GPUs) or clusters based on central processing units (CPUs). Both platforms are expensive in terms of purchase costs and maintenance and, in case of the GPU, provide only limited scalability. In this work we propose a cloud-based MC solution, which offers high scalability of accurate photon dose calculations. The MC simulations run on a private virtual supercomputer that is formed in the cloud. Computational resources can be provisioned dynamically at low cost without upfront investment in expensive hardware. A client-server software solution has been developed which controls the simulations and transports data to and from the cloud efficiently and securely. The client application integrates seamlessly into a treatment planning system. It runs the MC simulation workflow automatically and securely exchanges simulation data with the server side application that controls the virtual supercomputer. Advanced encryption standards were used to add an additional security layer, which encrypts and decrypts patient data on-the-fly at the processor register level. We could show that our cloud-based MC framework enables near real-time dose computation. It delivers excellent linear scaling for high-resolution datasets with absolute runtimes of 1.1 seconds to 10.9 seconds for simulating a clinical prostate and liver case up to 1% statistical uncertainty. The computation runtimes include the transportation of data to and from the cloud as well as process scheduling and synchronisation overhead. Cloud-based MC simulations offer a fast, affordable and easily accessible alternative for near real-time accurate dose calculations to currently used GPU or cluster solutions.

Towards real-time photon Monte Carlo dose calculation in the cloud.

PubMed

Ziegenhein, Peter; Kozin, Igor N; Kamerling, Cornelis Ph; Oelfke, Uwe

2017-06-07

Near real-time application of Monte Carlo (MC) dose calculation in clinic and research is hindered by the long computational runtimes of established software. Currently, fast MC software solutions are available utilising accelerators such as graphical processing units (GPUs) or clusters based on central processing units (CPUs). Both platforms are expensive in terms of purchase costs and maintenance and, in case of the GPU, provide only limited scalability. In this work we propose a cloud-based MC solution, which offers high scalability of accurate photon dose calculations. The MC simulations run on a private virtual supercomputer that is formed in the cloud. Computational resources can be provisioned dynamically at low cost without upfront investment in expensive hardware. A client-server software solution has been developed which controls the simulations and transports data to and from the cloud efficiently and securely. The client application integrates seamlessly into a treatment planning system. It runs the MC simulation workflow automatically and securely exchanges simulation data with the server side application that controls the virtual supercomputer. Advanced encryption standards were used to add an additional security layer, which encrypts and decrypts patient data on-the-fly at the processor register level. We could show that our cloud-based MC framework enables near real-time dose computation. It delivers excellent linear scaling for high-resolution datasets with absolute runtimes of 1.1 seconds to 10.9 seconds for simulating a clinical prostate and liver case up to 1% statistical uncertainty. The computation runtimes include the transportation of data to and from the cloud as well as process scheduling and synchronisation overhead. Cloud-based MC simulations offer a fast, affordable and easily accessible alternative for near real-time accurate dose calculations to currently used GPU or cluster solutions.

Extra projection data identification method for fast-continuous-rotation industrial cone-beam CT.

PubMed

Yang, Min; Duan, Shengling; Duan, Jinghui; Wang, Xiaolong; Li, Xingdong; Meng, Fanyong; Zhang, Jianhai

2013-01-01

Fast-continuous-rotation is an effective measure to improve the scanning speed and decrease the radiation dose for cone-beam CT. However, because of acceleration and deceleration of the motor, as well as the response lag of the scanning control terminals to the host PC, uneven-distributed and redundant projections are inevitably created, which seriously decrease the quality of the reconstruction images. In this paper, we first analyzed the aspects of the theoretical sequence chart of the fast-continuous-rotation mode. Then, an optimized sequence chart was proposed by extending the rotation angle span to ensure the effective 2π-span projections were situated in the stable rotation stage. In order to match the rotation angle with the projection image accurately, structure similarity (SSIM) index was used as a control parameter for extraction of the effective projection sequence which was exactly the complete projection data for image reconstruction. The experimental results showed that SSIM based method had a high accuracy of projection view locating and was easy to realize.

Fast Image Subtraction Using Multi-cores and GPUs

NASA Astrophysics Data System (ADS)

Hartung, Steven; Shukla, H.

2013-01-01

Many important image processing techniques in astronomy require a massive number of computations per pixel. Among them is an image differencing technique known as Optimal Image Subtraction (OIS), which is very useful for detecting and characterizing transient phenomena. Like many image processing routines, OIS computations increase proportionally with the number of pixels being processed, and the number of pixels in need of processing is increasing rapidly. Utilizing many-core graphical processing unit (GPU) technology in a hybrid conjunction with multi-core CPU and computer clustering technologies, this work presents a new astronomy image processing pipeline architecture. The chosen OIS implementation focuses on the 2nd order spatially-varying kernel with the Dirac delta function basis, a powerful image differencing method that has seen limited deployment in part because of the heavy computational burden. This tool can process standard image calibration and OIS differencing in a fashion that is scalable with the increasing data volume. It employs several parallel processing technologies in a hierarchical fashion in order to best utilize each of their strengths. The Linux/Unix based application can operate on a single computer, or on an MPI configured cluster, with or without GPU hardware. With GPU hardware available, even low-cost commercial video cards, the OIS convolution and subtraction times for large images can be accelerated by up to three orders of magnitude.

How many molecules are required to measure a cyclic voltammogram?

NASA Astrophysics Data System (ADS)

Cutress, Ian J.; Compton, Richard G.

2011-05-01

The stochastic limit at which fully-reversible cyclic voltammetry can accurately be measured is investigated. Specifically, Monte Carlo GPU simulation is used to study low concentration cyclic voltammetry at a microdisk electrode over a range of scan rates and concentrations, and the results compared to the statistical limit as predicted by finite difference simulation based on Fick's Laws of Diffusion. Both Butler-Volmer and Marcus-Hush electrode kinetics are considered, simulated via random-walk methods, and shown to give identical results in the fast kinetic limit.

Thread scheduling for GPU-based OPC simulation on multi-thread

NASA Astrophysics Data System (ADS)

Lee, Heejun; Kim, Sangwook; Hong, Jisuk; Lee, Sooryong; Han, Hwansoo

2018-03-01

As semiconductor product development based on shrinkage continues, the accuracy and difficulty required for the model based optical proximity correction (MBOPC) is increasing. OPC simulation time, which is the most timeconsuming part of MBOPC, is rapidly increasing due to high pattern density in a layout and complex OPC model. To reduce OPC simulation time, we attempt to apply graphic processing unit (GPU) to MBOPC because OPC process is good to be programmed in parallel. We address some issues that may typically happen during GPU-based OPC simulation in multi thread system, such as "out of memory" and "GPU idle time". To overcome these problems, we propose a thread scheduling method, which manages OPC jobs in multiple threads in such a way that simulations jobs from multiple threads are alternatively executed on GPU while correction jobs are executed at the same time in each CPU cores. It was observed that the amount of GPU peak memory usage decreases by up to 35%, and MBOPC runtime also decreases by 4%. In cases where out of memory issues occur in a multi-threaded environment, the thread scheduler was used to improve MBOPC runtime up to 23%.

GPU-Powered Coherent Beamforming

NASA Astrophysics Data System (ADS)

Magro, A.; Adami, K. Zarb; Hickish, J.

2015-03-01

Graphics processing units (GPU)-based beamforming is a relatively unexplored area in radio astronomy, possibly due to the assumption that any such system will be severely limited by the PCIe bandwidth required to transfer data to the GPU. We have developed a CUDA-based GPU implementation of a coherent beamformer, specifically designed and optimized for deployment at the BEST-2 array which can generate an arbitrary number of synthesized beams for a wide range of parameters. It achieves ˜1.3 TFLOPs on an NVIDIA Tesla K20, approximately 10x faster than an optimized, multithreaded CPU implementation. This kernel has been integrated into two real-time, GPU-based time-domain software pipelines deployed at the BEST-2 array in Medicina: a standalone beamforming pipeline and a transient detection pipeline. We present performance benchmarks for the beamforming kernel as well as the transient detection pipeline with beamforming capabilities as well as results of test observation.

Fast CPU-based Monte Carlo simulation for radiotherapy dose calculation.

PubMed

Ziegenhein, Peter; Pirner, Sven; Ph Kamerling, Cornelis; Oelfke, Uwe

2015-08-07

Monte-Carlo (MC) simulations are considered to be the most accurate method for calculating dose distributions in radiotherapy. Its clinical application, however, still is limited by the long runtimes conventional implementations of MC algorithms require to deliver sufficiently accurate results on high resolution imaging data. In order to overcome this obstacle we developed the software-package PhiMC, which is capable of computing precise dose distributions in a sub-minute time-frame by leveraging the potential of modern many- and multi-core CPU-based computers. PhiMC is based on the well verified dose planning method (DPM). We could demonstrate that PhiMC delivers dose distributions which are in excellent agreement to DPM. The multi-core implementation of PhiMC scales well between different computer architectures and achieves a speed-up of up to 37[Formula: see text] compared to the original DPM code executed on a modern system. Furthermore, we could show that our CPU-based implementation on a modern workstation is between 1.25[Formula: see text] and 1.95[Formula: see text] faster than a well-known GPU implementation of the same simulation method on a NVIDIA Tesla C2050. Since CPUs work on several hundreds of GB RAM the typical GPU memory limitation does not apply for our implementation and high resolution clinical plans can be calculated.

A CFD Heterogeneous Parallel Solver Based on Collaborating CPU and GPU

NASA Astrophysics Data System (ADS)

Lai, Jianqi; Tian, Zhengyu; Li, Hua; Pan, Sha

2018-03-01

Since Graphic Processing Unit (GPU) has a strong ability of floating-point computation and memory bandwidth for data parallelism, it has been widely used in the areas of common computing such as molecular dynamics (MD), computational fluid dynamics (CFD) and so on. The emergence of compute unified device architecture (CUDA), which reduces the complexity of compiling program, brings the great opportunities to CFD. There are three different modes for parallel solution of NS equations: parallel solver based on CPU, parallel solver based on GPU and heterogeneous parallel solver based on collaborating CPU and GPU. As we can see, GPUs are relatively rich in compute capacity but poor in memory capacity and the CPUs do the opposite. We need to make full use of the GPUs and CPUs, so a CFD heterogeneous parallel solver based on collaborating CPU and GPU has been established. Three cases are presented to analyse the solver’s computational accuracy and heterogeneous parallel efficiency. The numerical results agree well with experiment results, which demonstrate that the heterogeneous parallel solver has high computational precision. The speedup on a single GPU is more than 40 for laminar flow, it decreases for turbulent flow, but it still can reach more than 20. What’s more, the speedup increases as the grid size becomes larger.

Intensity-based segmentation and visualization of cells in 3D microscopic images using the GPU

NASA Astrophysics Data System (ADS)

Kang, Mi-Sun; Lee, Jeong-Eom; Jeon, Woong-ki; Choi, Heung-Kook; Kim, Myoung-Hee

2013-02-01

3D microscopy images contain abundant astronomical data, rendering 3D microscopy image processing time-consuming and laborious on a central processing unit (CPU). To solve these problems, many people crop a region of interest (ROI) of the input image to a small size. Although this reduces cost and time, there are drawbacks at the image processing level, e.g., the selected ROI strongly depends on the user and there is a loss in original image information. To mitigate these problems, we developed a 3D microscopy image processing tool on a graphics processing unit (GPU). Our tool provides efficient and various automatic thresholding methods to achieve intensity-based segmentation of 3D microscopy images. Users can select the algorithm to be applied. Further, the image processing tool provides visualization of segmented volume data and can set the scale, transportation, etc. using a keyboard and mouse. However, the 3D objects visualized fast still need to be analyzed to obtain information for biologists. To analyze 3D microscopic images, we need quantitative data of the images. Therefore, we label the segmented 3D objects within all 3D microscopic images and obtain quantitative information on each labeled object. This information can use the classification feature. A user can select the object to be analyzed. Our tool allows the selected object to be displayed on a new window, and hence, more details of the object can be observed. Finally, we validate the effectiveness of our tool by comparing the CPU and GPU processing times by matching the specification and configuration.

SU-E-T-493: Accelerated Monte Carlo Methods for Photon Dosimetry Using a Dual-GPU System and CUDA.

PubMed

Liu, T; Ding, A; Xu, X

2012-06-01

To develop a Graphics Processing Unit (GPU) based Monte Carlo (MC) code that accelerates dose calculations on a dual-GPU system. We simulated a clinical case of prostate cancer treatment. A voxelized abdomen phantom derived from 120 CT slices was used containing 218×126×60 voxels, and a GE LightSpeed 16-MDCT scanner was modeled. A CPU version of the MC code was first developed in C++ and tested on Intel Xeon X5660 2.8GHz CPU, then it was translated into GPU version using CUDA C 4.1 and run on a dual Tesla m 2 090 GPU system. The code was featured with automatic assignment of simulation task to multiple GPUs, as well as accurate calculation of energy- and material- dependent cross-sections. Double-precision floating point format was used for accuracy. Doses to the rectum, prostate, bladder and femoral heads were calculated. When running on a single GPU, the MC GPU code was found to be ×19 times faster than the CPU code and ×42 times faster than MCNPX. These speedup factors were doubled on the dual-GPU system. The dose Result was benchmarked against MCNPX and a maximum difference of 1% was observed when the relative error is kept below 0.1%. A GPU-based MC code was developed for dose calculations using detailed patient and CT scanner models. Efficiency and accuracy were both guaranteed in this code. Scalability of the code was confirmed on the dual-GPU system. © 2012 American Association of Physicists in Medicine.

gWEGA: GPU-accelerated WEGA for molecular superposition and shape comparison.

PubMed

Yan, Xin; Li, Jiabo; Gu, Qiong; Xu, Jun

2014-06-05

Virtual screening of a large chemical library for drug lead identification requires searching/superimposing a large number of three-dimensional (3D) chemical structures. This article reports a graphic processing unit (GPU)-accelerated weighted Gaussian algorithm (gWEGA) that expedites shape or shape-feature similarity score-based virtual screening. With 86 GPU nodes (each node has one GPU card), gWEGA can screen 110 million conformations derived from an entire ZINC drug-like database with diverse antidiabetic agents as query structures within 2 s (i.e., screening more than 55 million conformations per second). The rapid screening speed was accomplished through the massive parallelization on multiple GPU nodes and rapid prescreening of 3D structures (based on their shape descriptors and pharmacophore feature compositions). Copyright © 2014 Wiley Periodicals, Inc.

A GPU-based symmetric non-rigid image registration method in human lung.

PubMed

Haghighi, Babak; D Ellingwood, Nathan; Yin, Youbing; Hoffman, Eric A; Lin, Ching-Long

2018-03-01

Quantitative computed tomography (QCT) of the lungs plays an increasing role in identifying sub-phenotypes of pathologies previously lumped into broad categories such as chronic obstructive pulmonary disease and asthma. Methods for image matching and linking multiple lung volumes have proven useful in linking structure to function and in the identification of regional longitudinal changes. Here, we seek to improve the accuracy of image matching via the use of a symmetric multi-level non-rigid registration employing an inverse consistent (IC) transformation whereby images are registered both in the forward and reverse directions. To develop the symmetric method, two similarity measures, the sum of squared intensity difference (SSD) and the sum of squared tissue volume difference (SSTVD), were used. The method is based on a novel generic mathematical framework to include forward and backward transformations, simultaneously, eliminating the need to compute the inverse transformation. Two implementations were used to assess the proposed method: a two-dimensional (2-D) implementation using synthetic examples with SSD, and a multi-core CPU and graphics processing unit (GPU) implementation with SSTVD for three-dimensional (3-D) human lung datasets (six normal adults studied at total lung capacity (TLC) and functional residual capacity (FRC)). Success was evaluated in terms of the IC transformation consistency serving to link TLC to FRC. 2-D registration on synthetic images, using both symmetric and non-symmetric SSD methods, and comparison of displacement fields showed that the symmetric method gave a symmetrical grid shape and reduced IC errors, with the mean values of IC errors decreased by 37%. Results for both symmetric and non-symmetric transformations of human datasets showed that the symmetric method gave better results for IC errors in all cases, with mean values of IC errors for the symmetric method lower than the non-symmetric methods using both SSD and SSTVD. The GPU version demonstrated an average of 43 times speedup and ~5.2 times speedup over the single-threaded and 12-threaded CPU versions, respectively. Run times with the GPU were as fast as 2 min. The symmetric method improved the inverse consistency, aiding the use of image registration in the QCT-based evaluation of the lung.

Design of a tight frame of 2D shearlets based on a fast non-iterative analysis and synthesis algorithm

NASA Astrophysics Data System (ADS)

Goossens, Bart; Aelterman, Jan; Luong, Hi"p.; Pižurica, Aleksandra; Philips, Wilfried

2011-09-01

The shearlet transform is a recent sibling in the family of geometric image representations that provides a traditional multiresolution analysis combined with a multidirectional analysis. In this paper, we present a fast DFT-based analysis and synthesis scheme for the 2D discrete shearlet transform. Our scheme conforms to the continuous shearlet theory to high extent, provides perfect numerical reconstruction (up to floating point rounding errors) in a non-iterative scheme and is highly suitable for parallel implementation (e.g. FPGA, GPU). We show that our discrete shearlet representation is also a tight frame and the redundancy factor of the transform is around 2.6, independent of the number of analysis directions. Experimental denoising results indicate that the transform performs the same or even better than several related multiresolution transforms, while having a significantly lower redundancy factor.

GPU accelerated manifold correction method for spinning compact binaries

NASA Astrophysics Data System (ADS)

Ran, Chong-xi; Liu, Song; Zhong, Shuang-ying

2018-04-01

The graphics processing unit (GPU) acceleration of the manifold correction algorithm based on the compute unified device architecture (CUDA) technology is designed to simulate the dynamic evolution of the Post-Newtonian (PN) Hamiltonian formulation of spinning compact binaries. The feasibility and the efficiency of parallel computation on GPU have been confirmed by various numerical experiments. The numerical comparisons show that the accuracy on GPU execution of manifold corrections method has a good agreement with the execution of codes on merely central processing unit (CPU-based) method. The acceleration ability when the codes are implemented on GPU can increase enormously through the use of shared memory and register optimization techniques without additional hardware costs, implying that the speedup is nearly 13 times as compared with the codes executed on CPU for phase space scan (including 314 × 314 orbits). In addition, GPU-accelerated manifold correction method is used to numerically study how dynamics are affected by the spin-induced quadrupole-monopole interaction for black hole binary system.

A fast ultrasonic simulation tool based on massively parallel implementations

NASA Astrophysics Data System (ADS)

Lambert, Jason; Rougeron, Gilles; Lacassagne, Lionel; Chatillon, Sylvain

2014-02-01

This paper presents a CIVA optimized ultrasonic inspection simulation tool, which takes benefit of the power of massively parallel architectures: graphical processing units (GPU) and multi-core general purpose processors (GPP). This tool is based on the classical approach used in CIVA: the interaction model is based on Kirchoff, and the ultrasonic field around the defect is computed by the pencil method. The model has been adapted and parallelized for both architectures. At this stage, the configurations addressed by the tool are : multi and mono-element probes, planar specimens made of simple isotropic materials, planar rectangular defects or side drilled holes of small diameter. Validations on the model accuracy and performances measurements are presented.

Simulation study of core heating properties for recent FIREX-I experiments

NASA Astrophysics Data System (ADS)

Johzaki, Tomoyuki; Kai, Yusuke; Endo, Takuma; Nagatomo, Hideo; Sunahara, Atsushi; Sentoku, Yasuhiko; Taguchi, Toshihiro; Fujioka, Shinsuke; Shiraga, Hiroyuki; Azechi, Hiroshi; Firex Project Team

2016-10-01

The demonstration of efficient core heating is the main purpose of FIREX-I project, where Au cone-attached solid ball CD target is used. For the guiding of fast electron beam generated by relativistic laser plasma interactions, the kilo-Tesla-class longitudinal magnetic field is applied by a capacitor-coil target and kJ-class ns-durration high power laser. In addition, to reduce the collisional effect (energy loss and scattering of fast electrons) during propagation in the Au cone tip, we introduced opened-tip cone (tipless cone). To evaluate the core heating properties, we carried out the integrated simulations, which shows the enhancement of core heating efficiency due to the magnetic guiding and opened-tip cone by a factor of three. These simulation results will be shown and be compared with the experimental results. JSPS KAKENHI (26400532, 15H03758, 16H02245, 15K21767), NIFS Collaboration Research program (NIFS12KUGK05, NIFS14KNSS054), and FIREX project.

Sparse matrix beamforming and image reconstruction for 2-D HIFU monitoring using harmonic motion imaging for focused ultrasound (HMIFU) with in vitro validation.

PubMed

Hou, Gary Y; Provost, Jean; Grondin, Julien; Wang, Shutao; Marquet, Fabrice; Bunting, Ethan; Konofagou, Elisa E

2014-11-01

Harmonic motion imaging for focused ultrasound (HMIFU) utilizes an amplitude-modulated HIFU beam to induce a localized focal oscillatory motion simultaneously estimated. The objective of this study is to develop and show the feasibility of a novel fast beamforming algorithm for image reconstruction using GPU-based sparse-matrix operation with real-time feedback. In this study, the algorithm was implemented onto a fully integrated, clinically relevant HMIFU system. A single divergent transmit beam was used while fast beamforming was implemented using a GPU-based delay-and-sum method and a sparse-matrix operation. Axial HMI displacements were then estimated from the RF signals using a 1-D normalized cross-correlation method and streamed to a graphic user interface with frame rates up to 15 Hz, a 100-fold increase compared to conventional CPU-based processing. The real-time feedback rate does not require interrupting the HIFU treatment. Results in phantom experiments showed reproducible HMI images and monitoring of 22 in vitro HIFU treatments using the new 2-D system demonstrated reproducible displacement imaging, and monitoring of 22 in vitro HIFU treatments using the new 2-D system showed a consistent average focal displacement decrease of 46.7 ±14.6% during lesion formation. Complementary focal temperature monitoring also indicated an average rate of displacement increase and decrease with focal temperature at 0.84±1.15%/(°)C, and 2.03±0.93%/(°)C , respectively. These results reinforce the HMIFU capability of estimating and monitoring stiffness related changes in real time. Current ongoing studies include clinical translation of the presented system for monitoring of HIFU treatment for breast and pancreatic tumor applications.

CUDA Optimization Strategies for Compute- and Memory-Bound Neuroimaging Algorithms

PubMed Central

Lee, Daren; Dinov, Ivo; Dong, Bin; Gutman, Boris; Yanovsky, Igor; Toga, Arthur W.

2011-01-01

As neuroimaging algorithms and technology continue to grow faster than CPU performance in complexity and image resolution, data-parallel computing methods will be increasingly important. The high performance, data-parallel architecture of modern graphical processing units (GPUs) can reduce computational times by orders of magnitude. However, its massively threaded architecture introduces challenges when GPU resources are exceeded. This paper presents optimization strategies for compute- and memory-bound algorithms for the CUDA architecture. For compute-bound algorithms, the registers are reduced through variable reuse via shared memory and the data throughput is increased through heavier thread workloads and maximizing the thread configuration for a single thread block per multiprocessor. For memory-bound algorithms, fitting the data into the fast but limited GPU resources is achieved through reorganizing the data into self-contained structures and employing a multi-pass approach. Memory latencies are reduced by selecting memory resources whose cache performance are optimized for the algorithm's access patterns. We demonstrate the strategies on two computationally expensive algorithms and achieve optimized GPU implementations that perform up to 6× faster than unoptimized ones. Compared to CPU implementations, we achieve peak GPU speedups of 129× for the 3D unbiased nonlinear image registration technique and 93× for the non-local means surface denoising algorithm. PMID:21159404

BALSA: integrated secondary analysis for whole-genome and whole-exome sequencing, accelerated by GPU.

PubMed

Luo, Ruibang; Wong, Yiu-Lun; Law, Wai-Chun; Lee, Lap-Kei; Cheung, Jeanno; Liu, Chi-Man; Lam, Tak-Wah

2014-01-01

This paper reports an integrated solution, called BALSA, for the secondary analysis of next generation sequencing data; it exploits the computational power of GPU and an intricate memory management to give a fast and accurate analysis. From raw reads to variants (including SNPs and Indels), BALSA, using just a single computing node with a commodity GPU board, takes 5.5 h to process 50-fold whole genome sequencing (∼750 million 100 bp paired-end reads), or just 25 min for 210-fold whole exome sequencing. BALSA's speed is rooted at its parallel algorithms to effectively exploit a GPU to speed up processes like alignment, realignment and statistical testing. BALSA incorporates a 16-genotype model to support the calling of SNPs and Indels and achieves competitive variant calling accuracy and sensitivity when compared to the ensemble of six popular variant callers. BALSA also supports efficient identification of somatic SNVs and CNVs; experiments showed that BALSA recovers all the previously validated somatic SNVs and CNVs, and it is more sensitive for somatic Indel detection. BALSA outputs variants in VCF format. A pileup-like SNAPSHOT format, while maintaining the same fidelity as BAM in variant calling, enables efficient storage and indexing, and facilitates the App development of downstream analyses. BALSA is available at: http://sourceforge.net/p/balsa.

CUDA optimization strategies for compute- and memory-bound neuroimaging algorithms.

PubMed

Lee, Daren; Dinov, Ivo; Dong, Bin; Gutman, Boris; Yanovsky, Igor; Toga, Arthur W

2012-06-01

As neuroimaging algorithms and technology continue to grow faster than CPU performance in complexity and image resolution, data-parallel computing methods will be increasingly important. The high performance, data-parallel architecture of modern graphical processing units (GPUs) can reduce computational times by orders of magnitude. However, its massively threaded architecture introduces challenges when GPU resources are exceeded. This paper presents optimization strategies for compute- and memory-bound algorithms for the CUDA architecture. For compute-bound algorithms, the registers are reduced through variable reuse via shared memory and the data throughput is increased through heavier thread workloads and maximizing the thread configuration for a single thread block per multiprocessor. For memory-bound algorithms, fitting the data into the fast but limited GPU resources is achieved through reorganizing the data into self-contained structures and employing a multi-pass approach. Memory latencies are reduced by selecting memory resources whose cache performance are optimized for the algorithm's access patterns. We demonstrate the strategies on two computationally expensive algorithms and achieve optimized GPU implementations that perform up to 6× faster than unoptimized ones. Compared to CPU implementations, we achieve peak GPU speedups of 129× for the 3D unbiased nonlinear image registration technique and 93× for the non-local means surface denoising algorithm. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Parallelization and checkpointing of GPU applications through program transformation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Solano-Quinde, Lizandro Damian

2012-01-01

GPUs have emerged as a powerful tool for accelerating general-purpose applications. The availability of programming languages that makes writing general-purpose applications for running on GPUs tractable have consolidated GPUs as an alternative for accelerating general purpose applications. Among the areas that have benefited from GPU acceleration are: signal and image processing, computational fluid dynamics, quantum chemistry, and, in general, the High Performance Computing (HPC) Industry. In order to continue to exploit higher levels of parallelism with GPUs, multi-GPU systems are gaining popularity. In this context, single-GPU applications are parallelized for running in multi-GPU systems. Furthermore, multi-GPU systems help to solvemore » the GPU memory limitation for applications with large application memory footprint. Parallelizing single-GPU applications has been approached by libraries that distribute the workload at runtime, however, they impose execution overhead and are not portable. On the other hand, on traditional CPU systems, parallelization has been approached through application transformation at pre-compile time, which enhances the application to distribute the workload at application level and does not have the issues of library-based approaches. Hence, a parallelization scheme for GPU systems based on application transformation is needed. Like any computing engine of today, reliability is also a concern in GPUs. GPUs are vulnerable to transient and permanent failures. Current checkpoint/restart techniques are not suitable for systems with GPUs. Checkpointing for GPU systems present new and interesting challenges, primarily due to the natural differences imposed by the hardware design, the memory subsystem architecture, the massive number of threads, and the limited amount of synchronization among threads. Therefore, a checkpoint/restart technique suitable for GPU systems is needed. The goal of this work is to exploit higher levels of parallelism and to develop support for application-level fault tolerance in applications using multiple GPUs. Our techniques reduce the burden of enhancing single-GPU applications to support these features. To achieve our goal, this work designs and implements a framework for enhancing a single-GPU OpenCL application through application transformation.« less

The CUBLAS and CULA based GPU acceleration of adaptive finite element framework for bioluminescence tomography.

PubMed

Zhang, Bo; Yang, Xiang; Yang, Fei; Yang, Xin; Qin, Chenghu; Han, Dong; Ma, Xibo; Liu, Kai; Tian, Jie

2010-09-13

In molecular imaging (MI), especially the optical molecular imaging, bioluminescence tomography (BLT) emerges as an effective imaging modality for small animal imaging. The finite element methods (FEMs), especially the adaptive finite element (AFE) framework, play an important role in BLT. The processing speed of the FEMs and the AFE framework still needs to be improved, although the multi-thread CPU technology and the multi CPU technology have already been applied. In this paper, we for the first time introduce a new kind of acceleration technology to accelerate the AFE framework for BLT, using the graphics processing unit (GPU). Besides the processing speed, the GPU technology can get a balance between the cost and performance. The CUBLAS and CULA are two main important and powerful libraries for programming on NVIDIA GPUs. With the help of CUBLAS and CULA, it is easy to code on NVIDIA GPU and there is no need to worry about the details about the hardware environment of a specific GPU. The numerical experiments are designed to show the necessity, effect and application of the proposed CUBLAS and CULA based GPU acceleration. From the results of the experiments, we can reach the conclusion that the proposed CUBLAS and CULA based GPU acceleration method can improve the processing speed of the AFE framework very much while getting a balance between cost and performance.

Parallel Optimization of 3D Cardiac Electrophysiological Model Using GPU

PubMed Central

Xia, Yong; Zhang, Henggui

2015-01-01

Large-scale 3D virtual heart model simulations are highly demanding in computational resources. This imposes a big challenge to the traditional computation resources based on CPU environment, which already cannot meet the requirement of the whole computation demands or are not easily available due to expensive costs. GPU as a parallel computing environment therefore provides an alternative to solve the large-scale computational problems of whole heart modeling. In this study, using a 3D sheep atrial model as a test bed, we developed a GPU-based simulation algorithm to simulate the conduction of electrical excitation waves in the 3D atria. In the GPU algorithm, a multicellular tissue model was split into two components: one is the single cell model (ordinary differential equation) and the other is the diffusion term of the monodomain model (partial differential equation). Such a decoupling enabled realization of the GPU parallel algorithm. Furthermore, several optimization strategies were proposed based on the features of the virtual heart model, which enabled a 200-fold speedup as compared to a CPU implementation. In conclusion, an optimized GPU algorithm has been developed that provides an economic and powerful platform for 3D whole heart simulations. PMID:26581957

Parallel Optimization of 3D Cardiac Electrophysiological Model Using GPU.

PubMed

Xia, Yong; Wang, Kuanquan; Zhang, Henggui

2015-01-01

Large-scale 3D virtual heart model simulations are highly demanding in computational resources. This imposes a big challenge to the traditional computation resources based on CPU environment, which already cannot meet the requirement of the whole computation demands or are not easily available due to expensive costs. GPU as a parallel computing environment therefore provides an alternative to solve the large-scale computational problems of whole heart modeling. In this study, using a 3D sheep atrial model as a test bed, we developed a GPU-based simulation algorithm to simulate the conduction of electrical excitation waves in the 3D atria. In the GPU algorithm, a multicellular tissue model was split into two components: one is the single cell model (ordinary differential equation) and the other is the diffusion term of the monodomain model (partial differential equation). Such a decoupling enabled realization of the GPU parallel algorithm. Furthermore, several optimization strategies were proposed based on the features of the virtual heart model, which enabled a 200-fold speedup as compared to a CPU implementation. In conclusion, an optimized GPU algorithm has been developed that provides an economic and powerful platform for 3D whole heart simulations.

Lossless data compression for improving the performance of a GPU-based beamformer.

PubMed

Lok, U-Wai; Fan, Gang-Wei; Li, Pai-Chi

2015-04-01

The powerful parallel computation ability of a graphics processing unit (GPU) makes it feasible to perform dynamic receive beamforming However, a real time GPU-based beamformer requires high data rate to transfer radio-frequency (RF) data from hardware to software memory, as well as from central processing unit (CPU) to GPU memory. There are data compression methods (e.g. Joint Photographic Experts Group (JPEG)) available for the hardware front end to reduce data size, alleviating the data transfer requirement of the hardware interface. Nevertheless, the required decoding time may even be larger than the transmission time of its original data, in turn degrading the overall performance of the GPU-based beamformer. This article proposes and implements a lossless compression-decompression algorithm, which enables in parallel compression and decompression of data. By this means, the data transfer requirement of hardware interface and the transmission time of CPU to GPU data transfers are reduced, without sacrificing image quality. In simulation results, the compression ratio reached around 1.7. The encoder design of our lossless compression approach requires low hardware resources and reasonable latency in a field programmable gate array. In addition, the transmission time of transferring data from CPU to GPU with the parallel decoding process improved by threefold, as compared with transferring original uncompressed data. These results show that our proposed lossless compression plus parallel decoder approach not only mitigate the transmission bandwidth requirement to transfer data from hardware front end to software system but also reduce the transmission time for CPU to GPU data transfer. © The Author(s) 2014.

Monte Carlo study of the effects of system geometry and antiscatter grids on cone-beam CT scatter distributions

PubMed Central

Sisniega, A.; Zbijewski, W.; Badal, A.; Kyprianou, I. S.; Stayman, J. W.; Vaquero, J. J.; Siewerdsen, J. H.

2013-01-01

Purpose: The proliferation of cone-beam CT (CBCT) has created interest in performance optimization, with x-ray scatter identified among the main limitations to image quality. CBCT often contends with elevated scatter, but the wide variety of imaging geometry in different CBCT configurations suggests that not all configurations are affected to the same extent. Graphics processing unit (GPU) accelerated Monte Carlo (MC) simulations are employed over a range of imaging geometries to elucidate the factors governing scatter characteristics, efficacy of antiscatter grids, guide system design, and augment development of scatter correction. Methods: A MC x-ray simulator implemented on GPU was accelerated by inclusion of variance reduction techniques (interaction splitting, forced scattering, and forced detection) and extended to include x-ray spectra and analytical models of antiscatter grids and flat-panel detectors. The simulator was applied to small animal (SA), musculoskeletal (MSK) extremity, otolaryngology (Head), breast, interventional C-arm, and on-board (kilovoltage) linear accelerator (Linac) imaging, with an axis-to-detector distance (ADD) of 5, 12, 22, 32, 60, and 50 cm, respectively. Each configuration was modeled with and without an antiscatter grid and with (i) an elliptical cylinder varying 70–280 mm in major axis; and (ii) digital murine and anthropomorphic models. The effects of scatter were evaluated in terms of the angular distribution of scatter incident upon the detector, scatter-to-primary ratio (SPR), artifact magnitude, contrast, contrast-to-noise ratio (CNR), and visual assessment. Results: Variance reduction yielded improvements in MC simulation efficiency ranging from ∼17-fold (for SA CBCT) to ∼35-fold (for Head and C-arm), with the most significant acceleration due to interaction splitting (∼6 to ∼10-fold increase in efficiency). The benefit of a more extended geometry was evident by virtue of a larger air gap—e.g., for a 16 cm diameter object, the SPR reduced from 1.5 for ADD = 12 cm (MSK geometry) to 1.1 for ADD = 22 cm (Head) and to 0.5 for ADD = 60 cm (C-arm). Grid efficiency was higher for configurations with shorter air gap due to a broader angular distribution of scattered photons—e.g., scatter rejection factor ∼0.8 for MSK geometry versus ∼0.65 for C-arm. Grids reduced cupping for all configurations but had limited improvement on scatter-induced streaks and resulted in a loss of CNR for the SA, Breast, and C-arm. Relative contribution of forward-directed scatter increased with a grid (e.g., Rayleigh scatter fraction increasing from ∼0.15 without a grid to ∼0.25 with a grid for the MSK configuration), resulting in scatter distributions with greater spatial variation (the form of which depended on grid orientation). Conclusions: A fast MC simulator combining GPU acceleration with variance reduction provided a systematic examination of a range of CBCT configurations in relation to scatter, highlighting the magnitude and spatial uniformity of individual scatter components, illustrating tradeoffs in CNR and artifacts and identifying the system geometries for which grids are more beneficial (e.g., MSK) from those in which an extended geometry is the better defense (e.g., C-arm head imaging). Compact geometries with an antiscatter grid challenge assumptions of slowly varying scatter distributions due to increased contribution of Rayleigh scatter. PMID:23635285

A survey of GPU-based medical image computing techniques

PubMed Central

Shi, Lin; Liu, Wen; Zhang, Heye; Xie, Yongming

2012-01-01

Medical imaging currently plays a crucial role throughout the entire clinical applications from medical scientific research to diagnostics and treatment planning. However, medical imaging procedures are often computationally demanding due to the large three-dimensional (3D) medical datasets to process in practical clinical applications. With the rapidly enhancing performances of graphics processors, improved programming support, and excellent price-to-performance ratio, the graphics processing unit (GPU) has emerged as a competitive parallel computing platform for computationally expensive and demanding tasks in a wide range of medical image applications. The major purpose of this survey is to provide a comprehensive reference source for the starters or researchers involved in GPU-based medical image processing. Within this survey, the continuous advancement of GPU computing is reviewed and the existing traditional applications in three areas of medical image processing, namely, segmentation, registration and visualization, are surveyed. The potential advantages and associated challenges of current GPU-based medical imaging are also discussed to inspire future applications in medicine. PMID:23256080

Optimum hot electron production with low-density foams for laser fusion by fast ignition.

PubMed

Lei, A L; Tanaka, K A; Kodama, R; Kumar, G R; Nagai, K; Norimatsu, T; Yabuuchi, T; Mima, K

2006-06-30

We propose a foam cone-in-shell target design aiming at optimum hot electron production for the fast ignition. A thin low-density foam is proposed to cover the inner tip of a gold cone inserted in a fuel shell. An intense laser is then focused on the foam to generate hot electrons for the fast ignition. Element experiments demonstrate increased laser energy coupling efficiency into hot electrons without increasing the electron temperature and beam divergence with foam coated targets in comparison with solid targets. This may enhance the laser energy deposition in the compressed fuel plasma.

Optimized x-ray source scanning trajectories for iterative reconstruction in high cone-angle tomography

NASA Astrophysics Data System (ADS)

Kingston, Andrew M.; Myers, Glenn R.; Latham, Shane J.; Li, Heyang; Veldkamp, Jan P.; Sheppard, Adrian P.

2016-10-01

With the GPU computing becoming main-stream, iterative tomographic reconstruction (IR) is becoming a com- putationally viable alternative to traditional single-shot analytical methods such as filtered back-projection. IR liberates one from the continuous X-ray source trajectories required for analytical reconstruction. We present a family of novel X-ray source trajectories for large-angle CBCT. These discrete (sparsely sampled) trajectories optimally fill the space of possible source locations by maximising the degree of mutually independent information. They satisfy a discrete equivalent of Tuy's sufficiency condition and allow high cone-angle (high-flux) tomog- raphy. The highly isotropic nature of the trajectory has several advantages: (1) The average source distance is approximately constant throughout the reconstruction volume, thus avoiding the differential-magnification artefacts that plague high cone-angle helical computed tomography; (2) Reduced streaking artifacts due to e.g. X-ray beam-hardening; (3) Misalignment and component motion manifests as blur in the tomogram rather than double-edges, which is easier to automatically correct; (4) An approximately shift-invariant point-spread-function which enables filtering as a pre-conditioner to speed IR convergence. We describe these space-filling trajectories and demonstrate their above-mentioned properties compared with a traditional helical trajectories.

Development of High-speed Visualization System of Hypocenter Data Using CUDA-based GPU computing

NASA Astrophysics Data System (ADS)

Kumagai, T.; Okubo, K.; Uchida, N.; Matsuzawa, T.; Kawada, N.; Takeuchi, N.

2014-12-01

After the Great East Japan Earthquake on March 11, 2011, intelligent visualization of seismic information is becoming important to understand the earthquake phenomena. On the other hand, to date, the quantity of seismic data becomes enormous as a progress of high accuracy observation network; we need to treat many parameters (e.g., positional information, origin time, magnitude, etc.) to efficiently display the seismic information. Therefore, high-speed processing of data and image information is necessary to handle enormous amounts of seismic data. Recently, GPU (Graphic Processing Unit) is used as an acceleration tool for data processing and calculation in various study fields. This movement is called GPGPU (General Purpose computing on GPUs). In the last few years the performance of GPU keeps on improving rapidly. GPU computing gives us the high-performance computing environment at a lower cost than before. Moreover, use of GPU has an advantage of visualization of processed data, because GPU is originally architecture for graphics processing. In the GPU computing, the processed data is always stored in the video memory. Therefore, we can directly write drawing information to the VRAM on the video card by combining CUDA and the graphics API. In this study, we employ CUDA and OpenGL and/or DirectX to realize full-GPU implementation. This method makes it possible to write drawing information to the VRAM on the video card without PCIe bus data transfer: It enables the high-speed processing of seismic data. The present study examines the GPU computing-based high-speed visualization and the feasibility for high-speed visualization system of hypocenter data.

hybrid\\scriptsize{{MANTIS}}: a CPU-GPU Monte Carlo method for modeling indirect x-ray detectors with columnar scintillators

NASA Astrophysics Data System (ADS)

Sharma, Diksha; Badal, Andreu; Badano, Aldo

2012-04-01

The computational modeling of medical imaging systems often requires obtaining a large number of simulated images with low statistical uncertainty which translates into prohibitive computing times. We describe a novel hybrid approach for Monte Carlo simulations that maximizes utilization of CPUs and GPUs in modern workstations. We apply the method to the modeling of indirect x-ray detectors using a new and improved version of the code \\scriptsize{{MANTIS}}, an open source software tool used for the Monte Carlo simulations of indirect x-ray imagers. We first describe a GPU implementation of the physics and geometry models in fast\\scriptsize{{DETECT}}2 (the optical transport model) and a serial CPU version of the same code. We discuss its new features like on-the-fly column geometry and columnar crosstalk in relation to the \\scriptsize{{MANTIS}} code, and point out areas where our model provides more flexibility for the modeling of realistic columnar structures in large area detectors. Second, we modify \\scriptsize{{PENELOPE}} (the open source software package that handles the x-ray and electron transport in \\scriptsize{{MANTIS}}) to allow direct output of location and energy deposited during x-ray and electron interactions occurring within the scintillator. This information is then handled by optical transport routines in fast\\scriptsize{{DETECT}}2. A load balancer dynamically allocates optical transport showers to the GPU and CPU computing cores. Our hybrid\\scriptsize{{MANTIS}} approach achieves a significant speed-up factor of 627 when compared to \\scriptsize{{MANTIS}} and of 35 when compared to the same code running only in a CPU instead of a GPU. Using hybrid\\scriptsize{{MANTIS}}, we successfully hide hours of optical transport time by running it in parallel with the x-ray and electron transport, thus shifting the computational bottleneck from optical to x-ray transport. The new code requires much less memory than \\scriptsize{{MANTIS}} and, as a result, allows us to efficiently simulate large area detectors.

A fast CT reconstruction scheme for a general multi-core PC.

PubMed

Zeng, Kai; Bai, Erwei; Wang, Ge

2007-01-01

Expensive computational cost is a severe limitation in CT reconstruction for clinical applications that need real-time feedback. A primary example is bolus-chasing computed tomography (CT) angiography (BCA) that we have been developing for the past several years. To accelerate the reconstruction process using the filtered backprojection (FBP) method, specialized hardware or graphics cards can be used. However, specialized hardware is expensive and not flexible. The graphics processing unit (GPU) in a current graphic card can only reconstruct images in a reduced precision and is not easy to program. In this paper, an acceleration scheme is proposed based on a multi-core PC. In the proposed scheme, several techniques are integrated, including utilization of geometric symmetry, optimization of data structures, single-instruction multiple-data (SIMD) processing, multithreaded computation, and an Intel C++ compilier. Our scheme maintains the original precision and involves no data exchange between the GPU and CPU. The merits of our scheme are demonstrated in numerical experiments against the traditional implementation. Our scheme achieves a speedup of about 40, which can be further improved by several folds using the latest quad-core processors.

A Fast CT Reconstruction Scheme for a General Multi-Core PC

PubMed Central

Zeng, Kai; Bai, Erwei; Wang, Ge

2007-01-01

Expensive computational cost is a severe limitation in CT reconstruction for clinical applications that need real-time feedback. A primary example is bolus-chasing computed tomography (CT) angiography (BCA) that we have been developing for the past several years. To accelerate the reconstruction process using the filtered backprojection (FBP) method, specialized hardware or graphics cards can be used. However, specialized hardware is expensive and not flexible. The graphics processing unit (GPU) in a current graphic card can only reconstruct images in a reduced precision and is not easy to program. In this paper, an acceleration scheme is proposed based on a multi-core PC. In the proposed scheme, several techniques are integrated, including utilization of geometric symmetry, optimization of data structures, single-instruction multiple-data (SIMD) processing, multithreaded computation, and an Intel C++ compilier. Our scheme maintains the original precision and involves no data exchange between the GPU and CPU. The merits of our scheme are demonstrated in numerical experiments against the traditional implementation. Our scheme achieves a speedup of about 40, which can be further improved by several folds using the latest quad-core processors. PMID:18256731

A GPU-Based Architecture for Real-Time Data Assessment at Synchrotron Experiments

NASA Astrophysics Data System (ADS)

Chilingaryan, Suren; Mirone, Alessandro; Hammersley, Andrew; Ferrero, Claudio; Helfen, Lukas; Kopmann, Andreas; Rolo, Tomy dos Santos; Vagovic, Patrik

2011-08-01

Advances in digital detector technology leads presently to rapidly increasing data rates in imaging experiments. Using fast two-dimensional detectors in computed tomography, the data acquisition can be much faster than the reconstruction if no adequate measures are taken, especially when a high photon flux at synchrotron sources is used. We have optimized the reconstruction software employed at the micro-tomography beamlines of our synchrotron facilities to use the computational power of modern graphic cards. The main paradigm of our approach is the full utilization of all system resources. We use a pipelined architecture, where the GPUs are used as compute coprocessors to reconstruct slices, while the CPUs are preparing the next ones. Special attention is devoted to minimize data transfers between the host and GPU memory and to execute memory transfers in parallel with the computations. We were able to reduce the reconstruction time by a factor 30 and process a typical data set of 20 GB in 40 seconds. The time needed for the first evaluation of the reconstructed sample is reduced significantly and quasi real-time visualization is now possible.

SU-E-T-29: A Web Application for GPU-Based Monte Carlo IMRT/VMAT QA with Delivered Dose Verification

DOE Office of Scientific and Technical Information (OSTI.GOV)

Folkerts, M; University of California, San Diego, La Jolla, CA; Graves, Y

Purpose: To enable an existing web application for GPU-based Monte Carlo (MC) 3D dosimetry quality assurance (QA) to compute “delivered dose” from linac logfile data. Methods: We added significant features to an IMRT/VMAT QA web application which is based on existing technologies (HTML5, Python, and Django). This tool interfaces with python, c-code libraries, and command line-based GPU applications to perform a MC-based IMRT/VMAT QA. The web app automates many complicated aspects of interfacing clinical DICOM and logfile data with cutting-edge GPU software to run a MC dose calculation. The resultant web app is powerful, easy to use, and is ablemore » to re-compute both plan dose (from DICOM data) and delivered dose (from logfile data). Both dynalog and trajectorylog file formats are supported. Users upload zipped DICOM RP, CT, and RD data and set the expected statistic uncertainty for the MC dose calculation. A 3D gamma index map, 3D dose distribution, gamma histogram, dosimetric statistics, and DVH curves are displayed to the user. Additional the user may upload the delivery logfile data from the linac to compute a 'delivered dose' calculation and corresponding gamma tests. A comprehensive PDF QA report summarizing the results can also be downloaded. Results: We successfully improved a web app for a GPU-based QA tool that consists of logfile parcing, fluence map generation, CT image processing, GPU based MC dose calculation, gamma index calculation, and DVH calculation. The result is an IMRT and VMAT QA tool that conducts an independent dose calculation for a given treatment plan and delivery log file. The system takes both DICOM data and logfile data to compute plan dose and delivered dose respectively. Conclusion: We sucessfully improved a GPU-based MC QA tool to allow for logfile dose calculation. The high efficiency and accessibility will greatly facilitate IMRT and VMAT QA.« less

Fabrication of aerogel capsule, bromine-doped capsule, and modified gold cone in modified target for the Fast Ignition Realization Experiment (FIREX) Project

NASA Astrophysics Data System (ADS)

Nagai, Keiji; Yang, H.; Norimatsu, T.; Azechi, H.; Belkada, F.; Fujimoto, Y.; Fujimura, T.; Fujioka, K.; Fujioka, S.; Homma, H.; Ito, F.; Iwamoto, A.; Jitsuno, T.; Kaneyasu, Y.; Nakai, M.; Nemoto, N.; Saika, H.; Shimoyama, T.; Suzuki, Y.; Yamanaka, K.; Mima, K.

2009-09-01

The development of target fabrication for the Fast Ignition Realization EXperiment (FIREX) Project is described in this paper. For the first stage of the FIREX Project (FIREX-I), the previously designed target has been modified by using a bromine-doped ablator and coating the inner gold cone with a low-density material. A high-quality bromine-doped capsule without vacuoles was fabricated from bromine-doped deuterated polystyrene. The gold surface was coated with a low-density material by electrochemical plating. For the cryogenic fuel target, a brand new type of aerogel material, phloroglucinol/formaldehyde (PF), was investigated and encapsulated to meet the specifications of 500 µm diameter and 20 µm thickness, with 30 nm nanopores. Polystyrene-based low-density materials were investigated and the relationship between the crosslinker content and the nanopore structure was observed.

Implementation of GPU accelerated SPECT reconstruction with Monte Carlo-based scatter correction.

PubMed

Bexelius, Tobias; Sohlberg, Antti

2018-06-01

Statistical SPECT reconstruction can be very time-consuming especially when compensations for collimator and detector response, attenuation, and scatter are included in the reconstruction. This work proposes an accelerated SPECT reconstruction algorithm based on graphics processing unit (GPU) processing. Ordered subset expectation maximization (OSEM) algorithm with CT-based attenuation modelling, depth-dependent Gaussian convolution-based collimator-detector response modelling, and Monte Carlo-based scatter compensation was implemented using OpenCL. The OpenCL implementation was compared against the existing multi-threaded OSEM implementation running on a central processing unit (CPU) in terms of scatter-to-primary ratios, standardized uptake values (SUVs), and processing speed using mathematical phantoms and clinical multi-bed bone SPECT/CT studies. The difference in scatter-to-primary ratios, visual appearance, and SUVs between GPU and CPU implementations was minor. On the other hand, at its best, the GPU implementation was noticed to be 24 times faster than the multi-threaded CPU version on a normal 128 × 128 matrix size 3 bed bone SPECT/CT data set when compensations for collimator and detector response, attenuation, and scatter were included. GPU SPECT reconstructions show great promise as an every day clinical reconstruction tool.

Sparse matrix beamforming and image reconstruction for real-time 2D HIFU monitoring using Harmonic Motion Imaging for Focused Ultrasound (HMIFU) with in vitro validation

PubMed Central

Hou, Gary Y.; Provost, Jean; Grondin, Julien; Wang, Shutao; Marquet, Fabrice; Bunting, Ethan; Konofagou, Elisa E.

2015-01-01

Harmonic Motion Imaging for Focused Ultrasound (HMIFU) is a recently developed High-Intensity Focused Ultrasound (HIFU) treatment monitoring method. HMIFU utilizes an Amplitude-Modulated (fAM = 25 Hz) HIFU beam to induce a localized focal oscillatory motion, which is simultaneously estimated and imaged by confocally-aligned imaging transducer. HMIFU feasibilities have been previously shown in silico, in vitro, and in vivo in 1-D or 2-D monitoring of HIFU treatment. The objective of this study is to develop and show the feasibility of a novel fast beamforming algorithm for image reconstruction using GPU-based sparse-matrix operation with real-time feedback. In this study, the algorithm was implemented onto a fully integrated, clinically relevant HMIFU system composed of a 93-element HIFU transducer (fcenter = 4.5MHz) and coaxially-aligned 64-element phased array (fcenter = 2.5MHz) for displacement excitation and motion estimation, respectively. A single transmit beam with divergent beam transmit was used while fast beamforming was implemented using a GPU-based delay-and-sum method and a sparse-matrix operation. Axial HMI displacements were then estimated from the RF signals using a 1-D normalized cross-correlation method and streamed to a graphic user interface. The present work developed and implemented a sparse matrix beamforming onto a fully-integrated, clinically relevant system, which can stream displacement images up to 15 Hz using a GPU-based processing, an increase of 100 fold in rate of streaming displacement images compared to conventional CPU-based conventional beamforming and reconstruction processing. The achieved feedback rate is also currently the fastest and only approach that does not require interrupting the HIFU treatment amongst the acoustic radiation force based HIFU imaging techniques. Results in phantom experiments showed reproducible displacement imaging, and monitoring of twenty two in vitro HIFU treatments using the new 2D system showed a consistent average focal displacement decrease of 46.7±14.6% during lesion formation. Complementary focal temperature monitoring also indicated an average rate of displacement increase and decrease with focal temperature at 0.84±1.15 %/ °C, and 2.03± 0.93%/ °C, respectively. These results reinforce the HMIFU capability of estimating and monitoring stiffness related changes in real time. Current ongoing studies include clinical translation of the presented system for monitoring of HIFU treatment for breast and pancreatic tumor applications. PMID:24960528

GraphReduce: Processing Large-Scale Graphs on Accelerator-Based Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sengupta, Dipanjan; Song, Shuaiwen; Agarwal, Kapil

2015-11-15

Recent work on real-world graph analytics has sought to leverage the massive amount of parallelism offered by GPU devices, but challenges remain due to the inherent irregularity of graph algorithms and limitations in GPU-resident memory for storing large graphs. We present GraphReduce, a highly efficient and scalable GPU-based framework that operates on graphs that exceed the device’s internal memory capacity. GraphReduce adopts a combination of edge- and vertex-centric implementations of the Gather-Apply-Scatter programming model and operates on multiple asynchronous GPU streams to fully exploit the high degrees of parallelism in GPUs with efficient graph data movement between the host andmore » device.« less

Understanding GPU Power. A Survey of Profiling, Modeling, and Simulation Methods

DOE PAGES

Bridges, Robert A.; Imam, Neena; Mintz, Tiffany M.

2016-09-01

Modern graphics processing units (GPUs) have complex architectures that admit exceptional performance and energy efficiency for high throughput applications.Though GPUs consume large amounts of power, their use for high throughput applications facilitate state-of-the-art energy efficiency and performance. Consequently, continued development relies on understanding their power consumption. Our work is a survey of GPU power modeling and profiling methods with increased detail on noteworthy efforts. Moreover, as direct measurement of GPU power is necessary for model evaluation and parameter initiation, internal and external power sensors are discussed. Hardware counters, which are low-level tallies of hardware events, share strong correlation to powermore » use and performance. Statistical correlation between power and performance counters has yielded worthwhile GPU power models, yet the complexity inherent to GPU architectures presents new hurdles for power modeling. Developments and challenges of counter-based GPU power modeling is discussed. Often building on the counter-based models, research efforts for GPU power simulation, which make power predictions from input code and hardware knowledge, provide opportunities for optimization in programming or architectural design. Noteworthy strides in power simulations for GPUs are included along with their performance or functional simulator counterparts when appropriate. Lastly, possible directions for future research are discussed.« less

Understanding GPU Power. A Survey of Profiling, Modeling, and Simulation Methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bridges, Robert A.; Imam, Neena; Mintz, Tiffany M.

Modern graphics processing units (GPUs) have complex architectures that admit exceptional performance and energy efficiency for high throughput applications.Though GPUs consume large amounts of power, their use for high throughput applications facilitate state-of-the-art energy efficiency and performance. Consequently, continued development relies on understanding their power consumption. Our work is a survey of GPU power modeling and profiling methods with increased detail on noteworthy efforts. Moreover, as direct measurement of GPU power is necessary for model evaluation and parameter initiation, internal and external power sensors are discussed. Hardware counters, which are low-level tallies of hardware events, share strong correlation to powermore » use and performance. Statistical correlation between power and performance counters has yielded worthwhile GPU power models, yet the complexity inherent to GPU architectures presents new hurdles for power modeling. Developments and challenges of counter-based GPU power modeling is discussed. Often building on the counter-based models, research efforts for GPU power simulation, which make power predictions from input code and hardware knowledge, provide opportunities for optimization in programming or architectural design. Noteworthy strides in power simulations for GPUs are included along with their performance or functional simulator counterparts when appropriate. Lastly, possible directions for future research are discussed.« less

Geometric artifacts reduction for cone-beam CT via L0-norm minimization without dedicated phantoms.

PubMed

Gong, Changcheng; Cai, Yufang; Zeng, Li

2018-01-01

For cone-beam computed tomography (CBCT), transversal shifts of the rotation center exist inevitably, which will result in geometric artifacts in CT images. In this work, we propose a novel geometric calibration method for CBCT, which can also be used in micro-CT. The symmetry property of the sinogram is used for the first calibration, and then L0-norm of the gradient image from the reconstructed image is used as the cost function to be minimized for the second calibration. An iterative search method is adopted to pursue the local minimum of the L0-norm minimization problem. The transversal shift value is updated with affirmatory step size within a search range determined by the first calibration. In addition, graphic processing unit (GPU)-based FDK algorithm and acceleration techniques are designed to accelerate the calibration process of the presented new method. In simulation experiments, the mean absolute difference (MAD) and the standard deviation (SD) of the transversal shift value were less than 0.2 pixels between the noise-free and noisy projection images, which indicated highly accurate calibration applying the new calibration method. In real data experiments, the smaller entropies of the corrected images also indicated that higher resolution image was acquired using the corrected projection data and the textures were well protected. Study results also support the feasibility of applying the proposed method to other imaging modalities.

Accelerating EPI distortion correction by utilizing a modern GPU-based parallel computation.

PubMed

Yang, Yao-Hao; Huang, Teng-Yi; Wang, Fu-Nien; Chuang, Tzu-Chao; Chen, Nan-Kuei

2013-04-01

The combination of phase demodulation and field mapping is a practical method to correct echo planar imaging (EPI) geometric distortion. However, since phase dispersion accumulates in each phase-encoding step, the calculation complexity of phase modulation is Ny-fold higher than conventional image reconstructions. Thus, correcting EPI images via phase demodulation is generally a time-consuming task. Parallel computing by employing general-purpose calculations on graphics processing units (GPU) can accelerate scientific computing if the algorithm is parallelized. This study proposes a method that incorporates the GPU-based technique into phase demodulation calculations to reduce computation time. The proposed parallel algorithm was applied to a PROPELLER-EPI diffusion tensor data set. The GPU-based phase demodulation method reduced the EPI distortion correctly, and accelerated the computation. The total reconstruction time of the 16-slice PROPELLER-EPI diffusion tensor images with matrix size of 128 × 128 was reduced from 1,754 seconds to 101 seconds by utilizing the parallelized 4-GPU program. GPU computing is a promising method to accelerate EPI geometric correction. The resulting reduction in computation time of phase demodulation should accelerate postprocessing for studies performed with EPI, and should effectuate the PROPELLER-EPI technique for clinical practice. Copyright © 2011 by the American Society of Neuroimaging.

Multi-GPU hybrid programming accelerated three-dimensional phase-field model in binary alloy

NASA Astrophysics Data System (ADS)

Zhu, Changsheng; Liu, Jieqiong; Zhu, Mingfang; Feng, Li

2018-03-01

In the process of dendritic growth simulation, the computational efficiency and the problem scales have extremely important influence on simulation efficiency of three-dimensional phase-field model. Thus, seeking for high performance calculation method to improve the computational efficiency and to expand the problem scales has a great significance to the research of microstructure of the material. A high performance calculation method based on MPI+CUDA hybrid programming model is introduced. Multi-GPU is used to implement quantitative numerical simulations of three-dimensional phase-field model in binary alloy under the condition of multi-physical processes coupling. The acceleration effect of different GPU nodes on different calculation scales is explored. On the foundation of multi-GPU calculation model that has been introduced, two optimization schemes, Non-blocking communication optimization and overlap of MPI and GPU computing optimization, are proposed. The results of two optimization schemes and basic multi-GPU model are compared. The calculation results show that the use of multi-GPU calculation model can improve the computational efficiency of three-dimensional phase-field obviously, which is 13 times to single GPU, and the problem scales have been expanded to 8193. The feasibility of two optimization schemes is shown, and the overlap of MPI and GPU computing optimization has better performance, which is 1.7 times to basic multi-GPU model, when 21 GPUs are used.

Initial development of goCMC: a GPU-oriented fast cross-platform Monte Carlo engine for carbon ion therapy

PubMed Central

Qin, Nan; Pinto, Marco; Tian, Zhen; Dedes, Georgios; Pompos, Arnold; Jiang, Steve B.; Parodi, Katia; Jia, Xun

2017-01-01

Monte Carlo (MC) simulation is considered as the most accurate method for calculation of absorbed dose and fundamental physics quantities related to biological effects in carbon ion therapy. To improve its computational efficiency, we have developed a GPU-oriented fast MC package named goCMC, for carbon therapy. goCMC simulates particle transport in voxelized geometry with kinetic energy up to 450 MeV/u. Class II condensed history simulation scheme with a continuous slowing down approximation was employed. Energy straggling and multiple scattering were modeled. δ-electrons were terminated with their energy locally deposited. Four types of nuclear interactions were implemented in goCMC, i.e., carbon-hydrogen, carbon-carbon, carbon-oxygen and carbon-calcium inelastic collisions. Total cross section data from Geant4 were used. Secondary particles produced in these interactions were sampled according to particle yield with energy and directional distribution data derived from Geant4 simulation results. Secondary charged particles were transported following the condensed history scheme, whereas secondary neutral particles were ignored. goCMC was developed under OpenCL framework and is executable on different platforms, e.g. GPU and multi-core CPU. We have validated goCMC with Geant4 in cases with different beam energy and phantoms including four homogeneous phantoms, one heterogeneous half-slab phantom, and one patient case. For each case 3 × 107 carbon ions were simulated, such that in the region with dose greater than 10% of maximum dose, the mean relative statistical uncertainty was less than 1%. Good agreements for dose distributions and range estimations between goCMC and Geant4 were observed. 3D gamma passing rates with 1%/1 mm criterion were over 90% within 10%) isodose line except in two extreme cases, and those with 2%/1 mm criterion were all over 96%. Efficiency and code portability were tested with different GPUs and CPUs. Depending on the beam energy and voxel size, the computation time to simulate 107 carbons was 9.9–125 sec, 2.5–50 sec and 60–612 sec on an AMD Radeon GPU card, an NVidia GeForce GTX 1080 GPU card and an Intel Xeon E5-2640 CPU, respectively. The combined accuracy, efficiency and portability make goCMC attractive for research and clinical applications in carbon ion therapy. PMID:28140352

Initial development of goCMC: a GPU-oriented fast cross-platform Monte Carlo engine for carbon ion therapy

NASA Astrophysics Data System (ADS)

Qin, Nan; Pinto, Marco; Tian, Zhen; Dedes, Georgios; Pompos, Arnold; Jiang, Steve B.; Parodi, Katia; Jia, Xun

2017-05-01

Monte Carlo (MC) simulation is considered as the most accurate method for calculation of absorbed dose and fundamental physics quantities related to biological effects in carbon ion therapy. To improve its computational efficiency, we have developed a GPU-oriented fast MC package named goCMC, for carbon therapy. goCMC simulates particle transport in voxelized geometry with kinetic energy up to 450 MeV u-1. Class II condensed history simulation scheme with a continuous slowing down approximation was employed. Energy straggling and multiple scattering were modeled. δ-electrons were terminated with their energy locally deposited. Four types of nuclear interactions were implemented in goCMC, i.e. carbon-hydrogen, carbon-carbon, carbon-oxygen and carbon-calcium inelastic collisions. Total cross section data from Geant4 were used. Secondary particles produced in these interactions were sampled according to particle yield with energy and directional distribution data derived from Geant4 simulation results. Secondary charged particles were transported following the condensed history scheme, whereas secondary neutral particles were ignored. goCMC was developed under OpenCL framework and is executable on different platforms, e.g. GPU and multi-core CPU. We have validated goCMC with Geant4 in cases with different beam energy and phantoms including four homogeneous phantoms, one heterogeneous half-slab phantom, and one patient case. For each case 3× {{10}7} carbon ions were simulated, such that in the region with dose greater than 10% of maximum dose, the mean relative statistical uncertainty was less than 1%. Good agreements for dose distributions and range estimations between goCMC and Geant4 were observed. 3D gamma passing rates with 1%/1 mm criterion were over 90% within 10% isodose line except in two extreme cases, and those with 2%/1 mm criterion were all over 96%. Efficiency and code portability were tested with different GPUs and CPUs. Depending on the beam energy and voxel size, the computation time to simulate {{10}7} carbons was 9.9-125 s, 2.5-50 s and 60-612 s on an AMD Radeon GPU card, an NVidia GeForce GTX 1080 GPU card and an Intel Xeon E5-2640 CPU, respectively. The combined accuracy, efficiency and portability make goCMC attractive for research and clinical applications in carbon ion therapy.

Initial development of goCMC: a GPU-oriented fast cross-platform Monte Carlo engine for carbon ion therapy.

PubMed

Qin, Nan; Pinto, Marco; Tian, Zhen; Dedes, Georgios; Pompos, Arnold; Jiang, Steve B; Parodi, Katia; Jia, Xun

2017-05-07

Monte Carlo (MC) simulation is considered as the most accurate method for calculation of absorbed dose and fundamental physics quantities related to biological effects in carbon ion therapy. To improve its computational efficiency, we have developed a GPU-oriented fast MC package named goCMC, for carbon therapy. goCMC simulates particle transport in voxelized geometry with kinetic energy up to 450 MeV u -1 . Class II condensed history simulation scheme with a continuous slowing down approximation was employed. Energy straggling and multiple scattering were modeled. δ-electrons were terminated with their energy locally deposited. Four types of nuclear interactions were implemented in goCMC, i.e. carbon-hydrogen, carbon-carbon, carbon-oxygen and carbon-calcium inelastic collisions. Total cross section data from Geant4 were used. Secondary particles produced in these interactions were sampled according to particle yield with energy and directional distribution data derived from Geant4 simulation results. Secondary charged particles were transported following the condensed history scheme, whereas secondary neutral particles were ignored. goCMC was developed under OpenCL framework and is executable on different platforms, e.g. GPU and multi-core CPU. We have validated goCMC with Geant4 in cases with different beam energy and phantoms including four homogeneous phantoms, one heterogeneous half-slab phantom, and one patient case. For each case [Formula: see text] carbon ions were simulated, such that in the region with dose greater than 10% of maximum dose, the mean relative statistical uncertainty was less than 1%. Good agreements for dose distributions and range estimations between goCMC and Geant4 were observed. 3D gamma passing rates with 1%/1 mm criterion were over 90% within 10% isodose line except in two extreme cases, and those with 2%/1 mm criterion were all over 96%. Efficiency and code portability were tested with different GPUs and CPUs. Depending on the beam energy and voxel size, the computation time to simulate [Formula: see text] carbons was 9.9-125 s, 2.5-50 s and 60-612 s on an AMD Radeon GPU card, an NVidia GeForce GTX 1080 GPU card and an Intel Xeon E5-2640 CPU, respectively. The combined accuracy, efficiency and portability make goCMC attractive for research and clinical applications in carbon ion therapy.

Gravitational tree-code on graphics processing units: implementation in CUDA

NASA Astrophysics Data System (ADS)

Gaburov, Evghenii; Bédorf, Jeroen; Portegies Zwart, Simon

2010-05-01

We present a new very fast tree-code which runs on massively parallel Graphical Processing Units (GPU) with NVIDIA CUDA architecture. The tree-construction and calculation of multipole moments is carried out on the host CPU, while the force calculation which consists of tree walks and evaluation of interaction list is carried out on the GPU. In this way we achieve a sustained performance of about 100GFLOP/s and data transfer rates of about 50GB/s. It takes about a second to compute forces on a million particles with an opening angle of θ ≈ 0.5. The code has a convenient user interface and is freely available for use. http://castle.strw.leidenuniv.nl/software/octgrav.html

Performance evaluation of throughput computing workloads using multi-core processors and graphics processors

NASA Astrophysics Data System (ADS)

Dave, Gaurav P.; Sureshkumar, N.; Blessy Trencia Lincy, S. S.

2017-11-01

Current trend in processor manufacturing focuses on multi-core architectures rather than increasing the clock speed for performance improvement. Graphic processors have become as commodity hardware for providing fast co-processing in computer systems. Developments in IoT, social networking web applications, big data created huge demand for data processing activities and such kind of throughput intensive applications inherently contains data level parallelism which is more suited for SIMD architecture based GPU. This paper reviews the architectural aspects of multi/many core processors and graphics processors. Different case studies are taken to compare performance of throughput computing applications using shared memory programming in OpenMP and CUDA API based programming.

Study on efficiency of time computation in x-ray imaging simulation base on Monte Carlo algorithm using graphics processing unit

DOE Office of Scientific and Technical Information (OSTI.GOV)

Setiani, Tia Dwi, E-mail: tiadwisetiani@gmail.com; Suprijadi; Nuclear Physics and Biophysics Reaserch Division, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung Jalan Ganesha 10 Bandung, 40132

Monte Carlo (MC) is one of the powerful techniques for simulation in x-ray imaging. MC method can simulate the radiation transport within matter with high accuracy and provides a natural way to simulate radiation transport in complex systems. One of the codes based on MC algorithm that are widely used for radiographic images simulation is MC-GPU, a codes developed by Andrea Basal. This study was aimed to investigate the time computation of x-ray imaging simulation in GPU (Graphics Processing Unit) compared to a standard CPU (Central Processing Unit). Furthermore, the effect of physical parameters to the quality of radiographic imagesmore » and the comparison of image quality resulted from simulation in the GPU and CPU are evaluated in this paper. The simulations were run in CPU which was simulated in serial condition, and in two GPU with 384 cores and 2304 cores. In simulation using GPU, each cores calculates one photon, so, a large number of photon were calculated simultaneously. Results show that the time simulations on GPU were significantly accelerated compared to CPU. The simulations on the 2304 core of GPU were performed about 64 -114 times faster than on CPU, while the simulation on the 384 core of GPU were performed about 20 – 31 times faster than in a single core of CPU. Another result shows that optimum quality of images from the simulation was gained at the history start from 10{sup 8} and the energy from 60 Kev to 90 Kev. Analyzed by statistical approach, the quality of GPU and CPU images are relatively the same.« less

GPU accelerated Monte-Carlo simulation of SEM images for metrology

NASA Astrophysics Data System (ADS)

Verduin, T.; Lokhorst, S. R.; Hagen, C. W.

2016-03-01

In this work we address the computation times of numerical studies in dimensional metrology. In particular, full Monte-Carlo simulation programs for scanning electron microscopy (SEM) image acquisition are known to be notoriously slow. Our quest in reducing the computation time of SEM image simulation has led us to investigate the use of graphics processing units (GPUs) for metrology. We have succeeded in creating a full Monte-Carlo simulation program for SEM images, which runs entirely on a GPU. The physical scattering models of this GPU simulator are identical to a previous CPU-based simulator, which includes the dielectric function model for inelastic scattering and also refinements for low-voltage SEM applications. As a case study for the performance, we considered the simulated exposure of a complex feature: an isolated silicon line with rough sidewalls located on a at silicon substrate. The surface of the rough feature is decomposed into 408 012 triangles. We have used an exposure dose of 6 mC/cm2, which corresponds to 6 553 600 primary electrons on average (Poisson distributed). We repeat the simulation for various primary electron energies, 300 eV, 500 eV, 800 eV, 1 keV, 3 keV and 5 keV. At first we run the simulation on a GeForce GTX480 from NVIDIA. The very same simulation is duplicated on our CPU-based program, for which we have used an Intel Xeon X5650. Apart from statistics in the simulation, no difference is found between the CPU and GPU simulated results. The GTX480 generates the images (depending on the primary electron energy) 350 to 425 times faster than a single threaded Intel X5650 CPU. Although this is a tremendous speedup, we actually have not reached the maximum throughput because of the limited amount of available memory on the GTX480. Nevertheless, the speedup enables the fast acquisition of simulated SEM images for metrology. We now have the potential to investigate case studies in CD-SEM metrology, which otherwise would take unreasonable amounts of computation time.

A GPU-accelerated Monte Carlo dose calculation platform and its application toward validating an MRI-guided radiation therapy beam model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Yuhe; Mazur, Thomas R.; Green, Olga

Purpose: The clinical commissioning of IMRT subject to a magnetic field is challenging. The purpose of this work is to develop a GPU-accelerated Monte Carlo dose calculation platform based on PENELOPE and then use the platform to validate a vendor-provided MRIdian head model toward quality assurance of clinical IMRT treatment plans subject to a 0.35 T magnetic field. Methods: PENELOPE was first translated from FORTRAN to C++ and the result was confirmed to produce equivalent results to the original code. The C++ code was then adapted to CUDA in a workflow optimized for GPU architecture. The original code was expandedmore » to include voxelized transport with Woodcock tracking, faster electron/positron propagation in a magnetic field, and several features that make gPENELOPE highly user-friendly. Moreover, the vendor-provided MRIdian head model was incorporated into the code in an effort to apply gPENELOPE as both an accurate and rapid dose validation system. A set of experimental measurements were performed on the MRIdian system to examine the accuracy of both the head model and gPENELOPE. Ultimately, gPENELOPE was applied toward independent validation of patient doses calculated by MRIdian’s KMC. Results: An acceleration factor of 152 was achieved in comparison to the original single-thread FORTRAN implementation with the original accuracy being preserved. For 16 treatment plans including stomach (4), lung (2), liver (3), adrenal gland (2), pancreas (2), spleen(1), mediastinum (1), and breast (1), the MRIdian dose calculation engine agrees with gPENELOPE with a mean gamma passing rate of 99.1% ± 0.6% (2%/2 mm). Conclusions: A Monte Carlo simulation platform was developed based on a GPU- accelerated version of PENELOPE. This platform was used to validate that both the vendor-provided head model and fast Monte Carlo engine used by the MRIdian system are accurate in modeling radiation transport in a patient using 2%/2 mm gamma criteria. Future applications of this platform will include dose validation and accumulation, IMRT optimization, and dosimetry system modeling for next generation MR-IGRT systems.« less

A GPU-accelerated Monte Carlo dose calculation platform and its application toward validating an MRI-guided radiation therapy beam model

PubMed Central

Wang, Yuhe; Mazur, Thomas R.; Green, Olga; Hu, Yanle; Li, Hua; Rodriguez, Vivian; Wooten, H. Omar; Yang, Deshan; Zhao, Tianyu; Mutic, Sasa; Li, H. Harold

2016-01-01

Purpose: The clinical commissioning of IMRT subject to a magnetic field is challenging. The purpose of this work is to develop a GPU-accelerated Monte Carlo dose calculation platform based on penelope and then use the platform to validate a vendor-provided MRIdian head model toward quality assurance of clinical IMRT treatment plans subject to a 0.35 T magnetic field. Methods: penelope was first translated from fortran to c++ and the result was confirmed to produce equivalent results to the original code. The c++ code was then adapted to cuda in a workflow optimized for GPU architecture. The original code was expanded to include voxelized transport with Woodcock tracking, faster electron/positron propagation in a magnetic field, and several features that make gpenelope highly user-friendly. Moreover, the vendor-provided MRIdian head model was incorporated into the code in an effort to apply gpenelope as both an accurate and rapid dose validation system. A set of experimental measurements were performed on the MRIdian system to examine the accuracy of both the head model and gpenelope. Ultimately, gpenelope was applied toward independent validation of patient doses calculated by MRIdian’s kmc. Results: An acceleration factor of 152 was achieved in comparison to the original single-thread fortran implementation with the original accuracy being preserved. For 16 treatment plans including stomach (4), lung (2), liver (3), adrenal gland (2), pancreas (2), spleen(1), mediastinum (1), and breast (1), the MRIdian dose calculation engine agrees with gpenelope with a mean gamma passing rate of 99.1% ± 0.6% (2%/2 mm). Conclusions: A Monte Carlo simulation platform was developed based on a GPU- accelerated version of penelope. This platform was used to validate that both the vendor-provided head model and fast Monte Carlo engine used by the MRIdian system are accurate in modeling radiation transport in a patient using 2%/2 mm gamma criteria. Future applications of this platform will include dose validation and accumulation, IMRT optimization, and dosimetry system modeling for next generation MR-IGRT systems. PMID:27370123

A GPU-accelerated Monte Carlo dose calculation platform and its application toward validating an MRI-guided radiation therapy beam model.

PubMed

Wang, Yuhe; Mazur, Thomas R; Green, Olga; Hu, Yanle; Li, Hua; Rodriguez, Vivian; Wooten, H Omar; Yang, Deshan; Zhao, Tianyu; Mutic, Sasa; Li, H Harold

2016-07-01

The clinical commissioning of IMRT subject to a magnetic field is challenging. The purpose of this work is to develop a GPU-accelerated Monte Carlo dose calculation platform based on penelope and then use the platform to validate a vendor-provided MRIdian head model toward quality assurance of clinical IMRT treatment plans subject to a 0.35 T magnetic field. penelope was first translated from fortran to c++ and the result was confirmed to produce equivalent results to the original code. The c++ code was then adapted to cuda in a workflow optimized for GPU architecture. The original code was expanded to include voxelized transport with Woodcock tracking, faster electron/positron propagation in a magnetic field, and several features that make gpenelope highly user-friendly. Moreover, the vendor-provided MRIdian head model was incorporated into the code in an effort to apply gpenelope as both an accurate and rapid dose validation system. A set of experimental measurements were performed on the MRIdian system to examine the accuracy of both the head model and gpenelope. Ultimately, gpenelope was applied toward independent validation of patient doses calculated by MRIdian's kmc. An acceleration factor of 152 was achieved in comparison to the original single-thread fortran implementation with the original accuracy being preserved. For 16 treatment plans including stomach (4), lung (2), liver (3), adrenal gland (2), pancreas (2), spleen(1), mediastinum (1), and breast (1), the MRIdian dose calculation engine agrees with gpenelope with a mean gamma passing rate of 99.1% ± 0.6% (2%/2 mm). A Monte Carlo simulation platform was developed based on a GPU- accelerated version of penelope. This platform was used to validate that both the vendor-provided head model and fast Monte Carlo engine used by the MRIdian system are accurate in modeling radiation transport in a patient using 2%/2 mm gamma criteria. Future applications of this platform will include dose validation and accumulation, IMRT optimization, and dosimetry system modeling for next generation MR-IGRT systems.

Performance and accuracy of criticality calculations performed using WARP – A framework for continuous energy Monte Carlo neutron transport in general 3D geometries on GPUs

DOE PAGES

Bergmann, Ryan M.; Rowland, Kelly L.; Radnović, Nikola; ...

2017-05-01

In this companion paper to "Algorithmic Choices in WARP - A Framework for Continuous Energy Monte Carlo Neutron Transport in General 3D Geometries on GPUs" (doi:10.1016/j.anucene.2014.10.039), the WARP Monte Carlo neutron transport framework for graphics processing units (GPUs) is benchmarked against production-level central processing unit (CPU) Monte Carlo neutron transport codes for both performance and accuracy. We compare neutron flux spectra, multiplication factors, runtimes, speedup factors, and costs of various GPU and CPU platforms running either WARP, Serpent 2.1.24, or MCNP 6.1. WARP compares well with the results of the production-level codes, and it is shown that on the newestmore » hardware considered, GPU platforms running WARP are between 0.8 to 7.6 times as fast as CPU platforms running production codes. Also, the GPU platforms running WARP were between 15% and 50% as expensive to purchase and between 80% to 90% as expensive to operate as equivalent CPU platforms performing at an equal simulation rate.« less

BlochSolver: A GPU-optimized fast 3D MRI simulator for experimentally compatible pulse sequences

NASA Astrophysics Data System (ADS)

Kose, Ryoichi; Kose, Katsumi

2017-08-01

A magnetic resonance imaging (MRI) simulator, which reproduces MRI experiments using computers, has been developed using two graphic-processor-unit (GPU) boards (GTX 1080). The MRI simulator was developed to run according to pulse sequences used in experiments. Experiments and simulations were performed to demonstrate the usefulness of the MRI simulator for three types of pulse sequences, namely, three-dimensional (3D) gradient-echo, 3D radio-frequency spoiled gradient-echo, and gradient-echo multislice with practical matrix sizes. The results demonstrated that the calculation speed using two GPU boards was typically about 7 TFLOPS and about 14 times faster than the calculation speed using CPUs (two 18-core Xeons). We also found that MR images acquired by experiment could be reproduced using an appropriate number of subvoxels, and that 3D isotropic and two-dimensional multislice imaging experiments for practical matrix sizes could be simulated using the MRI simulator. Therefore, we concluded that such powerful MRI simulators are expected to become an indispensable tool for MRI research and development.

Real-time electroholography using a multiple-graphics processing unit cluster system with a single spatial light modulator and the InfiniBand network

NASA Astrophysics Data System (ADS)

Niwase, Hiroaki; Takada, Naoki; Araki, Hiromitsu; Maeda, Yuki; Fujiwara, Masato; Nakayama, Hirotaka; Kakue, Takashi; Shimobaba, Tomoyoshi; Ito, Tomoyoshi

2016-09-01

Parallel calculations of large-pixel-count computer-generated holograms (CGHs) are suitable for multiple-graphics processing unit (multi-GPU) cluster systems. However, it is not easy for a multi-GPU cluster system to accomplish fast CGH calculations when CGH transfers between PCs are required. In these cases, the CGH transfer between the PCs becomes a bottleneck. Usually, this problem occurs only in multi-GPU cluster systems with a single spatial light modulator. To overcome this problem, we propose a simple method using the InfiniBand network. The computational speed of the proposed method using 13 GPUs (NVIDIA GeForce GTX TITAN X) was more than 3000 times faster than that of a CPU (Intel Core i7 4770) when the number of three-dimensional (3-D) object points exceeded 20,480. In practice, we achieved ˜40 tera floating point operations per second (TFLOPS) when the number of 3-D object points exceeded 40,960. Our proposed method was able to reconstruct a real-time movie of a 3-D object comprising 95,949 points.

Performance and accuracy of criticality calculations performed using WARP – A framework for continuous energy Monte Carlo neutron transport in general 3D geometries on GPUs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bergmann, Ryan M.; Rowland, Kelly L.; Radnović, Nikola

In this companion paper to "Algorithmic Choices in WARP - A Framework for Continuous Energy Monte Carlo Neutron Transport in General 3D Geometries on GPUs" (doi:10.1016/j.anucene.2014.10.039), the WARP Monte Carlo neutron transport framework for graphics processing units (GPUs) is benchmarked against production-level central processing unit (CPU) Monte Carlo neutron transport codes for both performance and accuracy. We compare neutron flux spectra, multiplication factors, runtimes, speedup factors, and costs of various GPU and CPU platforms running either WARP, Serpent 2.1.24, or MCNP 6.1. WARP compares well with the results of the production-level codes, and it is shown that on the newestmore » hardware considered, GPU platforms running WARP are between 0.8 to 7.6 times as fast as CPU platforms running production codes. Also, the GPU platforms running WARP were between 15% and 50% as expensive to purchase and between 80% to 90% as expensive to operate as equivalent CPU platforms performing at an equal simulation rate.« less

Rapid earthquake detection through GPU-Based template matching

NASA Astrophysics Data System (ADS)

Mu, Dawei; Lee, En-Jui; Chen, Po

2017-12-01

The template-matching algorithm (TMA) has been widely adopted for improving the reliability of earthquake detection. The TMA is based on calculating the normalized cross-correlation coefficient (NCC) between a collection of selected template waveforms and the continuous waveform recordings of seismic instruments. In realistic applications, the computational cost of the TMA is much higher than that of traditional techniques. In this study, we provide an analysis of the TMA and show how the GPU architecture provides an almost ideal environment for accelerating the TMA and NCC-based pattern recognition algorithms in general. So far, our best-performing GPU code has achieved a speedup factor of more than 800 with respect to a common sequential CPU code. We demonstrate the performance of our GPU code using seismic waveform recordings from the ML 6.6 Meinong earthquake sequence in Taiwan.

High performance transcription factor-DNA docking with GPU computing

PubMed Central

2012-01-01

Background Protein-DNA docking is a very challenging problem in structural bioinformatics and has important implications in a number of applications, such as structure-based prediction of transcription factor binding sites and rational drug design. Protein-DNA docking is very computational demanding due to the high cost of energy calculation and the statistical nature of conformational sampling algorithms. More importantly, experiments show that the docking quality depends on the coverage of the conformational sampling space. It is therefore desirable to accelerate the computation of the docking algorithm, not only to reduce computing time, but also to improve docking quality. Methods In an attempt to accelerate the sampling process and to improve the docking performance, we developed a graphics processing unit (GPU)-based protein-DNA docking algorithm. The algorithm employs a potential-based energy function to describe the binding affinity of a protein-DNA pair, and integrates Monte-Carlo simulation and a simulated annealing method to search through the conformational space. Algorithmic techniques were developed to improve the computation efficiency and scalability on GPU-based high performance computing systems. Results The effectiveness of our approach is tested on a non-redundant set of 75 TF-DNA complexes and a newly developed TF-DNA docking benchmark. We demonstrated that the GPU-based docking algorithm can significantly accelerate the simulation process and thereby improving the chance of finding near-native TF-DNA complex structures. This study also suggests that further improvement in protein-DNA docking research would require efforts from two integral aspects: improvement in computation efficiency and energy function design. Conclusions We present a high performance computing approach for improving the prediction accuracy of protein-DNA docking. The GPU-based docking algorithm accelerates the search of the conformational space and thus increases the chance of finding more near-native structures. To the best of our knowledge, this is the first ad hoc effort of applying GPU or GPU clusters to the protein-DNA docking problem. PMID:22759575

GraphReduce: Large-Scale Graph Analytics on Accelerator-Based HPC Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sengupta, Dipanjan; Agarwal, Kapil; Song, Shuaiwen

2015-09-30

Recent work on real-world graph analytics has sought to leverage the massive amount of parallelism offered by GPU devices, but challenges remain due to the inherent irregularity of graph algorithms and limitations in GPU-resident memory for storing large graphs. We present GraphReduce, a highly efficient and scalable GPU-based framework that operates on graphs that exceed the device’s internal memory capacity. GraphReduce adopts a combination of both edge- and vertex-centric implementations of the Gather-Apply-Scatter programming model and operates on multiple asynchronous GPU streams to fully exploit the high degrees of parallelism in GPUs with efficient graph data movement between the hostmore » and the device.« less

Validation of a GPU-based Monte Carlo code (gPMC) for proton radiation therapy: clinical cases study.

PubMed

Giantsoudi, Drosoula; Schuemann, Jan; Jia, Xun; Dowdell, Stephen; Jiang, Steve; Paganetti, Harald

2015-03-21

Monte Carlo (MC) methods are recognized as the gold-standard for dose calculation, however they have not replaced analytical methods up to now due to their lengthy calculation times. GPU-based applications allow MC dose calculations to be performed on time scales comparable to conventional analytical algorithms. This study focuses on validating our GPU-based MC code for proton dose calculation (gPMC) using an experimentally validated multi-purpose MC code (TOPAS) and compare their performance for clinical patient cases. Clinical cases from five treatment sites were selected covering the full range from very homogeneous patient geometries (liver) to patients with high geometrical complexity (air cavities and density heterogeneities in head-and-neck and lung patients) and from short beam range (breast) to large beam range (prostate). Both gPMC and TOPAS were used to calculate 3D dose distributions for all patients. Comparisons were performed based on target coverage indices (mean dose, V95, D98, D50, D02) and gamma index distributions. Dosimetric indices differed less than 2% between TOPAS and gPMC dose distributions for most cases. Gamma index analysis with 1%/1 mm criterion resulted in a passing rate of more than 94% of all patient voxels receiving more than 10% of the mean target dose, for all patients except for prostate cases. Although clinically insignificant, gPMC resulted in systematic underestimation of target dose for prostate cases by 1-2% compared to TOPAS. Correspondingly the gamma index analysis with 1%/1 mm criterion failed for most beams for this site, while for 2%/1 mm criterion passing rates of more than 94.6% of all patient voxels were observed. For the same initial number of simulated particles, calculation time for a single beam for a typical head and neck patient plan decreased from 4 CPU hours per million particles (2.8-2.9 GHz Intel X5600) for TOPAS to 2.4 s per million particles (NVIDIA TESLA C2075) for gPMC. Excellent agreement was demonstrated between our fast GPU-based MC code (gPMC) and a previously extensively validated multi-purpose MC code (TOPAS) for a comprehensive set of clinical patient cases. This shows that MC dose calculations in proton therapy can be performed on time scales comparable to analytical algorithms with accuracy comparable to state-of-the-art CPU-based MC codes.

A GPU-based calculation using the three-dimensional FDTD method for electromagnetic field analysis.

PubMed

Nagaoka, Tomoaki; Watanabe, Soichi

2010-01-01

Numerical simulations with the numerical human model using the finite-difference time domain (FDTD) method have recently been performed frequently in a number of fields in biomedical engineering. However, the FDTD calculation runs too slowly. We focus, therefore, on general purpose programming on the graphics processing unit (GPGPU). The three-dimensional FDTD method was implemented on the GPU using Compute Unified Device Architecture (CUDA). In this study, we used the NVIDIA Tesla C1060 as a GPGPU board. The performance of the GPU is evaluated in comparison with the performance of a conventional CPU and a vector supercomputer. The results indicate that three-dimensional FDTD calculations using a GPU can significantly reduce run time in comparison with that using a conventional CPU, even a native GPU implementation of the three-dimensional FDTD method, while the GPU/CPU speed ratio varies with the calculation domain and thread block size.

SU-E-T-422: Fast Analytical Beamlet Optimization for Volumetric Intensity-Modulated Arc Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chan, Kenny S K; Lee, Louis K Y; Xing, L

2015-06-15

Purpose: To implement a fast optimization algorithm on CPU/GPU heterogeneous computing platform and to obtain an optimal fluence for a given target dose distribution from the pre-calculated beamlets in an analytical approach. Methods: The 2D target dose distribution was modeled as an n-dimensional vector and estimated by a linear combination of independent basis vectors. The basis set was composed of the pre-calculated beamlet dose distributions at every 6 degrees of gantry angle and the cost function was set as the magnitude square of the vector difference between the target and the estimated dose distribution. The optimal weighting of the basis,more » which corresponds to the optimal fluence, was obtained analytically by the least square method. Those basis vectors with a positive weighting were selected for entering into the next level of optimization. Totally, 7 levels of optimization were implemented in the study.Ten head-and-neck and ten prostate carcinoma cases were selected for the study and mapped to a round water phantom with a diameter of 20cm. The Matlab computation was performed in a heterogeneous programming environment with Intel i7 CPU and NVIDIA Geforce 840M GPU. Results: In all selected cases, the estimated dose distribution was in a good agreement with the given target dose distribution and their correlation coefficients were found to be in the range of 0.9992 to 0.9997. Their root-mean-square error was monotonically decreasing and converging after 7 cycles of optimization. The computation took only about 10 seconds and the optimal fluence maps at each gantry angle throughout an arc were quickly obtained. Conclusion: An analytical approach is derived for finding the optimal fluence for a given target dose distribution and a fast optimization algorithm implemented on the CPU/GPU heterogeneous computing environment greatly reduces the optimization time.« less

Efficient Acceleration of the Pair-HMMs Forward Algorithm for GATK HaplotypeCaller on Graphics Processing Units.

PubMed

Ren, Shanshan; Bertels, Koen; Al-Ars, Zaid

2018-01-01

GATK HaplotypeCaller (HC) is a popular variant caller, which is widely used to identify variants in complex genomes. However, due to its high variants detection accuracy, it suffers from long execution time. In GATK HC, the pair-HMMs forward algorithm accounts for a large percentage of the total execution time. This article proposes to accelerate the pair-HMMs forward algorithm on graphics processing units (GPUs) to improve the performance of GATK HC. This article presents several GPU-based implementations of the pair-HMMs forward algorithm. It also analyzes the performance bottlenecks of the implementations on an NVIDIA Tesla K40 card with various data sets. Based on these results and the characteristics of GATK HC, we are able to identify the GPU-based implementations with the highest performance for the various analyzed data sets. Experimental results show that the GPU-based implementations of the pair-HMMs forward algorithm achieve a speedup of up to 5.47× over existing GPU-based implementations.

GAPD: a GPU-accelerated atom-based polychromatic diffraction simulation code.

PubMed

E, J C; Wang, L; Chen, S; Zhang, Y Y; Luo, S N

2018-03-01

GAPD, a graphics-processing-unit (GPU)-accelerated atom-based polychromatic diffraction simulation code for direct, kinematics-based, simulations of X-ray/electron diffraction of large-scale atomic systems with mono-/polychromatic beams and arbitrary plane detector geometries, is presented. This code implements GPU parallel computation via both real- and reciprocal-space decompositions. With GAPD, direct simulations are performed of the reciprocal lattice node of ultralarge systems (∼5 billion atoms) and diffraction patterns of single-crystal and polycrystalline configurations with mono- and polychromatic X-ray beams (including synchrotron undulator sources), and validation, benchmark and application cases are presented.

Implementing a GPU-based numerical algorithm for modelling dynamics of a high-speed train

NASA Astrophysics Data System (ADS)

Sytov, E. S.; Bratus, A. S.; Yurchenko, D.

2018-04-01

This paper discusses the initiative of implementing a GPU-based numerical algorithm for studying various phenomena associated with dynamics of a high-speed railway transport. The proposed numerical algorithm for calculating a critical speed of the bogie is based on the first Lyapunov number. Numerical algorithm is validated by analytical results, derived for a simple model. A dynamic model of a carriage connected to a new dual-wheelset flexible bogie is studied for linear and dry friction damping. Numerical results obtained by CPU, MPU and GPU approaches are compared and appropriateness of these methods is discussed.

Local Alignment Tool Based on Hadoop Framework and GPU Architecture

PubMed Central

Hung, Che-Lun; Hua, Guan-Jie

2014-01-01

With the rapid growth of next generation sequencing technologies, such as Slex, more and more data have been discovered and published. To analyze such huge data the computational performance is an important issue. Recently, many tools, such as SOAP, have been implemented on Hadoop and GPU parallel computing architectures. BLASTP is an important tool, implemented on GPU architectures, for biologists to compare protein sequences. To deal with the big biology data, it is hard to rely on single GPU. Therefore, we implement a distributed BLASTP by combining Hadoop and multi-GPUs. The experimental results present that the proposed method can improve the performance of BLASTP on single GPU, and also it can achieve high availability and fault tolerance. PMID:24955362

Local alignment tool based on Hadoop framework and GPU architecture.

PubMed

Hung, Che-Lun; Hua, Guan-Jie

2014-01-01

With the rapid growth of next generation sequencing technologies, such as Slex, more and more data have been discovered and published. To analyze such huge data the computational performance is an important issue. Recently, many tools, such as SOAP, have been implemented on Hadoop and GPU parallel computing architectures. BLASTP is an important tool, implemented on GPU architectures, for biologists to compare protein sequences. To deal with the big biology data, it is hard to rely on single GPU. Therefore, we implement a distributed BLASTP by combining Hadoop and multi-GPUs. The experimental results present that the proposed method can improve the performance of BLASTP on single GPU, and also it can achieve high availability and fault tolerance.

Accelerating image reconstruction in dual-head PET system by GPU and symmetry properties.

PubMed

Chou, Cheng-Ying; Dong, Yun; Hung, Yukai; Kao, Yu-Jiun; Wang, Weichung; Kao, Chien-Min; Chen, Chin-Tu

2012-01-01

Positron emission tomography (PET) is an important imaging modality in both clinical usage and research studies. We have developed a compact high-sensitivity PET system that consisted of two large-area panel PET detector heads, which produce more than 224 million lines of response and thus request dramatic computational demands. In this work, we employed a state-of-the-art graphics processing unit (GPU), NVIDIA Tesla C2070, to yield an efficient reconstruction process. Our approaches ingeniously integrate the distinguished features of the symmetry properties of the imaging system and GPU architectures, including block/warp/thread assignments and effective memory usage, to accelerate the computations for ordered subset expectation maximization (OSEM) image reconstruction. The OSEM reconstruction algorithms were implemented employing both CPU-based and GPU-based codes, and their computational performance was quantitatively analyzed and compared. The results showed that the GPU-accelerated scheme can drastically reduce the reconstruction time and thus can largely expand the applicability of the dual-head PET system.

The GPU implementation of micro - Doppler period estimation

NASA Astrophysics Data System (ADS)

Yang, Liyuan; Wang, Junling; Bi, Ran

2018-03-01

Aiming at the problem that the computational complexity and the deficiency of real-time of the wideband radar echo signal, a program is designed to improve the performance of real-time extraction of micro-motion feature in this paper based on the CPU-GPU heterogeneous parallel structure. Firstly, we discuss the principle of the micro-Doppler effect generated by the rolling of the scattering points on the orbiting satellite, analyses how to use Kalman filter to compensate the translational motion of tumbling satellite and how to use the joint time-frequency analysis and inverse Radon transform to extract the micro-motion features from the echo after compensation. Secondly, the advantages of GPU in terms of real-time processing and the working principle of CPU-GPU heterogeneous parallelism are analysed, and a program flow based on GPU to extract the micro-motion feature from the radar echo signal of rolling satellite is designed. At the end of the article the results of extraction are given to verify the correctness of the program and algorithm.

Fast analysis of molecular dynamics trajectories with graphics processing units-Radial distribution function histogramming

DOE Office of Scientific and Technical Information (OSTI.GOV)

Levine, Benjamin G., E-mail: ben.levine@temple.ed; Stone, John E., E-mail: johns@ks.uiuc.ed; Kohlmeyer, Axel, E-mail: akohlmey@temple.ed

2011-05-01

The calculation of radial distribution functions (RDFs) from molecular dynamics trajectory data is a common and computationally expensive analysis task. The rate limiting step in the calculation of the RDF is building a histogram of the distance between atom pairs in each trajectory frame. Here we present an implementation of this histogramming scheme for multiple graphics processing units (GPUs). The algorithm features a tiling scheme to maximize the reuse of data at the fastest levels of the GPU's memory hierarchy and dynamic load balancing to allow high performance on heterogeneous configurations of GPUs. Several versions of the RDF algorithm aremore » presented, utilizing the specific hardware features found on different generations of GPUs. We take advantage of larger shared memory and atomic memory operations available on state-of-the-art GPUs to accelerate the code significantly. The use of atomic memory operations allows the fast, limited-capacity on-chip memory to be used much more efficiently, resulting in a fivefold increase in performance compared to the version of the algorithm without atomic operations. The ultimate version of the algorithm running in parallel on four NVIDIA GeForce GTX 480 (Fermi) GPUs was found to be 92 times faster than a multithreaded implementation running on an Intel Xeon 5550 CPU. On this multi-GPU hardware, the RDF between two selections of 1,000,000 atoms each can be calculated in 26.9 s per frame. The multi-GPU RDF algorithms described here are implemented in VMD, a widely used and freely available software package for molecular dynamics visualization and analysis.« less

Fast precalculated triangular mesh algorithm for 3D binary computer-generated holograms.

PubMed

Yang, Fan; Kaczorowski, Andrzej; Wilkinson, Tim D

2014-12-10

A new method for constructing computer-generated holograms using a precalculated triangular mesh is presented. The speed of calculation can be increased dramatically by exploiting both the precalculated base triangle and GPU parallel computing. Unlike algorithms using point-based sources, this method can reconstruct a more vivid 3D object instead of a "hollow image." In addition, there is no need to do a fast Fourier transform for each 3D element every time. A ferroelectric liquid crystal spatial light modulator is used to display the binary hologram within our experiment and the hologram of a base right triangle is produced by utilizing just a one-step Fourier transform in the 2D case, which can be expanded to the 3D case by multiplying by a suitable Fresnel phase plane. All 3D holograms generated in this paper are based on Fresnel propagation; thus, the Fresnel plane is treated as a vital element in producing the hologram. A GeForce GTX 770 graphics card with 2 GB memory is used to achieve parallel computing.

Vesicle Pool Size at the Salamander Cone Ribbon Synapse

PubMed Central

Bartoletti, Theodore M.; Babai, Norbert

2010-01-01

Cone light responses are transmitted to postsynaptic neurons by changes in the rate of synaptic vesicle release. Vesicle pool size at the cone synapse constrains the amount of release and can thus shape contrast detection. We measured the number of vesicles in the rapidly releasable and reserve pools at cone ribbon synapses by performing simultaneous whole cell recording from cones and horizontal or off bipolar cells in the salamander retinal slice preparation. We found that properties of spontaneously occurring miniature excitatory postsynaptic currents (mEPSCs) are representative of mEPSCs evoked by depolarizing presynaptic stimulation. Strong, brief depolarization of the cone stimulated release of the entire rapidly releasable pool (RRP) of vesicles. Comparing charge transfer of the EPSC with mEPSC charge transfer, we determined that the fast component of the EPSC reflects release of ∼40 vesicles. Comparing EPSCs with simultaneous presynaptic capacitance measurements, we found that horizontal cell EPSCs constitute 14% of the total number of vesicles released from a cone terminal. Using a fluorescent ribeye-binding peptide, we counted ∼13 ribbons per cone. Together, these results suggest each cone contacts a single horizontal cell at ∼2 ribbons. The size of discrete components in the EPSC amplitude histogram also suggested ∼2 ribbon contacts per cell pair. We therefore conclude there are ∼20 vesicles per ribbon in the RRP, similar to the number of vesicles contacting the plasma membrane at the ribbon base. EPSCs evoked by lengthy depolarization suggest a reserve pool of ∼90 vesicles per ribbon, similar to the number of additional docking sites further up the ribbon. PMID:19923246

OpenACC acceleration of an unstructured CFD solver based on a reconstructed discontinuous Galerkin method for compressible flows

DOE PAGES

Xia, Yidong; Lou, Jialin; Luo, Hong; ...

2015-02-09

Here, an OpenACC directive-based graphics processing unit (GPU) parallel scheme is presented for solving the compressible Navier–Stokes equations on 3D hybrid unstructured grids with a third-order reconstructed discontinuous Galerkin method. The developed scheme requires the minimum code intrusion and algorithm alteration for upgrading a legacy solver with the GPU computing capability at very little extra effort in programming, which leads to a unified and portable code development strategy. A face coloring algorithm is adopted to eliminate the memory contention because of the threading of internal and boundary face integrals. A number of flow problems are presented to verify the implementationmore » of the developed scheme. Timing measurements were obtained by running the resulting GPU code on one Nvidia Tesla K20c GPU card (Nvidia Corporation, Santa Clara, CA, USA) and compared with those obtained by running the equivalent Message Passing Interface (MPI) parallel CPU code on a compute node (consisting of two AMD Opteron 6128 eight-core CPUs (Advanced Micro Devices, Inc., Sunnyvale, CA, USA)). Speedup factors of up to 24× and 1.6× for the GPU code were achieved with respect to one and 16 CPU cores, respectively. The numerical results indicate that this OpenACC-based parallel scheme is an effective and extensible approach to port unstructured high-order CFD solvers to GPU computing.« less

Real-time generation of infrared ocean scene based on GPU

NASA Astrophysics Data System (ADS)

Jiang, Zhaoyi; Wang, Xun; Lin, Yun; Jin, Jianqiu

2007-12-01

Infrared (IR) image synthesis for ocean scene has become more and more important nowadays, especially for remote sensing and military application. Although a number of works present ready-to-use simulations, those techniques cover only a few possible ways of water interacting with the environment. And the detail calculation of ocean temperature is rarely considered by previous investigators. With the advance of programmable features of graphic card, many algorithms previously limited to offline processing have become feasible for real-time usage. In this paper, we propose an efficient algorithm for real-time rendering of infrared ocean scene using the newest features of programmable graphics processors (GPU). It differs from previous works in three aspects: adaptive GPU-based ocean surface tessellation, sophisticated balance equation of thermal balance for ocean surface, and GPU-based rendering for infrared ocean scene. Finally some results of infrared image are shown, which are in good accordance with real images.

Dental cone-beam CT reconstruction from limited-angle view data based on compressed-sensing (CS) theory for fast, low-dose X-ray imaging

NASA Astrophysics Data System (ADS)

Je, Uikyu; Cho, Hyosung; Lee, Minsik; Oh, Jieun; Park, Yeonok; Hong, Daeki; Park, Cheulkyu; Cho, Heemoon; Choi, Sungil; Koo, Yangseo

2014-06-01

Recently, reducing radiation doses has become an issue of critical importance in the broader radiological community. As a possible technical approach, especially, in dental cone-beam computed tomography (CBCT), reconstruction from limited-angle view data (< 360°) would enable fast scanning with reduced doses to the patient. In this study, we investigated and implemented an efficient reconstruction algorithm based on compressed-sensing (CS) theory for the scan geometry and performed systematic simulation works to investigate the image characteristics. We also performed experimental works by applying the algorithm to a commercially-available dental CBCT system to demonstrate its effectiveness for image reconstruction in incomplete data problems. We successfully reconstructed CBCT images with incomplete projections acquired at selected scan angles of 120, 150, 180, and 200° with a fixed angle step of 1.2° and evaluated the reconstruction quality quantitatively. Both simulation and experimental demonstrations of the CS-based reconstruction from limited-angle view data show that the algorithm can be applied directly to current dental CBCT systems for reducing the imaging doses and further improving the image quality.

GPU Accelerated Clustering for Arbitrary Shapes in Geoscience Data

NASA Astrophysics Data System (ADS)

Pankratius, V.; Gowanlock, M.; Rude, C. M.; Li, J. D.

2016-12-01

Clustering algorithms have become a vital component in intelligent systems for geoscience that helps scientists discover and track phenomena of various kinds. Here, we outline advances in Density-Based Spatial Clustering of Applications with Noise (DBSCAN) which detects clusters of arbitrary shape that are common in geospatial data. In particular, we propose a hybrid CPU-GPU implementation of DBSCAN and highlight new optimization approaches on the GPU that allows clustering detection in parallel while optimizing data transport during CPU-GPU interactions. We employ an efficient batching scheme between the host and GPU such that limited GPU memory is not prohibitive when processing large and/or dense datasets. To minimize data transfer overhead, we estimate the total workload size and employ an execution that generates optimized batches that will not overflow the GPU buffer. This work is demonstrated on space weather Total Electron Content (TEC) datasets containing over 5 million measurements from instruments worldwide, and allows scientists to spot spatially coherent phenomena with ease. Our approach is up to 30 times faster than a sequential implementation and therefore accelerates discoveries in large datasets. We acknowledge support from NSF ACI-1442997.

Hardware and Software Design of FPGA-based PCIe Gen3 interface for APEnet+ network interconnect system

NASA Astrophysics Data System (ADS)

Ammendola, R.; Biagioni, A.; Frezza, O.; Lo Cicero, F.; Lonardo, A.; Martinelli, M.; Paolucci, P. S.; Pastorelli, E.; Rossetti, D.; Simula, F.; Tosoratto, L.; Vicini, P.

2015-12-01

In the attempt to develop an interconnection architecture optimized for hybrid HPC systems dedicated to scientific computing, we designed APEnet+, a point-to-point, low-latency and high-performance network controller supporting 6 fully bidirectional off-board links over a 3D torus topology. The first release of APEnet+ (named V4) was a board based on a 40 nm Altera FPGA, integrating 6 channels at 34 Gbps of raw bandwidth per direction and a PCIe Gen2 x8 host interface. It has been the first-of-its-kind device to implement an RDMA protocol to directly read/write data from/to Fermi and Kepler NVIDIA GPUs using NVIDIA peer-to-peer and GPUDirect RDMA protocols, obtaining real zero-copy GPU-to-GPU transfers over the network. The latest generation of APEnet+ systems (now named V5) implements a PCIe Gen3 x8 host interface on a 28 nm Altera Stratix V FPGA, with multi-standard fast transceivers (up to 14.4 Gbps) and an increased amount of configurable internal resources and hardware IP cores to support main interconnection standard protocols. Herein we present the APEnet+ V5 architecture, the status of its hardware and its system software design. Both its Linux Device Driver and the low-level libraries have been redeveloped to support the PCIe Gen3 protocol, introducing optimizations and solutions based on hardware/software co-design.

Analytical modeling and feasibility study of a multi-GPU cloud-based server (MGCS) framework for non-voxel-based dose calculations.

PubMed

Neylon, J; Min, Y; Kupelian, P; Low, D A; Santhanam, A

2017-04-01

In this paper, a multi-GPU cloud-based server (MGCS) framework is presented for dose calculations, exploring the feasibility of remote computing power for parallelization and acceleration of computationally and time intensive radiotherapy tasks in moving toward online adaptive therapies. An analytical model was developed to estimate theoretical MGCS performance acceleration and intelligently determine workload distribution. Numerical studies were performed with a computing setup of 14 GPUs distributed over 4 servers interconnected by a 1 Gigabits per second (Gbps) network. Inter-process communication methods were optimized to facilitate resource distribution and minimize data transfers over the server interconnect. The analytically predicted computation time predicted matched experimentally observations within 1-5 %. MGCS performance approached a theoretical limit of acceleration proportional to the number of GPUs utilized when computational tasks far outweighed memory operations. The MGCS implementation reproduced ground-truth dose computations with negligible differences, by distributing the work among several processes and implemented optimization strategies. The results showed that a cloud-based computation engine was a feasible solution for enabling clinics to make use of fast dose calculations for advanced treatment planning and adaptive radiotherapy. The cloud-based system was able to exceed the performance of a local machine even for optimized calculations, and provided significant acceleration for computationally intensive tasks. Such a framework can provide access to advanced technology and computational methods to many clinics, providing an avenue for standardization across institutions without the requirements of purchasing, maintaining, and continually updating hardware.

TH-C-18A-11: Investigating the Minimum Scan Parameters Required to Generate Free-Breathing Fast-Helical CT Scans Without Motion-Artifacts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thomas, D; Neylon, J; Dou, T

Purpose: A recently proposed 4D-CT protocol uses deformable registration of free-breathing fast-helical CT scans to generate a breathing motion model. In order to allow accurate registration, free-breathing images are required to be free of doubling-artifacts, which arise when tissue motion is greater than scan speed. This work identifies the minimum scanner parameters required to successfully generate free-breathing fast-helical scans without doubling-artifacts. Methods: 10 patients were imaged under free breathing conditions 25 times in alternating directions with a 64-slice CT scanner using a low dose fast helical protocol. A high temporal resolution (0.1s) 4D-CT was generated using a patient specific motionmore » model and patient breathing waveforms, and used as the input for a scanner simulation. Forward projections were calculated using helical cone-beam geometry (800 projections per rotation) and a GPU accelerated reconstruction algorithm was implemented. Various CT scanner detector widths and rotation times were simulated, and verified using a motion phantom. Doubling-artifacts were quantified in patient images using structural similarity maps to determine the similarity between axial slices. Results: Increasing amounts of doubling-artifacts were observed with increasing rotation times > 0.2s for 16×1mm slice scan geometry. No significant increase in doubling artifacts was observed for 64×1mm slice scan geometry up to 1.0s rotation time although blurring artifacts were observed >0.6s. Using a 16×1mm slice scan geometry, a rotation time of less than 0.3s (53mm/s scan speed) would be required to produce images of similar quality to a 64×1mm slice scan geometry. Conclusion: The current generation of 16 slice CT scanners, which are present in most Radiation Oncology departments, are not capable of generating free-breathing sorting-artifact-free images in the majority of patients. The next generation of CT scanners should be capable of at least 53mm/s scan speed in order to use a fast-helical 4D-CT protocol to generate a motion-artifact free 4D-CT. NIH R01CA096679.« less

A Framework for Batched and GPU-Resident Factorization Algorithms Applied to Block Householder Transformations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dong, Tingzing Tim; Tomov, Stanimire Z; Luszczek, Piotr R

As modern hardware keeps evolving, an increasingly effective approach to developing energy efficient and high-performance solvers is to design them to work on many small size and independent problems. Many applications already need this functionality, especially for GPUs, which are currently known to be about four to five times more energy efficient than multicore CPUs. We describe the development of one-sided factorizations that work for a set of small dense matrices in parallel, and we illustrate our techniques on the QR factorization based on Householder transformations. We refer to this mode of operation as a batched factorization. Our approach ismore » based on representing the algorithms as a sequence of batched BLAS routines for GPU-only execution. This is in contrast to the hybrid CPU-GPU algorithms that rely heavily on using the multicore CPU for specific parts of the workload. But for a system to benefit fully from the GPU's significantly higher energy efficiency, avoiding the use of the multicore CPU must be a primary design goal, so the system can rely more heavily on the more efficient GPU. Additionally, this will result in the removal of the costly CPU-to-GPU communication. Furthermore, we do not use a single symmetric multiprocessor(on the GPU) to factorize a single problem at a time. We illustrate how our performance analysis, and the use of profiling and tracing tools, guided the development and optimization of our batched factorization to achieve up to a 2-fold speedup and a 3-fold energy efficiency improvement compared to our highly optimized batched CPU implementations based on the MKL library(when using two sockets of Intel Sandy Bridge CPUs). Compared to a batched QR factorization featured in the CUBLAS library for GPUs, we achieved up to 5x speedup on the K40 GPU.« less

Explicit integration with GPU acceleration for large kinetic networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brock, Benjamin; Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830; Belt, Andrew

2015-12-01

We demonstrate the first implementation of recently-developed fast explicit kinetic integration algorithms on modern graphics processing unit (GPU) accelerators. Taking as a generic test case a Type Ia supernova explosion with an extremely stiff thermonuclear network having 150 isotopic species and 1604 reactions coupled to hydrodynamics using operator splitting, we demonstrate the capability to solve of order 100 realistic kinetic networks in parallel in the same time that standard implicit methods can solve a single such network on a CPU. This orders-of-magnitude decrease in computation time for solving systems of realistic kinetic networks implies that important coupled, multiphysics problems inmore » various scientific and technical fields that were intractable, or could be simulated only with highly schematic kinetic networks, are now computationally feasible.« less

TH-AB-207A-07: Radiation Dose Simulation for a Newly Proposed Dynamic Bowtie Filters for CT Using Fast Monte Carlo Methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, T; Lin, H; Gao, Y

Purpose: Dynamic bowtie filter is an innovative design capable of modulating the X-ray and balancing the flux in the detectors, and it introduces a new way of patient-specific CT scan optimizations. This study demonstrates the feasibility of performing fast Monte Carlo dose calculation for a type of dynamic bowtie filter for cone-beam CT (Liu et al. 2014 9(7) PloS one) using MIC coprocessors. Methods: The dynamic bowtie filter in question consists of a highly attenuating bowtie component (HB) and a weakly attenuating bowtie (WB). The HB is filled with CeCl3 solution and its surface is defined by a transcendental equation.more » The WB is an elliptical cylinder filled with air and immersed in the HB. As the scanner rotates, the orientation of WB remains the same with the static patient. In our Monte Carlo simulation, the HB was approximated by 576 boxes. The phantom was a voxelized elliptical cylinder composed of PMMA and surrounded by air (44cm×44cm×40cm, 1000×1000×1 voxels). The dose to the PMMA phantom was tallied with 0.15% statistical uncertainty under 100 kVp source. Two Monte Carlo codes ARCHER and MCNP-6.1 were compared. Both used double-precision. Compiler flags that may trade accuracy for speed were avoided. Results: The wall time of the simulation was 25.4 seconds by ARCHER on a 5110P MIC, 40 seconds on a X5650 CPU, and 523 seconds by the multithreaded MCNP on the same CPU. The high performance of ARCHER is attributed to the parameterized geometry and vectorization of the program hotspots. Conclusion: The dynamic bowtie filter modeled in this study is able to effectively reduce the dynamic range of the detected signals for the photon-counting detectors. With appropriate software optimization methods, the accelerator-based (MIC and GPU) Monte Carlo dose engines have shown good performance and can contribute to patient-specific CT scan optimizations.« less

Progress in Fast Ignition Studies with Electrons and Protons

NASA Astrophysics Data System (ADS)

MacKinnon, A. J.; Akli, K. U.; Bartal, T.; Beg, F. N.; Chawla, S.; Chen, C. D.; Chen, H.; Chen, S.; Chowdhury, E.; Fedosejevs, R.; Freeman, R. R.; Hey, D.; Higginson, D.; Key, M. H.; King, J. A.; Link, A.; Ma, T.; MacPhee, A. G.; Offermann, D.; Ovchinnikov, V.; Pasley, J.; Patel, P. K.; Ping, Y.; Schumacher, D. W.; Stephens, R. B.; Tsui, Y. Y.; Wei, M. S.; Van Woerkom, L. D.

2009-09-01

Isochoric heating of inertially confined fusion plasmas by laser driven MeV electrons or protons is an area of great topical interest in the inertial confinement fusion community, particularly with respect to the fast ignition (FI) concept for initiating burn in a fusion capsule. In order to investigate critical aspects needed for a FI point design, experiments were performed to study 1) laser-to-electrons or protons conversion issues and 2) laser-cone interactions including prepulse effects. A large suite of diagnostics was utilized to study these important parameters. Using cone—wire surrogate targets it is found that pre-pulse levels on medium scale lasers such as Titan at Lawrence Livermore National Laboratory produce long scale length plasmas that strongly effect coupling of the laser to FI relevant electrons inside cones. The cone wall thickness also affects coupling to the wire. Conversion efficiency to protons has also been measured and modeled as a function of target thickness, material. Conclusions from the proton and electron source experiments will be presented. Recent advances in modeling electron transport and innovative target designs for reducing igniter energy and increasing gain curves will also be discussed. In conclusion, a program of study will be presented based on understanding the fundamental physics of the electron or proton source relevant to FI.

Employing OpenCL to Accelerate Ab Initio Calculations on Graphics Processing Units.

PubMed

Kussmann, Jörg; Ochsenfeld, Christian

2017-06-13

We present an extension of our graphics processing units (GPU)-accelerated quantum chemistry package to employ OpenCL compute kernels, which can be executed on a wide range of computing devices like CPUs, Intel Xeon Phi, and AMD GPUs. Here, we focus on the use of AMD GPUs and discuss differences as compared to CUDA-based calculations on NVIDIA GPUs. First illustrative timings are presented for hybrid density functional theory calculations using serial as well as parallel compute environments. The results show that AMD GPUs are as fast or faster than comparable NVIDIA GPUs and provide a viable alternative for quantum chemical applications.

Fast optically sectioned fluorescence HiLo endomicroscopy.

PubMed

Ford, Tim N; Lim, Daryl; Mertz, Jerome

2012-02-01

We describe a nonscanning, fiber bundle endomicroscope that performs optically sectioned fluorescence imaging with fast frame rates and real-time processing. Our sectioning technique is based on HiLo imaging, wherein two widefield images are acquired under uniform and structured illumination and numerically processed to reject out-of-focus background. This work is an improvement upon an earlier demonstration of widefield optical sectioning through a flexible fiber bundle. The improved device features lateral and axial resolutions of 2.6 and 17 μm, respectively, a net frame rate of 9.5 Hz obtained by real-time image processing with a graphics processing unit (GPU) and significantly reduced motion artifacts obtained by the use of a double-shutter camera. We demonstrate the performance of our system with optically sectioned images and videos of a fluorescently labeled chorioallantoic membrane (CAM) in the developing G. gallus embryo. HiLo endomicroscopy is a candidate technique for low-cost, high-speed clinical optical biopsies.

Fast optically sectioned fluorescence HiLo endomicroscopy

NASA Astrophysics Data System (ADS)

Ford, Tim N.; Lim, Daryl; Mertz, Jerome

2012-02-01

We describe a nonscanning, fiber bundle endomicroscope that performs optically sectioned fluorescence imaging with fast frame rates and real-time processing. Our sectioning technique is based on HiLo imaging, wherein two widefield images are acquired under uniform and structured illumination and numerically processed to reject out-of-focus background. This work is an improvement upon an earlier demonstration of widefield optical sectioning through a flexible fiber bundle. The improved device features lateral and axial resolutions of 2.6 and 17 μm, respectively, a net frame rate of 9.5 Hz obtained by real-time image processing with a graphics processing unit (GPU) and significantly reduced motion artifacts obtained by the use of a double-shutter camera. We demonstrate the performance of our system with optically sectioned images and videos of a fluorescently labeled chorioallantoic membrane (CAM) in the developing G. gallus embryo. HiLo endomicroscopy is a candidate technique for low-cost, high-speed clinical optical biopsies.

Potential Application of a Graphical Processing Unit to Parallel Computations in the NUBEAM Code

NASA Astrophysics Data System (ADS)

Payne, J.; McCune, D.; Prater, R.

2010-11-01

NUBEAM is a comprehensive computational Monte Carlo based model for neutral beam injection (NBI) in tokamaks. NUBEAM computes NBI-relevant profiles in tokamak plasmas by tracking the deposition and the slowing of fast ions. At the core of NUBEAM are vector calculations used to track fast ions. These calculations have recently been parallelized to run on MPI clusters. However, cost and interlink bandwidth limit the ability to fully parallelize NUBEAM on an MPI cluster. Recent implementation of double precision capabilities for Graphical Processing Units (GPUs) presents a cost effective and high performance alternative or complement to MPI computation. Commercially available graphics cards can achieve up to 672 GFLOPS double precision and can handle hundreds of thousands of threads. The ability to execute at least one thread per particle simultaneously could significantly reduce the execution time and the statistical noise of NUBEAM. Progress on implementation on a GPU will be presented.

Validation of GPU-accelerated superposition-convolution dose computations for the Small Animal Radiation Research Platform.

PubMed

Cho, Nathan; Tsiamas, Panagiotis; Velarde, Esteban; Tryggestad, Erik; Jacques, Robert; Berbeco, Ross; McNutt, Todd; Kazanzides, Peter; Wong, John

2018-05-01

The Small Animal Radiation Research Platform (SARRP) has been developed for conformal microirradiation with on-board cone beam CT (CBCT) guidance. The graphics processing unit (GPU)-accelerated Superposition-Convolution (SC) method for dose computation has been integrated into the treatment planning system (TPS) for SARRP. This paper describes the validation of the SC method for the kilovoltage energy by comparing with EBT2 film measurements and Monte Carlo (MC) simulations. MC data were simulated by EGSnrc code with 3 × 10 8 -1.5 × 10 9 histories, while 21 photon energy bins were used to model the 220 kVp x-rays in the SC method. Various types of phantoms including plastic water, cork, graphite, and aluminum were used to encompass the range of densities of mouse organs. For the comparison, percentage depth dose (PDD) of SC, MC, and film measurements were analyzed. Cross beam (x,y) dosimetric profiles of SC and film measurements are also presented. Correction factors (CFz) to convert SC to MC dose-to-medium are derived from the SC and MC simulations in homogeneous phantoms of aluminum and graphite to improve the estimation. The SC method produces dose values that are within 5% of film measurements and MC simulations in the flat regions of the profile. The dose is less accurate at the edges, due to factors such as geometric uncertainties of film placement and difference in dose calculation grids. The GPU-accelerated Superposition-Convolution dose computation method was successfully validated with EBT2 film measurements and MC calculations. The SC method offers much faster computation speed than MC and provides calculations of both dose-to-water in medium and dose-to-medium in medium. © 2018 American Association of Physicists in Medicine.

SU-C-BRC-07: Parametrized GPU Accelerated Electron Monte Carlo Second Check

DOE Office of Scientific and Technical Information (OSTI.GOV)

Haywood, J

Purpose: I am presenting a parameterized 3D GPU accelerated electron Monte Carlo second check program. Method: I wrote the 3D grid dose calculation algorithm in CUDA and utilized an NVIDIA GeForce GTX 780 Ti to run all of the calculations. The electron path beyond the distal end of the cone is governed by four parameters: the amplitude of scattering (AMP), the mean and width of a Gaussian energy distribution (E and α), and the percentage of photons. In my code, I adjusted all parameters until the calculated PDD and profile fit the measured 10×10 open beam data within 1%/1mm. Imore » then wrote a user interface for reading the DICOM treatment plan and images in Python. In order to verify the algorithm, I calculated 3D dose distributions on a variety of phantoms and geometries, and compared them with the Eclipse eMC calculations. I also calculated several patient specific dose distributions, including a nose and an ear. Finally, I compared my algorithm’s computation times to Eclipse’s. Results: The calculated MU for all of the investigated geometries agree with the TPS within the TG-114 action level of 5%. The MU for the nose was < 0.5 % different while the MU for the ear at 105 SSD was ∼2 %. Calculation times for a 12MeV 10×10 open beam ranged from 1 second for a 2.5 mm grid resolution with ∼15 million particles to 33 seconds on a 1 mm grid with ∼460 million particles. Eclipse calculation runtimes distributed over 10 FAS workers were 9 seconds to 15 minutes respectively. Conclusion: The GPU accelerated second check allows quick MU verification while accounting for patient specific geometry and heterogeneity.« less

Adaptive multi-GPU Exchange Monte Carlo for the 3D Random Field Ising Model

NASA Astrophysics Data System (ADS)

Navarro, Cristóbal A.; Huang, Wei; Deng, Youjin

2016-08-01

This work presents an adaptive multi-GPU Exchange Monte Carlo approach for the simulation of the 3D Random Field Ising Model (RFIM). The design is based on a two-level parallelization. The first level, spin-level parallelism, maps the parallel computation as optimal 3D thread-blocks that simulate blocks of spins in shared memory with minimal halo surface, assuming a constant block volume. The second level, replica-level parallelism, uses multi-GPU computation to handle the simulation of an ensemble of replicas. CUDA's concurrent kernel execution feature is used in order to fill the occupancy of each GPU with many replicas, providing a performance boost that is more notorious at the smallest values of L. In addition to the two-level parallel design, the work proposes an adaptive multi-GPU approach that dynamically builds a proper temperature set free of exchange bottlenecks. The strategy is based on mid-point insertions at the temperature gaps where the exchange rate is most compromised. The extra work generated by the insertions is balanced across the GPUs independently of where the mid-point insertions were performed. Performance results show that spin-level performance is approximately two orders of magnitude faster than a single-core CPU version and one order of magnitude faster than a parallel multi-core CPU version running on 16-cores. Multi-GPU performance is highly convenient under a weak scaling setting, reaching up to 99 % efficiency as long as the number of GPUs and L increase together. The combination of the adaptive approach with the parallel multi-GPU design has extended our possibilities of simulation to sizes of L = 32 , 64 for a workstation with two GPUs. Sizes beyond L = 64 can eventually be studied using larger multi-GPU systems.

NMF-mGPU: non-negative matrix factorization on multi-GPU systems.

PubMed

Mejía-Roa, Edgardo; Tabas-Madrid, Daniel; Setoain, Javier; García, Carlos; Tirado, Francisco; Pascual-Montano, Alberto

2015-02-13

In the last few years, the Non-negative Matrix Factorization ( NMF ) technique has gained a great interest among the Bioinformatics community, since it is able to extract interpretable parts from high-dimensional datasets. However, the computing time required to process large data matrices may become impractical, even for a parallel application running on a multiprocessors cluster. In this paper, we present NMF-mGPU, an efficient and easy-to-use implementation of the NMF algorithm that takes advantage of the high computing performance delivered by Graphics-Processing Units ( GPUs ). Driven by the ever-growing demands from the video-games industry, graphics cards usually provided in PCs and laptops have evolved from simple graphics-drawing platforms into high-performance programmable systems that can be used as coprocessors for linear-algebra operations. However, these devices may have a limited amount of on-board memory, which is not considered by other NMF implementations on GPU. NMF-mGPU is based on CUDA ( Compute Unified Device Architecture ), the NVIDIA's framework for GPU computing. On devices with low memory available, large input matrices are blockwise transferred from the system's main memory to the GPU's memory, and processed accordingly. In addition, NMF-mGPU has been explicitly optimized for the different CUDA architectures. Finally, platforms with multiple GPUs can be synchronized through MPI ( Message Passing Interface ). In a four-GPU system, this implementation is about 120 times faster than a single conventional processor, and more than four times faster than a single GPU device (i.e., a super-linear speedup). Applications of GPUs in Bioinformatics are getting more and more attention due to their outstanding performance when compared to traditional processors. In addition, their relatively low price represents a highly cost-effective alternative to conventional clusters. In life sciences, this results in an excellent opportunity to facilitate the daily work of bioinformaticians that are trying to extract biological meaning out of hundreds of gigabytes of experimental information. NMF-mGPU can be used "out of the box" by researchers with little or no expertise in GPU programming in a variety of platforms, such as PCs, laptops, or high-end GPU clusters. NMF-mGPU is freely available at https://github.com/bioinfo-cnb/bionmf-gpu .

Open source acceleration of wave optics simulations on energy efficient high-performance computing platforms

NASA Astrophysics Data System (ADS)

Beck, Jeffrey; Bos, Jeremy P.

2017-05-01

We compare several modifications to the open-source wave optics package, WavePy, intended to improve execution time. Specifically, we compare the relative performance of the Intel MKL, a CPU based OpenCV distribution, and GPU-based version. Performance is compared between distributions both on the same compute platform and between a fully-featured computing workstation and the NVIDIA Jetson TX1 platform. Comparisons are drawn in terms of both execution time and power consumption. We have found that substituting the Fast Fourier Transform operation from OpenCV provides a marked improvement on all platforms. In addition, we show that embedded platforms offer some possibility for extensive improvement in terms of efficiency compared to a fully featured workstation.

A GPU-Parallelized Eigen-Based Clutter Filter Framework for Ultrasound Color Flow Imaging.

PubMed

Chee, Adrian J Y; Yiu, Billy Y S; Yu, Alfred C H

2017-01-01

Eigen-filters with attenuation response adapted to clutter statistics in color flow imaging (CFI) have shown improved flow detection sensitivity in the presence of tissue motion. Nevertheless, its practical adoption in clinical use is not straightforward due to the high computational cost for solving eigendecompositions. Here, we provide a pedagogical description of how a real-time computing framework for eigen-based clutter filtering can be developed through a single-instruction, multiple data (SIMD) computing approach that can be implemented on a graphical processing unit (GPU). Emphasis is placed on the single-ensemble-based eigen-filtering approach (Hankel singular value decomposition), since it is algorithmically compatible with GPU-based SIMD computing. The key algebraic principles and the corresponding SIMD algorithm are explained, and annotations on how such algorithm can be rationally implemented on the GPU are presented. Real-time efficacy of our framework was experimentally investigated on a single GPU device (GTX Titan X), and the computing throughput for varying scan depths and slow-time ensemble lengths was studied. Using our eigen-processing framework, real-time video-range throughput (24 frames/s) can be attained for CFI frames with full view in azimuth direction (128 scanlines), up to a scan depth of 5 cm ( λ pixel axial spacing) for slow-time ensemble length of 16 samples. The corresponding CFI image frames, with respect to the ones derived from non-adaptive polynomial regression clutter filtering, yielded enhanced flow detection sensitivity in vivo, as demonstrated in a carotid imaging case example. These findings indicate that the GPU-enabled eigen-based clutter filtering can improve CFI flow detection performance in real time.

Visual based laser speckle pattern recognition method for structural health monitoring

NASA Astrophysics Data System (ADS)

Park, Kyeongtaek; Torbol, Marco

2017-04-01

This study performed the system identification of a target structure by analyzing the laser speckle pattern taken by a camera. The laser speckle pattern is generated by the diffuse reflection of the laser beam on a rough surface of the target structure. The camera, equipped with a red filter, records the scattered speckle particles of the laser light in real time and the raw speckle image of the pixel data is fed to the graphic processing unit (GPU) in the system. The algorithm for laser speckle contrast analysis (LASCA) computes: the laser speckle contrast images and the laser speckle flow images. The k-mean clustering algorithm is used to classify the pixels in each frame and the clusters' centroids, which function as virtual sensors, track the displacement between different frames in time domain. The fast Fourier transform (FFT) and the frequency domain decomposition (FDD) compute the modal properties of the structure: natural frequencies and damping ratios. This study takes advantage of the large scale computational capability of GPU. The algorithm is written in Compute Unifies Device Architecture (CUDA C) that allows the processing of speckle images in real time.

Gctf: Real-time CTF determination and correction

PubMed Central

Zhang, Kai

2016-01-01

Accurate estimation of the contrast transfer function (CTF) is critical for a near-atomic resolution cryo electron microscopy (cryoEM) reconstruction. Here, a GPU-accelerated computer program, Gctf, for accurate and robust, real-time CTF determination is presented. The main target of Gctf is to maximize the cross-correlation of a simulated CTF with the logarithmic amplitude spectra (LAS) of observed micrographs after background subtraction. Novel approaches in Gctf improve both speed and accuracy. In addition to GPU acceleration (e.g. 10–50×), a fast ‘1-dimensional search plus 2-dimensional refinement (1S2R)’ procedure further speeds up Gctf. Based on the global CTF determination, the local defocus for each particle and for single frames of movies is accurately refined, which improves CTF parameters of all particles for subsequent image processing. Novel diagnosis method using equiphase averaging (EPA) and self-consistency verification procedures have also been implemented in the program for practical use, especially for aims of near-atomic reconstruction. Gctf is an independent program and the outputs can be easily imported into other cryoEM software such as Relion (Scheres, 2012) and Frealign (Grigorieff, 2007). The results from several representative datasets are shown and discussed in this paper. PMID:26592709

In vivo retinal and choroidal hypoxia imaging using a novel activatable hypoxia-selective near-infrared fluorescent probe.

PubMed

Fukuda, Shinichi; Okuda, Kensuke; Kishino, Genichiro; Hoshi, Sujin; Kawano, Itsuki; Fukuda, Masahiro; Yamashita, Toshiharu; Beheregaray, Simone; Nagano, Masumi; Ohneda, Osamu; Nagasawa, Hideko; Oshika, Tetsuro

2016-12-01

Retinal hypoxia plays a crucial role in ocular neovascular diseases, such as diabetic retinopathy, retinopathy of prematurity, and retinal vascular occlusion. Fluorescein angiography is useful for identifying the hypoxia extent by detecting non-perfusion areas or neovascularization, but its ability to detect early stages of hypoxia is limited. Recently, in vivo fluorescent probes for detecting hypoxia have been developed; however, these have not been extensively applied in ophthalmology. We evaluated whether a novel donor-excited photo-induced electron transfer (d-PeT) system based on an activatable hypoxia-selective near-infrared fluorescent (NIRF) probe (GPU-327) responds to both mild and severe hypoxia in various ocular ischemic diseases animal models. The ocular fundus examination offers unique opportunities for direct observation of the retina through the transparent cornea and lens. After injection of GPU-327 in various ocular hypoxic diseases of mouse and rabbit models, NIRF imaging in the ocular fundus can be performed noninvasively and easily by using commercially available fundus cameras. To investigate the safety of GPU-327, electroretinograms were also recorded after GPU-327 and PBS injection. Fluorescence of GPU-327 increased under mild hypoxic conditions in vitro. GPU-327 also yielded excellent signal-to-noise ratio without washing out in vivo experiments. By using near-infrared region, GPU-327 enables imaging of deeper ischemia, such as choroidal circulation. Additionally, from an electroretinogram, GPU-327 did not cause neurotoxicity. GPU-327 identified hypoxic area both in vivo and in vitro.

Large-scale neural circuit mapping data analysis accelerated with the graphical processing unit (GPU).

PubMed

Shi, Yulin; Veidenbaum, Alexander V; Nicolau, Alex; Xu, Xiangmin

2015-01-15

Modern neuroscience research demands computing power. Neural circuit mapping studies such as those using laser scanning photostimulation (LSPS) produce large amounts of data and require intensive computation for post hoc processing and analysis. Here we report on the design and implementation of a cost-effective desktop computer system for accelerated experimental data processing with recent GPU computing technology. A new version of Matlab software with GPU enabled functions is used to develop programs that run on Nvidia GPUs to harness their parallel computing power. We evaluated both the central processing unit (CPU) and GPU-enabled computational performance of our system in benchmark testing and practical applications. The experimental results show that the GPU-CPU co-processing of simulated data and actual LSPS experimental data clearly outperformed the multi-core CPU with up to a 22× speedup, depending on computational tasks. Further, we present a comparison of numerical accuracy between GPU and CPU computation to verify the precision of GPU computation. In addition, we show how GPUs can be effectively adapted to improve the performance of commercial image processing software such as Adobe Photoshop. To our best knowledge, this is the first demonstration of GPU application in neural circuit mapping and electrophysiology-based data processing. Together, GPU enabled computation enhances our ability to process large-scale data sets derived from neural circuit mapping studies, allowing for increased processing speeds while retaining data precision. Copyright © 2014 Elsevier B.V. All rights reserved.

Large scale neural circuit mapping data analysis accelerated with the graphical processing unit (GPU)

PubMed Central

Shi, Yulin; Veidenbaum, Alexander V.; Nicolau, Alex; Xu, Xiangmin

2014-01-01

Background Modern neuroscience research demands computing power. Neural circuit mapping studies such as those using laser scanning photostimulation (LSPS) produce large amounts of data and require intensive computation for post-hoc processing and analysis. New Method Here we report on the design and implementation of a cost-effective desktop computer system for accelerated experimental data processing with recent GPU computing technology. A new version of Matlab software with GPU enabled functions is used to develop programs that run on Nvidia GPUs to harness their parallel computing power. Results We evaluated both the central processing unit (CPU) and GPU-enabled computational performance of our system in benchmark testing and practical applications. The experimental results show that the GPU-CPU co-processing of simulated data and actual LSPS experimental data clearly outperformed the multi-core CPU with up to a 22x speedup, depending on computational tasks. Further, we present a comparison of numerical accuracy between GPU and CPU computation to verify the precision of GPU computation. In addition, we show how GPUs can be effectively adapted to improve the performance of commercial image processing software such as Adobe Photoshop. Comparison with Existing Method(s) To our best knowledge, this is the first demonstration of GPU application in neural circuit mapping and electrophysiology-based data processing. Conclusions Together, GPU enabled computation enhances our ability to process large-scale data sets derived from neural circuit mapping studies, allowing for increased processing speeds while retaining data precision. PMID:25277633

Efficient methods for implementation of multi-level nonrigid mass-preserving image registration on GPUs and multi-threaded CPUs.

PubMed

Ellingwood, Nathan D; Yin, Youbing; Smith, Matthew; Lin, Ching-Long

2016-04-01

Faster and more accurate methods for registration of images are important for research involved in conducting population-based studies that utilize medical imaging, as well as improvements for use in clinical applications. We present a novel computation- and memory-efficient multi-level method on graphics processing units (GPU) for performing registration of two computed tomography (CT) volumetric lung images. We developed a computation- and memory-efficient Diffeomorphic Multi-level B-Spline Transform Composite (DMTC) method to implement nonrigid mass-preserving registration of two CT lung images on GPU. The framework consists of a hierarchy of B-Spline control grids of increasing resolution. A similarity criterion known as the sum of squared tissue volume difference (SSTVD) was adopted to preserve lung tissue mass. The use of SSTVD consists of the calculation of the tissue volume, the Jacobian, and their derivatives, which makes its implementation on GPU challenging due to memory constraints. The use of the DMTC method enabled reduced computation and memory storage of variables with minimal communication between GPU and Central Processing Unit (CPU) due to ability to pre-compute values. The method was assessed on six healthy human subjects. Resultant GPU-generated displacement fields were compared against the previously validated CPU counterpart fields, showing good agreement with an average normalized root mean square error (nRMS) of 0.044±0.015. Runtime and performance speedup are compared between single-threaded CPU, multi-threaded CPU, and GPU algorithms. Best performance speedup occurs at the highest resolution in the GPU implementation for the SSTVD cost and cost gradient computations, with a speedup of 112 times that of the single-threaded CPU version and 11 times over the twelve-threaded version when considering average time per iteration using a Nvidia Tesla K20X GPU. The proposed GPU-based DMTC method outperforms its multi-threaded CPU version in terms of runtime. Total registration time reduced runtime to 2.9min on the GPU version, compared to 12.8min on twelve-threaded CPU version and 112.5min on a single-threaded CPU. Furthermore, the GPU implementation discussed in this work can be adapted for use of other cost functions that require calculation of the first derivatives. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

A GPU-Based Implementation of the Firefly Algorithm for Variable Selection in Multivariate Calibration Problems

PubMed Central

de Paula, Lauro C. M.; Soares, Anderson S.; de Lima, Telma W.; Delbem, Alexandre C. B.; Coelho, Clarimar J.; Filho, Arlindo R. G.

2014-01-01

Several variable selection algorithms in multivariate calibration can be accelerated using Graphics Processing Units (GPU). Among these algorithms, the Firefly Algorithm (FA) is a recent proposed metaheuristic that may be used for variable selection. This paper presents a GPU-based FA (FA-MLR) with multiobjective formulation for variable selection in multivariate calibration problems and compares it with some traditional sequential algorithms in the literature. The advantage of the proposed implementation is demonstrated in an example involving a relatively large number of variables. The results showed that the FA-MLR, in comparison with the traditional algorithms is a more suitable choice and a relevant contribution for the variable selection problem. Additionally, the results also demonstrated that the FA-MLR performed in a GPU can be five times faster than its sequential implementation. PMID:25493625

A GPU-Based Implementation of the Firefly Algorithm for Variable Selection in Multivariate Calibration Problems.

PubMed

de Paula, Lauro C M; Soares, Anderson S; de Lima, Telma W; Delbem, Alexandre C B; Coelho, Clarimar J; Filho, Arlindo R G

2014-01-01

Several variable selection algorithms in multivariate calibration can be accelerated using Graphics Processing Units (GPU). Among these algorithms, the Firefly Algorithm (FA) is a recent proposed metaheuristic that may be used for variable selection. This paper presents a GPU-based FA (FA-MLR) with multiobjective formulation for variable selection in multivariate calibration problems and compares it with some traditional sequential algorithms in the literature. The advantage of the proposed implementation is demonstrated in an example involving a relatively large number of variables. The results showed that the FA-MLR, in comparison with the traditional algorithms is a more suitable choice and a relevant contribution for the variable selection problem. Additionally, the results also demonstrated that the FA-MLR performed in a GPU can be five times faster than its sequential implementation.

Parallelizing flow-accumulation calculations on graphics processing units—From iterative DEM preprocessing algorithm to recursive multiple-flow-direction algorithm

NASA Astrophysics Data System (ADS)

Qin, Cheng-Zhi; Zhan, Lijun

2012-06-01

As one of the important tasks in digital terrain analysis, the calculation of flow accumulations from gridded digital elevation models (DEMs) usually involves two steps in a real application: (1) using an iterative DEM preprocessing algorithm to remove the depressions and flat areas commonly contained in real DEMs, and (2) using a recursive flow-direction algorithm to calculate the flow accumulation for every cell in the DEM. Because both algorithms are computationally intensive, quick calculation of the flow accumulations from a DEM (especially for a large area) presents a practical challenge to personal computer (PC) users. In recent years, rapid increases in hardware capacity of the graphics processing units (GPUs) provided in modern PCs have made it possible to meet this challenge in a PC environment. Parallel computing on GPUs using a compute-unified-device-architecture (CUDA) programming model has been explored to speed up the execution of the single-flow-direction algorithm (SFD). However, the parallel implementation on a GPU of the multiple-flow-direction (MFD) algorithm, which generally performs better than the SFD algorithm, has not been reported. Moreover, GPU-based parallelization of the DEM preprocessing step in the flow-accumulation calculations has not been addressed. This paper proposes a parallel approach to calculate flow accumulations (including both iterative DEM preprocessing and a recursive MFD algorithm) on a CUDA-compatible GPU. For the parallelization of an MFD algorithm (MFD-md), two different parallelization strategies using a GPU are explored. The first parallelization strategy, which has been used in the existing parallel SFD algorithm on GPU, has the problem of computing redundancy. Therefore, we designed a parallelization strategy based on graph theory. The application results show that the proposed parallel approach to calculate flow accumulations on a GPU performs much faster than either sequential algorithms or other parallel GPU-based algorithms based on existing parallelization strategies.

Performance Testing of GPU-Based Approximate Matching Algorithm on Network Traffic

DTIC Science & Technology

2015-03-01

Defense Department’s use. vi THIS PAGE INTENTIONALLY LEFT BLANK vii TABLE OF CONTENTS I.  INTRODUCTION...22  D.  GENERATING DIGESTS ............................................................................23  1.  Reference...the-shelf GPU Graphical Processing Unit GPGPU General -Purpose Graphic Processing Unit HBSS Host-Based Security System HIPS Host Intrusion

GPU-accelerated FDTD modeling of radio-frequency field-tissue interactions in high-field MRI.

PubMed

Chi, Jieru; Liu, Feng; Weber, Ewald; Li, Yu; Crozier, Stuart

2011-06-01

The analysis of high-field RF field-tissue interactions requires high-performance finite-difference time-domain (FDTD) computing. Conventional CPU-based FDTD calculations offer limited computing performance in a PC environment. This study presents a graphics processing unit (GPU)-based parallel-computing framework, producing substantially boosted computing efficiency (with a two-order speedup factor) at a PC-level cost. Specific details of implementing the FDTD method on a GPU architecture have been presented and the new computational strategy has been successfully applied to the design of a novel 8-element transceive RF coil system at 9.4 T. Facilitated by the powerful GPU-FDTD computing, the new RF coil array offers optimized fields (averaging 25% improvement in sensitivity, and 20% reduction in loop coupling compared with conventional array structures of the same size) for small animal imaging with a robust RF configuration. The GPU-enabled acceleration paves the way for FDTD to be applied for both detailed forward modeling and inverse design of MRI coils, which were previously impractical.

GPU-Meta-Storms: computing the structure similarities among massive amount of microbial community samples using GPU.

PubMed

Su, Xiaoquan; Wang, Xuetao; Jing, Gongchao; Ning, Kang

2014-04-01

The number of microbial community samples is increasing with exponential speed. Data-mining among microbial community samples could facilitate the discovery of valuable biological information that is still hidden in the massive data. However, current methods for the comparison among microbial communities are limited by their ability to process large amount of samples each with complex community structure. We have developed an optimized GPU-based software, GPU-Meta-Storms, to efficiently measure the quantitative phylogenetic similarity among massive amount of microbial community samples. Our results have shown that GPU-Meta-Storms would be able to compute the pair-wise similarity scores for 10 240 samples within 20 min, which gained a speed-up of >17 000 times compared with single-core CPU, and >2600 times compared with 16-core CPU. Therefore, the high-performance of GPU-Meta-Storms could facilitate in-depth data mining among massive microbial community samples, and make the real-time analysis and monitoring of temporal or conditional changes for microbial communities possible. GPU-Meta-Storms is implemented by CUDA (Compute Unified Device Architecture) and C++. Source code is available at http://www.computationalbioenergy.org/meta-storms.html.

Migrating EO/IR sensors to cloud-based infrastructure as service architectures

NASA Astrophysics Data System (ADS)

Berglie, Stephen T.; Webster, Steven; May, Christopher M.

2014-06-01

The Night Vision Image Generator (NVIG), a product of US Army RDECOM CERDEC NVESD, is a visualization tool used widely throughout Army simulation environments to provide fully attributed synthesized, full motion video using physics-based sensor and environmental effects. The NVIG relies heavily on contemporary hardware-based acceleration and GPU processing techniques, which push the envelope of both enterprise and commodity-level hypervisor support for providing virtual machines with direct access to hardware resources. The NVIG has successfully been integrated into fully virtual environments where system architectures leverage cloudbased technologies to various extents in order to streamline infrastructure and service management. This paper details the challenges presented to engineers seeking to migrate GPU-bound processes, such as the NVIG, to virtual machines and, ultimately, Cloud-Based IAS architectures. In addition, it presents the path that led to success for the NVIG. A brief overview of Cloud-Based infrastructure management tool sets is provided, and several virtual desktop solutions are outlined. A discrimination is made between general purpose virtual desktop technologies compared to technologies that expose GPU-specific capabilities, including direct rendering and hard ware-based video encoding. Candidate hypervisor/virtual machine configurations that nominally satisfy the virtualized hardware-level GPU requirements of the NVIG are presented , and each is subsequently reviewed in light of its implications on higher-level Cloud management techniques. Implementation details are included from the hardware level, through the operating system, to the 3D graphics APls required by the NVIG and similar GPU-bound tools.

Boosting the FM-Index on the GPU: Effective Techniques to Mitigate Random Memory Access.

PubMed

Chacón, Alejandro; Marco-Sola, Santiago; Espinosa, Antonio; Ribeca, Paolo; Moure, Juan Carlos

2015-01-01

The recent advent of high-throughput sequencing machines producing big amounts of short reads has boosted the interest in efficient string searching techniques. As of today, many mainstream sequence alignment software tools rely on a special data structure, called the FM-index, which allows for fast exact searches in large genomic references. However, such searches translate into a pseudo-random memory access pattern, thus making memory access the limiting factor of all computation-efficient implementations, both on CPUs and GPUs. Here, we show that several strategies can be put in place to remove the memory bottleneck on the GPU: more compact indexes can be implemented by having more threads work cooperatively on larger memory blocks, and a k-step FM-index can be used to further reduce the number of memory accesses. The combination of those and other optimisations yields an implementation that is able to process about two Gbases of queries per second on our test platform, being about 8 × faster than a comparable multi-core CPU version, and about 3 × to 5 × faster than the FM-index implementation on the GPU provided by the recently announced Nvidia NVBIO bioinformatics library.

Dataflow-Based Implementation of Layered Sensing Applications on High-Performance Embedded Processors

DTIC Science & Technology

2013-03-01

time (milliseconds) GFlops Comparison to GPU peak performance (%) Cascade Gaussian Filtering 13 45.19 6.3 Difference of Gaussian 0.512 152...values for the GPU-targeted actor implementations in terms of Giga Floating Point Operations Per Second ( GFLOPS ). Our GFLOPS calculation for an actor...kernels. The results for GFLOPS are provided in Table . The actors were implemented on an NVIDIA GTX260 GPU, which provides 715 GFLOPS as peak

Memory-Scalable GPU Spatial Hierarchy Construction.

PubMed

Qiming Hou; Xin Sun; Kun Zhou; Lauterbach, C; Manocha, D

2011-04-01

Recent GPU algorithms for constructing spatial hierarchies have achieved promising performance for moderately complex models by using the breadth-first search (BFS) construction order. While being able to exploit the massive parallelism on the GPU, the BFS order also consumes excessive GPU memory, which becomes a serious issue for interactive applications involving very complex models with more than a few million triangles. In this paper, we propose to use the partial breadth-first search (PBFS) construction order to control memory consumption while maximizing performance. We apply the PBFS order to two hierarchy construction algorithms. The first algorithm is for kd-trees that automatically balances between the level of parallelism and intermediate memory usage. With PBFS, peak memory consumption during construction can be efficiently controlled without costly CPU-GPU data transfer. We also develop memory allocation strategies to effectively limit memory fragmentation. The resulting algorithm scales well with GPU memory and constructs kd-trees of models with millions of triangles at interactive rates on GPUs with 1 GB memory. Compared with existing algorithms, our algorithm is an order of magnitude more scalable for a given GPU memory bound. The second algorithm is for out-of-core bounding volume hierarchy (BVH) construction for very large scenes based on the PBFS construction order. At each iteration, all constructed nodes are dumped to the CPU memory, and the GPU memory is freed for the next iteration's use. In this way, the algorithm is able to build trees that are too large to be stored in the GPU memory. Experiments show that our algorithm can construct BVHs for scenes with up to 20 M triangles, several times larger than previous GPU algorithms.

Accelerating Smith-Waterman Alignment for Protein Database Search Using Frequency Distance Filtration Scheme Based on CPU-GPU Collaborative System.

PubMed

Liu, Yu; Hong, Yang; Lin, Chun-Yuan; Hung, Che-Lun

2015-01-01

The Smith-Waterman (SW) algorithm has been widely utilized for searching biological sequence databases in bioinformatics. Recently, several works have adopted the graphic card with Graphic Processing Units (GPUs) and their associated CUDA model to enhance the performance of SW computations. However, these works mainly focused on the protein database search by using the intertask parallelization technique, and only using the GPU capability to do the SW computations one by one. Hence, in this paper, we will propose an efficient SW alignment method, called CUDA-SWfr, for the protein database search by using the intratask parallelization technique based on a CPU-GPU collaborative system. Before doing the SW computations on GPU, a procedure is applied on CPU by using the frequency distance filtration scheme (FDFS) to eliminate the unnecessary alignments. The experimental results indicate that CUDA-SWfr runs 9.6 times and 96 times faster than the CPU-based SW method without and with FDFS, respectively.

NaNet-10: a 10GbE network interface card for the GPU-based low-level trigger of the NA62 RICH detector.

NASA Astrophysics Data System (ADS)

Ammendola, R.; Biagioni, A.; Fiorini, M.; Frezza, O.; Lonardo, A.; Lamanna, G.; Lo Cicero, F.; Martinelli, M.; Neri, I.; Paolucci, P. S.; Pastorelli, E.; Piandani, R.; Pontisso, L.; Rossetti, D.; Simula, F.; Sozzi, M.; Tosoratto, L.; Vicini, P.

2016-03-01

A GPU-based low level (L0) trigger is currently integrated in the experimental setup of the RICH detector of the NA62 experiment to assess the feasibility of building more refined physics-related trigger primitives and thus improve the trigger discriminating power. To ensure the real-time operation of the system, a dedicated data transport mechanism has been implemented: an FPGA-based Network Interface Card (NaNet-10) receives data from detectors and forwards them with low, predictable latency to the memory of the GPU performing the trigger algorithms. Results of the ring-shaped hit patterns reconstruction will be reported and discussed.

Automated volume of interest delineation and rendering of cone beam CT images in interventional cardiology

NASA Astrophysics Data System (ADS)

Lorenz, Cristian; Schäfer, Dirk; Eshuis, Peter; Carroll, John; Grass, Michael

2012-02-01

Interventional C-arm systems allow the efficient acquisition of 3D cone beam CT images. They can be used for intervention planning, navigation, and outcome assessment. We present a fast and completely automated volume of interest (VOI) delineation for cardiac interventions, covering the whole visceral cavity including mediastinum and lungs but leaving out rib-cage and spine. The problem is addressed in a model based approach. The procedure has been evaluated on 22 patient cases and achieves an average surface error below 2mm. The method is able to cope with varying image intensities, varying truncations due to the limited reconstruction volume, and partially with heavy metal and motion artifacts.

High performance computing for deformable image registration: towards a new paradigm in adaptive radiotherapy.

PubMed

Samant, Sanjiv S; Xia, Junyi; Muyan-Ozcelik, Pinar; Owens, John D

2008-08-01

The advent of readily available temporal imaging or time series volumetric (4D) imaging has become an indispensable component of treatment planning and adaptive radiotherapy (ART) at many radiotherapy centers. Deformable image registration (DIR) is also used in other areas of medical imaging, including motion corrected image reconstruction. Due to long computation time, clinical applications of DIR in radiation therapy and elsewhere have been limited and consequently relegated to offline analysis. With the recent advances in hardware and software, graphics processing unit (GPU) based computing is an emerging technology for general purpose computation, including DIR, and is suitable for highly parallelized computing. However, traditional general purpose computation on the GPU is limited because the constraints of the available programming platforms. As well, compared to CPU programming, the GPU currently has reduced dedicated processor memory, which can limit the useful working data set for parallelized processing. We present an implementation of the demons algorithm using the NVIDIA 8800 GTX GPU and the new CUDA programming language. The GPU performance will be compared with single threading and multithreading CPU implementations on an Intel dual core 2.4 GHz CPU using the C programming language. CUDA provides a C-like language programming interface, and allows for direct access to the highly parallel compute units in the GPU. Comparisons for volumetric clinical lung images acquired using 4DCT were carried out. Computation time for 100 iterations in the range of 1.8-13.5 s was observed for the GPU with image size ranging from 2.0 x 10(6) to 14.2 x 10(6) pixels. The GPU registration was 55-61 times faster than the CPU for the single threading implementation, and 34-39 times faster for the multithreading implementation. For CPU based computing, the computational time generally has a linear dependence on image size for medical imaging data. Computational efficiency is characterized in terms of time per megapixels per iteration (TPMI) with units of seconds per megapixels per iteration (or spmi). For the demons algorithm, our CPU implementation yielded largely invariant values of TPMI. The mean TPMIs were 0.527 spmi and 0.335 spmi for the single threading and multithreading cases, respectively, with <2% variation over the considered image data range. For GPU computing, we achieved TPMI =0.00916 spmi with 3.7% variation, indicating optimized memory handling under CUDA. The paradigm of GPU based real-time DIR opens up a host of clinical applications for medical imaging.

Comparison and Computational Performance of Tsunami-HySEA and MOST Models for the LANTEX 2013 scenario

NASA Astrophysics Data System (ADS)

González-Vida, Jose M.; Macías, Jorge; Mercado, Aurelio; Ortega, Sergio; Castro, Manuel J.

2017-04-01

Tsunami-HySEA model is used to simulate the Caribbean LANTEX 2013 scenario (LANTEX is the acronym for Large AtlaNtic Tsunami EXercise, which is carried out annually). The numerical simulation of the propagation and inundation phases, is performed with both models but using different mesh resolutions and nested meshes. Some comparisons with the MOST tsunami model available at the University of Puerto Rico (UPR) are made. Both models compare well for propagating tsunami waves in open sea, producing very similar results. In near-shore shallow waters, Tsunami-HySEA should be compared with the inundation version of MOST, since the propagation version of MOST is limited to deeper waters. Regarding the inundation phase, a 1 arc-sec (approximately 30 m) resolution mesh covering all of Puerto Rico, is used, and a three-level nested meshes technique implemented. In the inundation phase, larger differences between model results are observed. Nevertheless, the most striking difference resides in computational time; Tsunami-HySEA is coded using the advantages of GPU architecture, and can produce a 4 h simulation in a 60 arcsec resolution grid for the whole Caribbean Sea in less than 4 min with a single general-purpose GPU and as fast as 11 s with 32 general-purpose GPUs. In the inundation stage with nested meshes, approximately 8 hours of wall clock time is needed for a 2-h simulation in a single GPU (versus more than 2 days for the MOST inundation, running three different parts of the island—West, Center, East—at the same time due to memory limitations in MOST). When domain decomposition techniques are finally implemented by breaking up the computational domain into sub-domains and assigning a GPU to each sub-domain (multi-GPU Tsunami-HySEA version), we show that the wall clock time significantly decreases, allowing high-resolution inundation modelling in very short computational times, reducing, for example, if eight GPUs are used, the wall clock time to around 1 hour. Besides, these computational times are obtained using general-purpose GPU hardware.

Explicit integration with GPU acceleration for large kinetic networks

DOE PAGES

Brock, Benjamin; Belt, Andrew; Billings, Jay Jay; ...

2015-09-15

In this study, we demonstrate the first implementation of recently-developed fast explicit kinetic integration algorithms on modern graphics processing unit (GPU) accelerators. Taking as a generic test case a Type Ia supernova explosion with an extremely stiff thermonuclear network having 150 isotopic species and 1604 reactions coupled to hydrodynamics using operator splitting, we demonstrate the capability to solve of order 100 realistic kinetic networks in parallel in the same time that standard implicit methods can solve a single such network on a CPU. In addition, this orders-of-magnitude decrease in computation time for solving systems of realistic kinetic networks implies thatmore » important coupled, multiphysics problems in various scientific and technical fields that were intractable, or could be simulated only with highly schematic kinetic networks, are now computationally feasible.« less

A multi-port 10GbE PCIe NIC featuring UDP offload and GPUDirect capabilities.

NASA Astrophysics Data System (ADS)

Ammendola, Roberto; Biagioni, Andrea; Frezza, Ottorino; Lamanna, Gianluca; Lo Cicero, Francesca; Lonardo, Alessandro; Martinelli, Michele; Stanislao Paolucci, Pier; Pastorelli, Elena; Pontisso, Luca; Rossetti, Davide; Simula, Francesco; Sozzi, Marco; Tosoratto, Laura; Vicini, Piero

2015-12-01

NaNet-10 is a four-ports 10GbE PCIe Network Interface Card designed for low-latency real-time operations with GPU systems. To this purpose the design includes an UDP offload module, for fast and clock-cycle deterministic handling of the transport layer protocol, plus a GPUDirect P2P/RDMA engine for low-latency communication with NVIDIA Tesla GPU devices. A dedicated module (Multi-Stream) can optionally process input UDP streams before data is delivered through PCIe DMA to their destination devices, re-organizing data from different streams guaranteeing computational optimization. NaNet-10 is going to be integrated in the NA62 CERN experiment in order to assess the suitability of GPGPU systems as real-time triggers; results and lessons learned while performing this activity will be reported herein.

A non-voxel-based broad-beam (NVBB) framework for IMRT treatment planning.

PubMed

Lu, Weiguo

2010-12-07

We present a novel framework that enables very large scale intensity-modulated radiation therapy (IMRT) planning in limited computation resources with improvements in cost, plan quality and planning throughput. Current IMRT optimization uses a voxel-based beamlet superposition (VBS) framework that requires pre-calculation and storage of a large amount of beamlet data, resulting in large temporal and spatial complexity. We developed a non-voxel-based broad-beam (NVBB) framework for IMRT capable of direct treatment parameter optimization (DTPO). In this framework, both objective function and derivative are evaluated based on the continuous viewpoint, abandoning 'voxel' and 'beamlet' representations. Thus pre-calculation and storage of beamlets are no longer needed. The NVBB framework has linear complexities (O(N(3))) in both space and time. The low memory, full computation and data parallelization nature of the framework render its efficient implementation on the graphic processing unit (GPU). We implemented the NVBB framework and incorporated it with the TomoTherapy treatment planning system (TPS). The new TPS runs on a single workstation with one GPU card (NVBB-GPU). Extensive verification/validation tests were performed in house and via third parties. Benchmarks on dose accuracy, plan quality and throughput were compared with the commercial TomoTherapy TPS that is based on the VBS framework and uses a computer cluster with 14 nodes (VBS-cluster). For all tests, the dose accuracy of these two TPSs is comparable (within 1%). Plan qualities were comparable with no clinically significant difference for most cases except that superior target uniformity was seen in the NVBB-GPU for some cases. However, the planning time using the NVBB-GPU was reduced many folds over the VBS-cluster. In conclusion, we developed a novel NVBB framework for IMRT optimization. The continuous viewpoint and DTPO nature of the algorithm eliminate the need for beamlets and lead to better plan quality. The computation parallelization on a GPU instead of a computer cluster significantly reduces hardware and service costs. Compared with using the current VBS framework on a computer cluster, the planning time is significantly reduced using the NVBB framework on a single workstation with a GPU card.

Combining endoscopic ultrasound with Time-Of-Flight PET: The EndoTOFPET-US Project

NASA Astrophysics Data System (ADS)

Frisch, Benjamin

2013-12-01

The EndoTOFPET-US collaboration develops a multimodal imaging technique for endoscopic exams of the pancreas or the prostate. It combines the benefits of high resolution metabolic imaging with Time-Of-Flight Positron Emission Tomography (TOF PET) and anatomical imaging with ultrasound (US). EndoTOFPET-US consists of a PET head extension for a commercial US endoscope and a PET plate outside the body in coincidence with the head. The high level of miniaturization and integration creates challenges in fields such as scintillating crystals, ultra-fast photo-detection, highly integrated electronics, system integration and image reconstruction. Amongst the developments, fast scintillators as well as fast and compact digital SiPMs with single SPAD readout are used to obtain the best coincidence time resolution (CTR). Highly integrated ASICs and DAQ electronics contribute to the timing performances of EndoTOFPET. In view of the targeted resolution of around 1 mm in the reconstructed image, we present a prototype detector system with a CTR better than 240 ps FWHM. We discuss the challenges in simulating such a system and introduce reconstruction algorithms based on graphics processing units (GPU).

GPU Accelerated Prognostics

NASA Technical Reports Server (NTRS)

Gorospe, George E., Jr.; Daigle, Matthew J.; Sankararaman, Shankar; Kulkarni, Chetan S.; Ng, Eley

2017-01-01

Prognostic methods enable operators and maintainers to predict the future performance for critical systems. However, these methods can be computationally expensive and may need to be performed each time new information about the system becomes available. In light of these computational requirements, we have investigated the application of graphics processing units (GPUs) as a computational platform for real-time prognostics. Recent advances in GPU technology have reduced cost and increased the computational capability of these highly parallel processing units, making them more attractive for the deployment of prognostic software. We present a survey of model-based prognostic algorithms with considerations for leveraging the parallel architecture of the GPU and a case study of GPU-accelerated battery prognostics with computational performance results.

Cost-effective GPU-grid for genome-wide epistasis calculations.

PubMed

Pütz, B; Kam-Thong, T; Karbalai, N; Altmann, A; Müller-Myhsok, B

2013-01-01

Until recently, genotype studies were limited to the investigation of single SNP effects due to the computational burden incurred when studying pairwise interactions of SNPs. However, some genetic effects as simple as coloring (in plants and animals) cannot be ascribed to a single locus but only understood when epistasis is taken into account [1]. It is expected that such effects are also found in complex diseases where many genes contribute to the clinical outcome of affected individuals. Only recently have such problems become feasible computationally. The inherently parallel structure of the problem makes it a perfect candidate for massive parallelization on either grid or cloud architectures. Since we are also dealing with confidential patient data, we were not able to consider a cloud-based solution but had to find a way to process the data in-house and aimed to build a local GPU-based grid structure. Sequential epistatsis calculations were ported to GPU using CUDA at various levels. Parallelization on the CPU was compared to corresponding GPU counterparts with regards to performance and cost. A cost-effective solution was created by combining custom-built nodes equipped with relatively inexpensive consumer-level graphics cards with highly parallel GPUs in a local grid. The GPU method outperforms current cluster-based systems on a price/performance criterion, as a single GPU shows speed performance comparable up to 200 CPU cores. The outlined approach will work for problems that easily lend themselves to massive parallelization. Code for various tasks has been made available and ongoing development of tools will further ease the transition from sequential to parallel algorithms.

Rapid kinetics of endocytosis at rod photoreceptor synapses depends upon endocytic load and calcium.

PubMed

Cork, Karlene M; Thoreson, Wallace B

2014-05-01

Release from rods is triggered by the opening of L-type Ca2+ channels that lie beneath synaptic ribbons. After exocytosis, vesicles are retrieved by compensatory endocytosis. Previous work showed that endocytosis is dynamin-dependent in rods but dynamin-independent in cones. We hypothesized that fast endocytosis in rods may also differ from cones in its dependence upon the amount of Ca2+ influx and/or endocytic load. We measured exocytosis and endocytosis from membrane capacitance (C m) changes evoked by depolarizing steps in voltage clamped rods from tiger salamander retinal slices. Similar to cones, the time constant for endocytosis in rods was quite fast, averaging <200 ms. We manipulated Ca2+ influx and the amount of vesicle release by altering the duration and voltage of depolarizing steps. Unlike cones, endocytosis kinetics in rods slowed after increasing Ca2+ channel activation with longer step durations or more strongly depolarized voltage steps. Endocytosis kinetics also slowed as Ca2+ buffering was decreased by replacing BAPTA (10 or 1 mM) with the slower Ca2+ buffer EGTA (5 or 0.5 mM) in the pipette solution. These data provide further evidence that endocytosis mechanisms differ in rods and cones and suggest that endocytosis in rods is regulated by both endocytic load and local Ca2+ levels.

Rapid kinetics of endocytosis at rod photoreceptor synapses depends upon endocytic load and calcium

PubMed Central

CORK, KARLENE M.; THORESON, WALLACE B.

2015-01-01

Release from rods is triggered by the opening of L-type Ca2+ channels that lie beneath synaptic ribbons. After exocytosis, vesicles are retrieved by compensatory endocytosis. Previous work showed that endocytosis is dynamin-dependent in rods but dynamin-independent in cones. We hypothesized that fast endocytosis in rods may also differ from cones in its dependence upon the amount of Ca2+ influx and/or endocytic load. We measured exocytosis and endocytosis from membrane capacitance (Cm) changes evoked by depolarizing steps in voltage clamped rods from tiger salamander retinal slices. Similar to cones, the time constant for endocytosis in rods was quite fast, averaging <200 ms. We manipulated Ca2+ influx and the amount of vesicle release by altering the duration and voltage of depolarizing steps. Unlike cones, endocytosis kinetics in rods slowed after increasing Ca2+ channel activation with longer step durations or more strongly depolarized voltage steps. Endocytosis kinetics also slowed as Ca2+ buffering was decreased by replacing BAPTA (10 or 1 mM) with the slower Ca2+ buffer EGTA (5 or 0.5 mM) in the pipette solution. These data provide further evidence that endocytosis mechanisms differ in rods and cones and suggest that endocytosis in rods is regulated by both endocytic load and local Ca2+ levels. PMID:24735554

A Hybrid CPU/GPU Pattern-Matching Algorithm for Deep Packet Inspection

PubMed Central

Chen, Yaw-Chung

2015-01-01

The large quantities of data now being transferred via high-speed networks have made deep packet inspection indispensable for security purposes. Scalable and low-cost signature-based network intrusion detection systems have been developed for deep packet inspection for various software platforms. Traditional approaches that only involve central processing units (CPUs) are now considered inadequate in terms of inspection speed. Graphic processing units (GPUs) have superior parallel processing power, but transmission bottlenecks can reduce optimal GPU efficiency. In this paper we describe our proposal for a hybrid CPU/GPU pattern-matching algorithm (HPMA) that divides and distributes the packet-inspecting workload between a CPU and GPU. All packets are initially inspected by the CPU and filtered using a simple pre-filtering algorithm, and packets that might contain malicious content are sent to the GPU for further inspection. Test results indicate that in terms of random payload traffic, the matching speed of our proposed algorithm was 3.4 times and 2.7 times faster than those of the AC-CPU and AC-GPU algorithms, respectively. Further, HPMA achieved higher energy efficiency than the other tested algorithms. PMID:26437335

A Hybrid CPU/GPU Pattern-Matching Algorithm for Deep Packet Inspection.

PubMed

Lee, Chun-Liang; Lin, Yi-Shan; Chen, Yaw-Chung

2015-01-01

The large quantities of data now being transferred via high-speed networks have made deep packet inspection indispensable for security purposes. Scalable and low-cost signature-based network intrusion detection systems have been developed for deep packet inspection for various software platforms. Traditional approaches that only involve central processing units (CPUs) are now considered inadequate in terms of inspection speed. Graphic processing units (GPUs) have superior parallel processing power, but transmission bottlenecks can reduce optimal GPU efficiency. In this paper we describe our proposal for a hybrid CPU/GPU pattern-matching algorithm (HPMA) that divides and distributes the packet-inspecting workload between a CPU and GPU. All packets are initially inspected by the CPU and filtered using a simple pre-filtering algorithm, and packets that might contain malicious content are sent to the GPU for further inspection. Test results indicate that in terms of random payload traffic, the matching speed of our proposed algorithm was 3.4 times and 2.7 times faster than those of the AC-CPU and AC-GPU algorithms, respectively. Further, HPMA achieved higher energy efficiency than the other tested algorithms.

GAMUT: GPU accelerated microRNA analysis to uncover target genes through CUDA-miRanda

PubMed Central

2014-01-01

Background Non-coding sequences such as microRNAs have important roles in disease processes. Computational microRNA target identification (CMTI) is becoming increasingly important since traditional experimental methods for target identification pose many difficulties. These methods are time-consuming, costly, and often need guidance from computational methods to narrow down candidate genes anyway. However, most CMTI methods are computationally demanding, since they need to handle not only several million query microRNA and reference RNA pairs, but also several million nucleotide comparisons within each given pair. Thus, the need to perform microRNA identification at such large scale has increased the demand for parallel computing. Methods Although most CMTI programs (e.g., the miRanda algorithm) are based on a modified Smith-Waterman (SW) algorithm, the existing parallel SW implementations (e.g., CUDASW++ 2.0/3.0, SWIPE) are unable to meet this demand in CMTI tasks. We present CUDA-miRanda, a fast microRNA target identification algorithm that takes advantage of massively parallel computing on Graphics Processing Units (GPU) using NVIDIA's Compute Unified Device Architecture (CUDA). CUDA-miRanda specifically focuses on the local alignment of short (i.e., ≤ 32 nucleotides) sequences against longer reference sequences (e.g., 20K nucleotides). Moreover, the proposed algorithm is able to report multiple alignments (up to 191 top scores) and the corresponding traceback sequences for any given (query sequence, reference sequence) pair. Results Speeds over 5.36 Giga Cell Updates Per Second (GCUPs) are achieved on a server with 4 NVIDIA Tesla M2090 GPUs. Compared to the original miRanda algorithm, which is evaluated on an Intel Xeon E5620@2.4 GHz CPU, the experimental results show up to 166 times performance gains in terms of execution time. In addition, we have verified that the exact same targets were predicted in both CUDA-miRanda and the original miRanda implementations through multiple test datasets. Conclusions We offer a GPU-based alternative to high performance compute (HPC) that can be developed locally at a relatively small cost. The community of GPU developers in the biomedical research community, particularly for genome analysis, is still growing. With increasing shared resources, this community will be able to advance CMTI in a very significant manner. Our source code is available at https://sourceforge.net/projects/cudamiranda/. PMID:25077821

Heterogeneous real-time computing in radio astronomy

NASA Astrophysics Data System (ADS)

Ford, John M.; Demorest, Paul; Ransom, Scott

2010-07-01

Modern computer architectures suited for general purpose computing are often not the best choice for either I/O-bound or compute-bound problems. Sometimes the best choice is not to choose a single architecture, but to take advantage of the best characteristics of different computer architectures to solve your problems. This paper examines the tradeoffs between using computer systems based on the ubiquitous X86 Central Processing Units (CPU's), Field Programmable Gate Array (FPGA) based signal processors, and Graphical Processing Units (GPU's). We will show how a heterogeneous system can be produced that blends the best of each of these technologies into a real-time signal processing system. FPGA's tightly coupled to analog-to-digital converters connect the instrument to the telescope and supply the first level of computing to the system. These FPGA's are coupled to other FPGA's to continue to provide highly efficient processing power. Data is then packaged up and shipped over fast networks to a cluster of general purpose computers equipped with GPU's, which are used for floating-point intensive computation. Finally, the data is handled by the CPU and written to disk, or further processed. Each of the elements in the system has been chosen for its specific characteristics and the role it can play in creating a system that does the most for the least, in terms of power, space, and money.

Viscoelastic Finite Difference Modeling Using Graphics Processing Units

NASA Astrophysics Data System (ADS)

Fabien-Ouellet, G.; Gloaguen, E.; Giroux, B.

2014-12-01

Full waveform seismic modeling requires a huge amount of computing power that still challenges today's technology. This limits the applicability of powerful processing approaches in seismic exploration like full-waveform inversion. This paper explores the use of Graphics Processing Units (GPU) to compute a time based finite-difference solution to the viscoelastic wave equation. The aim is to investigate whether the adoption of the GPU technology is susceptible to reduce significantly the computing time of simulations. The code presented herein is based on the freely accessible software of Bohlen (2002) in 2D provided under a General Public License (GNU) licence. This implementation is based on a second order centred differences scheme to approximate time differences and staggered grid schemes with centred difference of order 2, 4, 6, 8, and 12 for spatial derivatives. The code is fully parallel and is written using the Message Passing Interface (MPI), and it thus supports simulations of vast seismic models on a cluster of CPUs. To port the code from Bohlen (2002) on GPUs, the OpenCl framework was chosen for its ability to work on both CPUs and GPUs and its adoption by most of GPU manufacturers. In our implementation, OpenCL works in conjunction with MPI, which allows computations on a cluster of GPU for large-scale model simulations. We tested our code for model sizes between 1002 and 60002 elements. Comparison shows a decrease in computation time of more than two orders of magnitude between the GPU implementation run on a AMD Radeon HD 7950 and the CPU implementation run on a 2.26 GHz Intel Xeon Quad-Core. The speed-up varies depending on the order of the finite difference approximation and generally increases for higher orders. Increasing speed-ups are also obtained for increasing model size, which can be explained by kernel overheads and delays introduced by memory transfers to and from the GPU through the PCI-E bus. Those tests indicate that the GPU memory size and the slow memory transfers are the limiting factors of our GPU implementation. Those results show the benefits of using GPUs instead of CPUs for time based finite-difference seismic simulations. The reductions in computation time and in hardware costs are significant and open the door for new approaches in seismic inversion.

GPU based cloud system for high-performance arrhythmia detection with parallel k-NN algorithm.

PubMed

Tae Joon Jun; Hyun Ji Park; Hyuk Yoo; Young-Hak Kim; Daeyoung Kim

2016-08-01

In this paper, we propose an GPU based Cloud system for high-performance arrhythmia detection. Pan-Tompkins algorithm is used for QRS detection and we optimized beat classification algorithm with K-Nearest Neighbor (K-NN). To support high performance beat classification on the system, we parallelized beat classification algorithm with CUDA to execute the algorithm on virtualized GPU devices on the Cloud system. MIT-BIH Arrhythmia database is used for validation of the algorithm. The system achieved about 93.5% of detection rate which is comparable to previous researches while our algorithm shows 2.5 times faster execution time compared to CPU only detection algorithm.

Parallelized computation for computer simulation of electrocardiograms using personal computers with multi-core CPU and general-purpose GPU.

PubMed

Shen, Wenfeng; Wei, Daming; Xu, Weimin; Zhu, Xin; Yuan, Shizhong

2010-10-01

Biological computations like electrocardiological modelling and simulation usually require high-performance computing environments. This paper introduces an implementation of parallel computation for computer simulation of electrocardiograms (ECGs) in a personal computer environment with an Intel CPU of Core (TM) 2 Quad Q6600 and a GPU of Geforce 8800GT, with software support by OpenMP and CUDA. It was tested in three parallelization device setups: (a) a four-core CPU without a general-purpose GPU, (b) a general-purpose GPU plus 1 core of CPU, and (c) a four-core CPU plus a general-purpose GPU. To effectively take advantage of a multi-core CPU and a general-purpose GPU, an algorithm based on load-prediction dynamic scheduling was developed and applied to setting (c). In the simulation with 1600 time steps, the speedup of the parallel computation as compared to the serial computation was 3.9 in setting (a), 16.8 in setting (b), and 20.0 in setting (c). This study demonstrates that a current PC with a multi-core CPU and a general-purpose GPU provides a good environment for parallel computations in biological modelling and simulation studies. Copyright 2010 Elsevier Ireland Ltd. All rights reserved.

GASPRNG: GPU accelerated scalable parallel random number generator library

NASA Astrophysics Data System (ADS)

Gao, Shuang; Peterson, Gregory D.

2013-04-01

Graphics processors represent a promising technology for accelerating computational science applications. Many computational science applications require fast and scalable random number generation with good statistical properties, so they use the Scalable Parallel Random Number Generators library (SPRNG). We present the GPU Accelerated SPRNG library (GASPRNG) to accelerate SPRNG in GPU-based high performance computing systems. GASPRNG includes code for a host CPU and CUDA code for execution on NVIDIA graphics processing units (GPUs) along with a programming interface to support various usage models for pseudorandom numbers and computational science applications executing on the CPU, GPU, or both. This paper describes the implementation approach used to produce high performance and also describes how to use the programming interface. The programming interface allows a user to be able to use GASPRNG the same way as SPRNG on traditional serial or parallel computers as well as to develop tightly coupled programs executing primarily on the GPU. We also describe how to install GASPRNG and use it. To help illustrate linking with GASPRNG, various demonstration codes are included for the different usage models. GASPRNG on a single GPU shows up to 280x speedup over SPRNG on a single CPU core and is able to scale for larger systems in the same manner as SPRNG. Because GASPRNG generates identical streams of pseudorandom numbers as SPRNG, users can be confident about the quality of GASPRNG for scalable computational science applications. Catalogue identifier: AEOI_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEOI_v1_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: UTK license. No. of lines in distributed program, including test data, etc.: 167900 No. of bytes in distributed program, including test data, etc.: 1422058 Distribution format: tar.gz Programming language: C and CUDA. Computer: Any PC or workstation with NVIDIA GPU (Tested on Fermi GTX480, Tesla C1060, Tesla M2070). Operating system: Linux with CUDA version 4.0 or later. Should also run on MacOS, Windows, or UNIX. Has the code been vectorized or parallelized?: Yes. Parallelized using MPI directives. RAM: 512 MB˜ 732 MB (main memory on host CPU, depending on the data type of random numbers.) / 512 MB (GPU global memory) Classification: 4.13, 6.5. Nature of problem: Many computational science applications are able to consume large numbers of random numbers. For example, Monte Carlo simulations are able to consume limitless random numbers for the computation as long as resources for the computing are supported. Moreover, parallel computational science applications require independent streams of random numbers to attain statistically significant results. The SPRNG library provides this capability, but at a significant computational cost. The GASPRNG library presented here accelerates the generators of independent streams of random numbers using graphical processing units (GPUs). Solution method: Multiple copies of random number generators in GPUs allow a computational science application to consume large numbers of random numbers from independent, parallel streams. GASPRNG is a random number generators library to allow a computational science application to employ multiple copies of random number generators to boost performance. Users can interface GASPRNG with software code executing on microprocessors and/or GPUs. Running time: The tests provided take a few minutes to run.

Ultrasound-directed robotic system for thermal ablation of liver tumors: a preliminary report

NASA Astrophysics Data System (ADS)

Zheng, Jian; Tian, Jie; Dai, Yakang; Zhang, Xing; Dong, Di; Xu, Min

2010-03-01

Thermal ablation has been proved safe and effective as the treatment for liver tumors that are not suitable for resection. Currently, manually performed thermal ablation is greatly dependent on the surgeon's acupuncture manipulation against hand tremor. Besides that, inaccurate or inappropriate placement of the applicator will also directly decrease the final treatment effect. In order to reduce the influence of hand tremor, and provide an accurate and appropriate guidance for a better treatment, we develop an ultrasound-directed robotic system for thermal ablation of liver tumors. In this paper, we will give a brief preliminary report of our system. Especially, three innovative techniques are proposed to solve the critical problems in our system: accurate ultrasound calibration when met with artifacts, realtime reconstruction with visualization using Graphic Processing Unit (GPU) acceleration and 2D-3D ultrasound image registration. To reduce the error of point extraction with artifacts, we propose a novel point extraction method by minimizing an error function which is defined based on the geometric property of our N-fiducial phantom. Then realtime reconstruction with visualization using GPU acceleration is provided for fast 3D ultrasound volume acquisition with dynamic display of reconstruction progress. After that, coarse 2D-3D ultrasound image registration is performed based on landmark points correspondences, followed by accurate 2D-3D ultrasound image registration based on Euclidean distance transform (EDT). The effectiveness of our proposed techniques is demonstrated in phantom experiments.

Streaming parallel GPU acceleration of large-scale filter-based spiking neural networks.

PubMed

Slażyński, Leszek; Bohte, Sander

2012-01-01

The arrival of graphics processing (GPU) cards suitable for massively parallel computing promises affordable large-scale neural network simulation previously only available at supercomputing facilities. While the raw numbers suggest that GPUs may outperform CPUs by at least an order of magnitude, the challenge is to develop fine-grained parallel algorithms to fully exploit the particulars of GPUs. Computation in a neural network is inherently parallel and thus a natural match for GPU architectures: given inputs, the internal state for each neuron can be updated in parallel. We show that for filter-based spiking neurons, like the Spike Response Model, the additive nature of membrane potential dynamics enables additional update parallelism. This also reduces the accumulation of numerical errors when using single precision computation, the native precision of GPUs. We further show that optimizing simulation algorithms and data structures to the GPU's architecture has a large pay-off: for example, matching iterative neural updating to the memory architecture of the GPU speeds up this simulation step by a factor of three to five. With such optimizations, we can simulate in better-than-realtime plausible spiking neural networks of up to 50 000 neurons, processing over 35 million spiking events per second.

Integrated Fast-Ignition Core-Heating Experiments on OMEGA

NASA Astrophysics Data System (ADS)

Theobald, W.

2010-11-01

Integrated fast-ignition core-heating experiments are carried out at the Omega Laser Facility. Plastic (CD) shell targets with a re-entrant gold cone are compressed with a ˜20-kJ, UV low-adiabat laser pulse. A 1-kJ, 10-ps pulse from OMEGA EP generates fast electrons in the hollow cone that are transported into the compressed core. The experiments demonstrate a significant enhancement of the neutron yield. The neutron-yield enhancement caused by the high-intensity pulse is 1.5 x 10^7, which is more than 150% of the implosion yield. For the first time, measurements of the breakout time of the compression-induced shock wave through the cone were performed for the same targets as used in the integrated experiments. The shock breakout was measured to be ˜100 ps after peak neutron production. The experiments demonstrate that the cone tip is intact at the time when the short-pulse laser interacts with the cone. This work was supported by the U.S. Department of Energy Office of Inertial Confinement Fusion under Cooperative Agreement Nos. DE-FC52-08NA28302, DE-FC02-04ER54789, and DE-FG02-05ER54839. [4pt] In collaboration with A. A. Solodov, K. S. Anderson, R. Betti (LLE/FSC); C. Stoeckl, T.R. Boehly, R.S. Craxton, J.A. Delettrez, V.Yu. Glebov, J.P. Knauer, F.J. Marshall, K.L. Marshall, D.D. Meyerhofer,^ P.M. Nilson, T.C. Sangster, W. Seka (LLE); F.N. Beg (UCSD), H. Habara (ILE), P.K. Patel (LLNL), R.B. Stephens (GA); J.A. Frenje, N. Sinenian (PSFC/MIT).

A GPU-Based Wide-Band Radio Spectrometer

NASA Astrophysics Data System (ADS)

Chennamangalam, Jayanth; Scott, Simon; Jones, Glenn; Chen, Hong; Ford, John; Kepley, Amanda; Lorimer, D. R.; Nie, Jun; Prestage, Richard; Roshi, D. Anish; Wagner, Mark; Werthimer, Dan

2014-12-01

The graphics processing unit has become an integral part of astronomical instrumentation, enabling high-performance online data reduction and accelerated online signal processing. In this paper, we describe a wide-band reconfigurable spectrometer built using an off-the-shelf graphics processing unit card. This spectrometer, when configured as a polyphase filter bank, supports a dual-polarisation bandwidth of up to 1.1 GHz (or a single-polarisation bandwidth of up to 2.2 GHz) on the latest generation of graphics processing units. On the other hand, when configured as a direct fast Fourier transform, the spectrometer supports a dual-polarisation bandwidth of up to 1.4 GHz (or a single-polarisation bandwidth of up to 2.8 GHz).

Graphics processing unit (GPU) real-time infrared scene generation

NASA Astrophysics Data System (ADS)

Christie, Chad L.; Gouthas, Efthimios (Themie); Williams, Owen M.

2007-04-01

VIRSuite, the GPU-based suite of software tools developed at DSTO for real-time infrared scene generation, is described. The tools include the painting of scene objects with radiometrically-associated colours, translucent object generation, polar plot validation and versatile scene generation. Special features include radiometric scaling within the GPU and the presence of zoom anti-aliasing at the core of VIRSuite. Extension of the zoom anti-aliasing construct to cover target embedding and the treatment of translucent objects is described.

A Fast Deep Learning System Using GPU

DTIC Science & Technology

2014-06-01

hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and...widely used in data modeling until three decades later when efficient training algorithm for RBM is invented by Hinton [3] and the computing power is...be trained using most of optimization algorithms , such as BP, conjugate gradient descent (CGD) or Levenberg-Marquardt (LM). The advantage of this

SU-G-TeP1-06: Fast GPU Framework for Four-Dimensional Monte Carlo in Adaptive Intensity Modulated Proton Therapy (IMPT) for Mobile Tumors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Botas, P; Heidelberg University, Heidelberg; Grassberger, C

Purpose: To demonstrate the feasibility of fast Monte Carlo (MC) treatment planning and verification using four-dimensional CT (4DCT) for adaptive IMPT for lung cancer patients. Methods: A validated GPU MC code, gPMC, has been linked to the patient database at our institution and employed to compute the dose-influence matrices (Dij) on the planning CT (pCT). The pCT is an average of the respiratory motion of the patient. The Dijs and patient structures were fed to the optimizer to calculate a treatment plan. To validate the plan against motion, a 4D dose distribution averaged over the possible starting phases is calculatedmore » using the 4DCT and a model of the time structure of the delivered spot map. The dose is accumulated using vector maps created by a GPU-accelerated deformable image registration program (DIR) from each phase of the 4DCT to the reference phase using the B-spline method. Calculation of the Dij matrices and the DIR are performed on a cluster, with each field and vector map calculated in parallel. Results: The Dij production takes ∼3.5s per beamlet for 10e6 protons, depending on the energy and the CT size. Generating a plan with 4D simulation of 1000 spots in 4 fields takes approximately 1h. To test the framework, IMPT plans for 10 lung cancer patients were generated for validation. Differences between the planned and the delivered dose of 19% in dose to some organs at risk and 1.4/21.1% in target mean dose/homogeneity with respect to the plan were observed, suggesting potential for improvement if adaptation is considered. Conclusion: A fast MC treatment planning framework has been developed that allows reliable plan design and verification for mobile targets and adaptation of treatment plans. This will significantly impact treatments for lung tumors, as 4D-MC dose calculations can now become part of planning strategies.« less

Implementation and optimization of ultrasound signal processing algorithms on mobile GPU

NASA Astrophysics Data System (ADS)

Kong, Woo Kyu; Lee, Wooyoul; Kim, Kyu Cheol; Yoo, Yangmo; Song, Tai-Kyong

2014-03-01

A general-purpose graphics processing unit (GPGPU) has been used for improving computing power in medical ultrasound imaging systems. Recently, a mobile GPU becomes powerful to deal with 3D games and videos at high frame rates on Full HD or HD resolution displays. This paper proposes the method to implement ultrasound signal processing on a mobile GPU available in the high-end smartphone (Galaxy S4, Samsung Electronics, Seoul, Korea) with programmable shaders on the OpenGL ES 2.0 platform. To maximize the performance of the mobile GPU, the optimization of shader design and load sharing between vertex and fragment shader was performed. The beamformed data were captured from a tissue mimicking phantom (Model 539 Multipurpose Phantom, ATS Laboratories, Inc., Bridgeport, CT, USA) by using a commercial ultrasound imaging system equipped with a research package (Ultrasonix Touch, Ultrasonix, Richmond, BC, Canada). The real-time performance is evaluated by frame rates while varying the range of signal processing blocks. The implementation method of ultrasound signal processing on OpenGL ES 2.0 was verified by analyzing PSNR with MATLAB gold standard that has the same signal path. CNR was also analyzed to verify the method. From the evaluations, the proposed mobile GPU-based processing method has no significant difference with the processing using MATLAB (i.e., PSNR<52.51 dB). The comparable results of CNR were obtained from both processing methods (i.e., 11.31). From the mobile GPU implementation, the frame rates of 57.6 Hz were achieved. The total execution time was 17.4 ms that was faster than the acquisition time (i.e., 34.4 ms). These results indicate that the mobile GPU-based processing method can support real-time ultrasound B-mode processing on the smartphone.

A GPU-accelerated immersive audio-visual framework for interaction with molecular dynamics using consumer depth sensors.

PubMed

Glowacki, David R; O'Connor, Michael; Calabró, Gaetano; Price, James; Tew, Philip; Mitchell, Thomas; Hyde, Joseph; Tew, David P; Coughtrie, David J; McIntosh-Smith, Simon

2014-01-01

With advances in computational power, the rapidly growing role of computational/simulation methodologies in the physical sciences, and the development of new human-computer interaction technologies, the field of interactive molecular dynamics seems destined to expand. In this paper, we describe and benchmark the software algorithms and hardware setup for carrying out interactive molecular dynamics utilizing an array of consumer depth sensors. The system works by interpreting the human form as an energy landscape, and superimposing this landscape on a molecular dynamics simulation to chaperone the motion of the simulated atoms, affecting both graphics and sonified simulation data. GPU acceleration has been key to achieving our target of 60 frames per second (FPS), giving an extremely fluid interactive experience. GPU acceleration has also allowed us to scale the system for use in immersive 360° spaces with an array of up to ten depth sensors, allowing several users to simultaneously chaperone the dynamics. The flexibility of our platform for carrying out molecular dynamics simulations has been considerably enhanced by wrappers that facilitate fast communication with a portable selection of GPU-accelerated molecular force evaluation routines. In this paper, we describe a 360° atmospheric molecular dynamics simulation we have run in a chemistry/physics education context. We also describe initial tests in which users have been able to chaperone the dynamics of 10-alanine peptide embedded in an explicit water solvent. Using this system, both expert and novice users have been able to accelerate peptide rare event dynamics by 3-4 orders of magnitude.

Photoreceptor counting and montaging of en-face retinal images from an adaptive optics fundus camera

PubMed Central

Xue, Bai; Choi, Stacey S.; Doble, Nathan; Werner, John S.

2008-01-01

A fast and efficient method for quantifying photoreceptor density in images obtained with an en-face flood-illuminated adaptive optics (AO) imaging system is described. To improve accuracy of cone counting, en-face images are analyzed over extended areas. This is achieved with two separate semiautomated algorithms: (1) a montaging algorithm that joins retinal images with overlapping common features without edge effects and (2) a cone density measurement algorithm that counts the individual cones in the montaged image. The accuracy of the cone density measurement algorithm is high, with >97% agreement for a simulated retinal image (of known density, with low contrast) and for AO images from normal eyes when compared with previously reported histological data. Our algorithms do not require spatial regularity in cone packing and are, therefore, useful for counting cones in diseased retinas, as demonstrated for eyes with Stargardt’s macular dystrophy and retinitis pigmentosa. PMID:17429482

Photoreceptor counting and montaging of en-face retinal images from an adaptive optics fundus camera

NASA Astrophysics Data System (ADS)

Xue, Bai; Choi, Stacey S.; Doble, Nathan; Werner, John S.

2007-05-01

A fast and efficient method for quantifying photoreceptor density in images obtained with an en-face flood-illuminated adaptive optics (AO) imaging system is described. To improve accuracy of cone counting, en-face images are analyzed over extended areas. This is achieved with two separate semiautomated algorithms: (1) a montaging algorithm that joins retinal images with overlapping common features without edge effects and (2) a cone density measurement algorithm that counts the individual cones in the montaged image. The accuracy of the cone density measurement algorithm is high, with >97% agreement for a simulated retinal image (of known density, with low contrast) and for AO images from normal eyes when compared with previously reported histological data. Our algorithms do not require spatial regularity in cone packing and are, therefore, useful for counting cones in diseased retinas, as demonstrated for eyes with Stargardt's macular dystrophy and retinitis pigmentosa.

CSBB-ConeExclusion, adapting structure based solution virtual screening to libraries on solid support.

PubMed

Shave, Steven; Auer, Manfred

2013-12-23

Combinatorial chemical libraries produced on solid support offer fast and cost-effective access to a large number of unique compounds. If such libraries are screened directly on-bead, the speed at which chemical space can be explored by chemists is much greater than that addressable using solution based synthesis and screening methods. Solution based screening has a large supporting body of software such as structure-based virtual screening tools which enable the prediction of protein-ligand complexes. Use of these techniques to predict the protein bound complexes of compounds synthesized on solid support neglects to take into account the conjugation site on the small molecule ligand. This may invalidate predicted binding modes, the linker may be clashing with protein atoms. We present CSBB-ConeExclusion, a methodology and computer program which provides a measure of the applicability of solution dockings to solid support. Output is given in the form of statistics for each docking pose, a unique 2D visualization method which can be used to determine applicability at a glance, and automatically generated PyMol scripts allowing visualization of protein atom incursion into a defined exclusion volume. CSBB-ConeExclusion is then exemplarically used to determine the optimum attachment point for a purine library targeting cyclin-dependent kinase 2 CDK2.

Clinical applications of advanced rotational radiation therapy

NASA Astrophysics Data System (ADS)

Nalichowski, Adrian

Purpose: With a fast adoption of emerging technologies, it is critical to fully test and understand its limits and capabilities. In this work we investigate new graphic processing unit (GPU) based treatment planning algorithm and its applications in helical tomotherapy dose delivery. We explore the limits of the system by applying it to challenging clinical cases of total marrow irradiation (TMI) and stereotactic radiosurgery (SRS). We also analyze the feasibility of alternative fractionation schemes for total body irradiation (TBI) and TMI based on reported historical data on lung dose and interstitial pneumonitis (IP) incidence rates. Methods and Materials: An anthropomorphic phantom was used to create TMI plans using the new GPU based treatment planning system and the existing CPU cluster based system. Optimization parameters were selected based on clinically used values for field width, modulation factor and pitch. Treatment plans were also created on Eclipse treatment planning system (Varian Medical Systems Inc, Palo Alto, CA) using volumetric modulated arc therapy (VMAT) for dose delivery on IX treatment unit. A retrospective review was performed of 42 publications that reported IP rates along with lung dose, fractionation regimen, dose rate and chemotherapy. The analysis consisted of nearly thirty two hundred patients and 34 unique radiation regimens. Multivariate logistic regression was performed to determine parameters associated with IP and establish does response function. Results: The results showed very good dosimetric agreement between the GPU and CPU calculated plans. The results from SBRT study show that GPU planning system can maintain 90% target coverage while meeting all the constraints of RTOG 0631 protocol. Beam on time for Tomotherapy and flattening filter free RapidArc was much faster than for Vero or Cyberknife. Retrospective data analysis showed that lung dose and Cyclophosphomide (Cy) are both predictors of IP in TBI/TMI treatments. The dose rate was not found to be an independent risk factor for IP. The model failed to establish accurate dose response function, but the discrete data indicated a radiation dose threshold of 7.6Gy (EQD2_repair) and 120 mg/kg of Cy below which no IP cases were reported. Conclusion: The TomoTherapy GPU based dose engine is capable of calculating TMI treatment plans with plan quality nearly identical to plans calculated using the traditional CPU/cluster based system, while significantly reducing the time required for optimization and dose calculation. The new system was able to achieve more uniform dose distribution throughout the target volume and steeper dose fall off, resulting in superior OAR sparing when compared to Eclipse treatment planning system for VMAT delivery. The machine optimization parameters tested for TMI cases provide a comprehensive overview of the capabilities of the treatment planning station and associated helical delivery system. The new system also proved to be dosimetrically compatible with other leading modalities for treatments of small and complicated target volumes and was even superior when treatment delivery times were compared. These finding demonstrate that the advanced treatment planning and delivery system from TomoTherapy is well suitable for treatments of complicated cases such as TMI and SRS and it's often dosimetrically and/or logistically superior to other modalities. The new planning system can easily meet the constraint of threshold lung dose established in this study. The results presented here on the capabilities of Tomotherapy and on the identified lung dose threshold provide an opportunity to explore alternative fractionation schemes without sacrificing target coverage or lung toxicity. (Abstract shortened by ProQuest.).

Accelerated GPU based SPECT Monte Carlo simulations.

PubMed

Garcia, Marie-Paule; Bert, Julien; Benoit, Didier; Bardiès, Manuel; Visvikis, Dimitris

2016-06-07

Monte Carlo (MC) modelling is widely used in the field of single photon emission computed tomography (SPECT) as it is a reliable technique to simulate very high quality scans. This technique provides very accurate modelling of the radiation transport and particle interactions in a heterogeneous medium. Various MC codes exist for nuclear medicine imaging simulations. Recently, new strategies exploiting the computing capabilities of graphical processing units (GPU) have been proposed. This work aims at evaluating the accuracy of such GPU implementation strategies in comparison to standard MC codes in the context of SPECT imaging. GATE was considered the reference MC toolkit and used to evaluate the performance of newly developed GPU Geant4-based Monte Carlo simulation (GGEMS) modules for SPECT imaging. Radioisotopes with different photon energies were used with these various CPU and GPU Geant4-based MC codes in order to assess the best strategy for each configuration. Three different isotopes were considered: (99m) Tc, (111)In and (131)I, using a low energy high resolution (LEHR) collimator, a medium energy general purpose (MEGP) collimator and a high energy general purpose (HEGP) collimator respectively. Point source, uniform source, cylindrical phantom and anthropomorphic phantom acquisitions were simulated using a model of the GE infinia II 3/8" gamma camera. Both simulation platforms yielded a similar system sensitivity and image statistical quality for the various combinations. The overall acceleration factor between GATE and GGEMS platform derived from the same cylindrical phantom acquisition was between 18 and 27 for the different radioisotopes. Besides, a full MC simulation using an anthropomorphic phantom showed the full potential of the GGEMS platform, with a resulting acceleration factor up to 71. The good agreement with reference codes and the acceleration factors obtained support the use of GPU implementation strategies for improving computational efficiency of SPECT imaging simulations.

CPU architecture for a fast and energy-saving calculation of convolution neural networks

NASA Astrophysics Data System (ADS)

Knoll, Florian J.; Grelcke, Michael; Czymmek, Vitali; Holtorf, Tim; Hussmann, Stephan

2017-06-01

One of the most difficult problem in the use of artificial neural networks is the computational capacity. Although large search engine companies own specially developed hardware to provide the necessary computing power, for the conventional user only remains the state of the art method, which is the use of a graphic processing unit (GPU) as a computational basis. Although these processors are well suited for large matrix computations, they need massive energy. Therefore a new processor on the basis of a field programmable gate array (FPGA) has been developed and is optimized for the application of deep learning. This processor is presented in this paper. The processor can be adapted for a particular application (in this paper to an organic farming application). The power consumption is only a fraction of a GPU application and should therefore be well suited for energy-saving applications.

Real-time digital holographic microscopy using the graphic processing unit.

PubMed

Shimobaba, Tomoyoshi; Sato, Yoshikuni; Miura, Junya; Takenouchi, Mai; Ito, Tomoyoshi

2008-08-04

Digital holographic microscopy (DHM) is a well-known powerful method allowing both the amplitude and phase of a specimen to be simultaneously observed. In order to obtain a reconstructed image from a hologram, numerous calculations for the Fresnel diffraction are required. The Fresnel diffraction can be accelerated by the FFT (Fast Fourier Transform) algorithm. However, real-time reconstruction from a hologram is difficult even if we use a recent central processing unit (CPU) to calculate the Fresnel diffraction by the FFT algorithm. In this paper, we describe a real-time DHM system using a graphic processing unit (GPU) with many stream processors, which allows use as a highly parallel processor. The computational speed of the Fresnel diffraction using the GPU is faster than that of recent CPUs. The real-time DHM system can obtain reconstructed images from holograms whose size is 512 x 512 grids in 24 frames per second.

Searching for Single Pulses Using Heimdall

NASA Astrophysics Data System (ADS)

Walsh, Gregory; Lynch, Ryan

2018-01-01

In radio pulsar surveys, the interstellar medium causes a frequency dependent dispersive delay of a pulsed signal across the observing band. If not corrected, this delay substantially lowers S/N and makes most pulses undetectable. The delay is proportional to an unknown dispersion measure (DM), which must be searched over with many trial values. A number of new, GPU-accelerated codes are now available to optimize this dedispersion task, and to search for transient pulsed radio emission. We report on the use of Heimdall, one such GPU-accelerated tree dedispersion utility, to search for transient radio sources in a Green Bank Telescope survey of the Cygnus Region and North Galactic Plane. The survey is carried out at central frequency of 820 MHz with a goal of finding Fast Radio Bursts, Rotating Radio Transients, young pulsars, and millisecond pulsars. We describe the the survey, data processing pipeline, and follow-up of candidate sources.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yanagawa, T.; Sakagami, H.; Nagatomo, H.

In inertial confinement fusion, the implosion process is important in forming a high-density plasma core. In the case of a fast ignition scheme using a cone-guided target, the fuel target is imploded with a cone inserted. This scheme is advantageous for efficiently heating the imploded fuel core; however, asymmetric implosion is essentially inevitable. Moreover, the effect of cone position and opening angle on implosion also becomes critical. Focusing on these problems, the effect of the asymmetric implosion, the initial position, and the opening angle on the compression rate of the fuel is investigated using a three-dimensional pure hydrodynamic code.

SU-E-J-60: Efficient Monte Carlo Dose Calculation On CPU-GPU Heterogeneous Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xiao, K; Chen, D. Z; Hu, X. S

Purpose: It is well-known that the performance of GPU-based Monte Carlo dose calculation implementations is bounded by memory bandwidth. One major cause of this bottleneck is the random memory writing patterns in dose deposition, which leads to several memory efficiency issues on GPU such as un-coalesced writing and atomic operations. We propose a new method to alleviate such issues on CPU-GPU heterogeneous systems, which achieves overall performance improvement for Monte Carlo dose calculation. Methods: Dose deposition is to accumulate dose into the voxels of a dose volume along the trajectories of radiation rays. Our idea is to partition this proceduremore » into the following three steps, which are fine-tuned for CPU or GPU: (1) each GPU thread writes dose results with location information to a buffer on GPU memory, which achieves fully-coalesced and atomic-free memory transactions; (2) the dose results in the buffer are transferred to CPU memory; (3) the dose volume is constructed from the dose buffer on CPU. We organize the processing of all radiation rays into streams. Since the steps within a stream use different hardware resources (i.e., GPU, DMA, CPU), we can overlap the execution of these steps for different streams by pipelining. Results: We evaluated our method using a Monte Carlo Convolution Superposition (MCCS) program and tested our implementation for various clinical cases on a heterogeneous system containing an Intel i7 quad-core CPU and an NVIDIA TITAN GPU. Comparing with a straightforward MCCS implementation on the same system (using both CPU and GPU for radiation ray tracing), our method gained 2-5X speedup without losing dose calculation accuracy. Conclusion: The results show that our new method improves the effective memory bandwidth and overall performance for MCCS on the CPU-GPU systems. Our proposed method can also be applied to accelerate other Monte Carlo dose calculation approaches. This research was supported in part by NSF under Grants CCF-1217906, and also in part by a research contract from the Sandia National Laboratories.« less

Irregular large-scale computed tomography on multiple graphics processors improves energy-efficiency metrics for industrial applications

NASA Astrophysics Data System (ADS)

Jimenez, Edward S.; Goodman, Eric L.; Park, Ryeojin; Orr, Laurel J.; Thompson, Kyle R.

2014-09-01

This paper will investigate energy-efficiency for various real-world industrial computed-tomography reconstruction algorithms, both CPU- and GPU-based implementations. This work shows that the energy required for a given reconstruction is based on performance and problem size. There are many ways to describe performance and energy efficiency, thus this work will investigate multiple metrics including performance-per-watt, energy-delay product, and energy consumption. This work found that irregular GPU-based approaches1 realized tremendous savings in energy consumption when compared to CPU implementations while also significantly improving the performance-per- watt and energy-delay product metrics. Additional energy savings and other metric improvement was realized on the GPU-based reconstructions by improving storage I/O by implementing a parallel MIMD-like modularization of the compute and I/O tasks.

GPU accelerated real-time confocal fluorescence lifetime imaging microscopy (FLIM) based on the analog mean-delay (AMD) method

PubMed Central

Kim, Byungyeon; Park, Byungjun; Lee, Seungrag; Won, Youngjae

2016-01-01

We demonstrated GPU accelerated real-time confocal fluorescence lifetime imaging microscopy (FLIM) based on the analog mean-delay (AMD) method. Our algorithm was verified for various fluorescence lifetimes and photon numbers. The GPU processing time was faster than the physical scanning time for images up to 800 × 800, and more than 149 times faster than a single core CPU. The frame rate of our system was demonstrated to be 13 fps for a 200 × 200 pixel image when observing maize vascular tissue. This system can be utilized for observing dynamic biological reactions, medical diagnosis, and real-time industrial inspection. PMID:28018724

SU-F-T-256: 4D IMRT Planning Using An Early Prototype GPU-Enabled Eclipse Workstation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hagan, A; Modiri, A; Sawant, A

Purpose: True 4D IMRT planning, based on simultaneous spatiotemporal optimization has been shown to significantly improve plan quality in lung radiotherapy. However, the high computational complexity associated with such planning represents a significant barrier to widespread clinical deployment. We introduce an early prototype GPU-enabled Eclipse workstation for inverse planning. To our knowledge, this is the first GPUintegrated Eclipse system demonstrating the potential for clinical translation of GPU computing on a major commercially-available TPS. Methods: The prototype system comprised of four NVIDIA Tesla K80 GPUs, with a maximum processing capability of 8.5 Tflops per K80 card. The system architecture consisted ofmore » three key modules: (i) a GPU-based inverse planning module using a highly-parallelizable, swarm intelligence-based global optimization algorithm, (ii) a GPU-based open-source b-spline deformable image registration module, Elastix, and (iii) a CUDA-based data management module. For evaluation, aperture fluence weights in an IMRT plan were optimized over 9 beams,166 apertures and 10 respiratory phases (14940 variables) for a lung cancer case (GTV = 95 cc, right lower lobe, 15 mm cranio-caudal motion). Sensitivity of the planning time and memory expense to parameter variations was quantified. Results: GPU-based inverse planning was significantly accelerated compared to its CPU counterpart (36 vs 488 min, for 10 phases, 10 search agents and 10 iterations). The optimized IMRT plan significantly improved OAR sparing compared to the original internal target volume (ITV)-based clinical plan, while maintaining prescribed tumor coverage. The dose-sparing improvements were: Esophagus Dmax 50%, Heart Dmax 42% and Spinal cord Dmax 25%. Conclusion: Our early prototype system demonstrates that through massive parallelization, computationally intense tasks such as 4D treatment planning can be accomplished in clinically feasible timeframes. With further optimization, such systems are expected to enable the eventual clinical translation of higher-dimensional and complex treatment planning strategies to significantly improve plan quality. This work was partially supported through research funding from National Institutes of Health (R01CA169102) and Varian Medical Systems, Palo Alto, CA, USA.« less

High Performance GPU-Based Fourier Volume Rendering.

PubMed

Abdellah, Marwan; Eldeib, Ayman; Sharawi, Amr

2015-01-01

Fourier volume rendering (FVR) is a significant visualization technique that has been used widely in digital radiography. As a result of its (N (2)log⁡N) time complexity, it provides a faster alternative to spatial domain volume rendering algorithms that are (N (3)) computationally complex. Relying on the Fourier projection-slice theorem, this technique operates on the spectral representation of a 3D volume instead of processing its spatial representation to generate attenuation-only projections that look like X-ray radiographs. Due to the rapid evolution of its underlying architecture, the graphics processing unit (GPU) became an attractive competent platform that can deliver giant computational raw power compared to the central processing unit (CPU) on a per-dollar-basis. The introduction of the compute unified device architecture (CUDA) technology enables embarrassingly-parallel algorithms to run efficiently on CUDA-capable GPU architectures. In this work, a high performance GPU-accelerated implementation of the FVR pipeline on CUDA-enabled GPUs is presented. This proposed implementation can achieve a speed-up of 117x compared to a single-threaded hybrid implementation that uses the CPU and GPU together by taking advantage of executing the rendering pipeline entirely on recent GPU architectures.

A novel heterogeneous algorithm to simulate multiphase flow in porous media on multicore CPU-GPU systems

NASA Astrophysics Data System (ADS)

McClure, J. E.; Prins, J. F.; Miller, C. T.

2014-07-01

Multiphase flow implementations of the lattice Boltzmann method (LBM) are widely applied to the study of porous medium systems. In this work, we construct a new variant of the popular "color" LBM for two-phase flow in which a three-dimensional, 19-velocity (D3Q19) lattice is used to compute the momentum transport solution while a three-dimensional, seven velocity (D3Q7) lattice is used to compute the mass transport solution. Based on this formulation, we implement a novel heterogeneous GPU-accelerated algorithm in which the mass transport solution is computed by multiple shared memory CPU cores programmed using OpenMP while a concurrent solution of the momentum transport is performed using a GPU. The heterogeneous solution is demonstrated to provide speedup of 2.6 × as compared to multi-core CPU solution and 1.8 × compared to GPU solution due to concurrent utilization of both CPU and GPU bandwidths. Furthermore, we verify that the proposed formulation provides an accurate physical representation of multiphase flow processes and demonstrate that the approach can be applied to perform heterogeneous simulations of two-phase flow in porous media using a typical GPU-accelerated workstation.

Optimizing a mobile robot control system using GPU acceleration

NASA Astrophysics Data System (ADS)

Tuck, Nat; McGuinness, Michael; Martin, Fred

2012-01-01

This paper describes our attempt to optimize a robot control program for the Intelligent Ground Vehicle Competition (IGVC) by running computationally intensive portions of the system on a commodity graphics processing unit (GPU). The IGVC Autonomous Challenge requires a control program that performs a number of different computationally intensive tasks ranging from computer vision to path planning. For the 2011 competition our Robot Operating System (ROS) based control system would not run comfortably on the multicore CPU on our custom robot platform. The process of profiling the ROS control program and selecting appropriate modules for porting to run on a GPU is described. A GPU-targeting compiler, Bacon, is used to speed up development and help optimize the ported modules. The impact of the ported modules on overall performance is discussed. We conclude that GPU optimization can free a significant amount of CPU resources with minimal effort for expensive user-written code, but that replacing heavily-optimized library functions is more difficult, and a much less efficient use of time.

Production Level CFD Code Acceleration for Hybrid Many-Core Architectures

NASA Technical Reports Server (NTRS)

Duffy, Austen C.; Hammond, Dana P.; Nielsen, Eric J.

2012-01-01

In this work, a novel graphics processing unit (GPU) distributed sharing model for hybrid many-core architectures is introduced and employed in the acceleration of a production-level computational fluid dynamics (CFD) code. The latest generation graphics hardware allows multiple processor cores to simultaneously share a single GPU through concurrent kernel execution. This feature has allowed the NASA FUN3D code to be accelerated in parallel with up to four processor cores sharing a single GPU. For codes to scale and fully use resources on these and the next generation machines, codes will need to employ some type of GPU sharing model, as presented in this work. Findings include the effects of GPU sharing on overall performance. A discussion of the inherent challenges that parallel unstructured CFD codes face in accelerator-based computing environments is included, with considerations for future generation architectures. This work was completed by the author in August 2010, and reflects the analysis and results of the time.

cellGPU: Massively parallel simulations of dynamic vertex models

NASA Astrophysics Data System (ADS)

Sussman, Daniel M.

2017-10-01

Vertex models represent confluent tissue by polygonal or polyhedral tilings of space, with the individual cells interacting via force laws that depend on both the geometry of the cells and the topology of the tessellation. This dependence on the connectivity of the cellular network introduces several complications to performing molecular-dynamics-like simulations of vertex models, and in particular makes parallelizing the simulations difficult. cellGPU addresses this difficulty and lays the foundation for massively parallelized, GPU-based simulations of these models. This article discusses its implementation for a pair of two-dimensional models, and compares the typical performance that can be expected between running cellGPU entirely on the CPU versus its performance when running on a range of commercial and server-grade graphics cards. By implementing the calculation of topological changes and forces on cells in a highly parallelizable fashion, cellGPU enables researchers to simulate time- and length-scales previously inaccessible via existing single-threaded CPU implementations. Program Files doi:http://dx.doi.org/10.17632/6j2cj29t3r.1 Licensing provisions: MIT Programming language: CUDA/C++ Nature of problem: Simulations of off-lattice "vertex models" of cells, in which the interaction forces depend on both the geometry and the topology of the cellular aggregate. Solution method: Highly parallelized GPU-accelerated dynamical simulations in which the force calculations and the topological features can be handled on either the CPU or GPU. Additional comments: The code is hosted at https://gitlab.com/dmsussman/cellGPU, with documentation additionally maintained at http://dmsussman.gitlab.io/cellGPUdocumentation

Testing and Validating Gadget2 for GPUs

NASA Astrophysics Data System (ADS)

Wibking, Benjamin; Holley-Bockelmann, K.; Berlind, A. A.

2013-01-01

We are currently upgrading a version of Gadget2 (Springel et al., 2005) that is optimized for NVIDIA's CUDA GPU architecture (Frigaard, unpublished) to work with the latest libraries and graphics cards. Preliminary tests of its performance indicate a ~40x speedup in the particle force tree approximation calculation, with overall speedup of 5-10x for cosmological simulations run with GPUs compared to running on the same CPU cores without GPU acceleration. We believe this speedup can be reasonably increased by an additional factor of two with futher optimization, including overlap of computation on CPU and GPU. Tests of single-precision GPU numerical fidelity currently indicate accuracy of the mass function and the spectral power density to within a few percent of extended-precision CPU results with the unmodified form of Gadget. Additionally, we plan to test and optimize the GPU code for Millenium-scale "grand challenge" simulations of >10^9 particles, a scale that has been previously untested with this code, with the aid of the NSF XSEDE flagship GPU-based supercomputing cluster codenamed "Keeneland." Current work involves additional validation of numerical results, extending the numerical precision of the GPU calculations to double precision, and evaluating performance/accuracy tradeoffs. We believe that this project, if successful, will yield substantial computational performance benefits to the N-body research community as the next generation of GPU supercomputing resources becomes available, both increasing the electrical power efficiency of ever-larger computations (making simulations possible a decade from now at scales and resolutions unavailable today) and accelerating the pace of research in the field.

GPU-accelerated automatic identification of robust beam setups for proton and carbon-ion radiotherapy

NASA Astrophysics Data System (ADS)

Ammazzalorso, F.; Bednarz, T.; Jelen, U.

2014-03-01

We demonstrate acceleration on graphic processing units (GPU) of automatic identification of robust particle therapy beam setups, minimizing negative dosimetric effects of Bragg peak displacement caused by treatment-time patient positioning errors. Our particle therapy research toolkit, RobuR, was extended with OpenCL support and used to implement calculation on GPU of the Port Homogeneity Index, a metric scoring irradiation port robustness through analysis of tissue density patterns prior to dose optimization and computation. Results were benchmarked against an independent native CPU implementation. Numerical results were in agreement between the GPU implementation and native CPU implementation. For 10 skull base cases, the GPU-accelerated implementation was employed to select beam setups for proton and carbon ion treatment plans, which proved to be dosimetrically robust, when recomputed in presence of various simulated positioning errors. From the point of view of performance, average running time on the GPU decreased by at least one order of magnitude compared to the CPU, rendering the GPU-accelerated analysis a feasible step in a clinical treatment planning interactive session. In conclusion, selection of robust particle therapy beam setups can be effectively accelerated on a GPU and become an unintrusive part of the particle therapy treatment planning workflow. Additionally, the speed gain opens new usage scenarios, like interactive analysis manipulation (e.g. constraining of some setup) and re-execution. Finally, through OpenCL portable parallelism, the new implementation is suitable also for CPU-only use, taking advantage of multiple cores, and can potentially exploit types of accelerators other than GPUs.

Real-time photoacoustic and ultrasound dual-modality imaging system facilitated with graphics processing unit and code parallel optimization.

PubMed

Yuan, Jie; Xu, Guan; Yu, Yao; Zhou, Yu; Carson, Paul L; Wang, Xueding; Liu, Xiaojun

2013-08-01

Photoacoustic tomography (PAT) offers structural and functional imaging of living biological tissue with highly sensitive optical absorption contrast and excellent spatial resolution comparable to medical ultrasound (US) imaging. We report the development of a fully integrated PAT and US dual-modality imaging system, which performs signal scanning, image reconstruction, and display for both photoacoustic (PA) and US imaging all in a truly real-time manner. The back-projection (BP) algorithm for PA image reconstruction is optimized to reduce the computational cost and facilitate parallel computation on a state of the art graphics processing unit (GPU) card. For the first time, PAT and US imaging of the same object can be conducted simultaneously and continuously, at a real-time frame rate, presently limited by the laser repetition rate of 10 Hz. Noninvasive PAT and US imaging of human peripheral joints in vivo were achieved, demonstrating the satisfactory image quality realized with this system. Another experiment, simultaneous PAT and US imaging of contrast agent flowing through an artificial vessel, was conducted to verify the performance of this system for imaging fast biological events. The GPU-based image reconstruction software code for this dual-modality system is open source and available for download from http://sourceforge.net/projects/patrealtime.

QuickProbs—A Fast Multiple Sequence Alignment Algorithm Designed for Graphics Processors

PubMed Central

Gudyś, Adam; Deorowicz, Sebastian

2014-01-01

Multiple sequence alignment is a crucial task in a number of biological analyses like secondary structure prediction, domain searching, phylogeny, etc. MSAProbs is currently the most accurate alignment algorithm, but its effectiveness is obtained at the expense of computational time. In the paper we present QuickProbs, the variant of MSAProbs customised for graphics processors. We selected the two most time consuming stages of MSAProbs to be redesigned for GPU execution: the posterior matrices calculation and the consistency transformation. Experiments on three popular benchmarks (BAliBASE, PREFAB, OXBench-X) on quad-core PC equipped with high-end graphics card show QuickProbs to be 5.7 to 9.7 times faster than original CPU-parallel MSAProbs. Additional tests performed on several protein families from Pfam database give overall speed-up of 6.7. Compared to other algorithms like MAFFT, MUSCLE, or ClustalW, QuickProbs proved to be much more accurate at similar speed. Additionally we introduce a tuned variant of QuickProbs which is significantly more accurate on sets of distantly related sequences than MSAProbs without exceeding its computation time. The GPU part of QuickProbs was implemented in OpenCL, thus the package is suitable for graphics processors produced by all major vendors. PMID:24586435

Transform coding for hardware-accelerated volume rendering.

PubMed

Fout, Nathaniel; Ma, Kwan-Liu

2007-01-01

Hardware-accelerated volume rendering using the GPU is now the standard approach for real-time volume rendering, although limited graphics memory can present a problem when rendering large volume data sets. Volumetric compression in which the decompression is coupled to rendering has been shown to be an effective solution to this problem; however, most existing techniques were developed in the context of software volume rendering, and all but the simplest approaches are prohibitive in a real-time hardware-accelerated volume rendering context. In this paper we present a novel block-based transform coding scheme designed specifically with real-time volume rendering in mind, such that the decompression is fast without sacrificing compression quality. This is made possible by consolidating the inverse transform with dequantization in such a way as to allow most of the reprojection to be precomputed. Furthermore, we take advantage of the freedom afforded by off-line compression in order to optimize the encoding as much as possible while hiding this complexity from the decoder. In this context we develop a new block classification scheme which allows us to preserve perceptually important features in the compression. The result of this work is an asymmetric transform coding scheme that allows very large volumes to be compressed and then decompressed in real-time while rendering on the GPU.

Collimated Propagation of Fast Electron Beams Accelerated by High-Contrast Laser Pulses in Highly Resistive Shocked Carbon.

PubMed

Vaisseau, X; Morace, A; Touati, M; Nakatsutsumi, M; Baton, S D; Hulin, S; Nicolaï, Ph; Nuter, R; Batani, D; Beg, F N; Breil, J; Fedosejevs, R; Feugeas, J-L; Forestier-Colleoni, P; Fourment, C; Fujioka, S; Giuffrida, L; Kerr, S; McLean, H S; Sawada, H; Tikhonchuk, V T; Santos, J J

2017-05-19

Collimated transport of ultrahigh intensity electron current was observed in cold and in laser-shocked vitreous carbon, in agreement with simulation predictions. The fast electron beams were created by coupling high-intensity and high-contrast laser pulses onto copper-coated cones drilled into the carbon samples. The guiding mechanism-observed only for times before the shock breakout at the inner cone tip-is due to self-generated resistive magnetic fields of ∼0.5-1  kT arising from the intense currents of fast electrons in vitreous carbon, by virtue of its specific high resistivity over the range of explored background temperatures. The spatial distribution of the electron beams, injected through the samples at different stages of compression, was characterized by side-on imaging of hard x-ray fluorescence.

Integrated simulation of magnetic-field-assist fast ignition laser fusion

NASA Astrophysics Data System (ADS)

Johzaki, T.; Nagatomo, H.; Sunahara, A.; Sentoku, Y.; Sakagami, H.; Hata, M.; Taguchi, T.; Mima, K.; Kai, Y.; Ajimi, D.; Isoda, T.; Endo, T.; Yogo, A.; Arikawa, Y.; Fujioka, S.; Shiraga, H.; Azechi, H.

2017-01-01

To enhance the core heating efficiency in fast ignition laser fusion, the concept of relativistic electron beam guiding by external magnetic fields was evaluated by integrated simulations for FIREX class targets. For the cone-attached shell target case, the core heating performance deteriorates by applying magnetic fields since the core is considerably deformed and most of the fast electrons are reflected due to the magnetic mirror formed through the implosion. On the other hand, in the case of a cone-attached solid ball target, the implosion is more stable under the kilo-tesla-class magnetic field. In addition, feasible magnetic field configuration is formed through the implosion. As a result, the core heating efficiency doubles by magnetic guiding. The dependence of core heating properties on the heating pulse shot timing was also investigated for the solid ball target.

GPU Acceleration of DSP for Communication Receivers.

PubMed

Gunther, Jake; Gunther, Hyrum; Moon, Todd

2017-09-01

Graphics processing unit (GPU) implementations of signal processing algorithms can outperform CPU-based implementations. This paper describes the GPU implementation of several algorithms encountered in a wide range of high-data rate communication receivers including filters, multirate filters, numerically controlled oscillators, and multi-stage digital down converters. These structures are tested by processing the 20 MHz wide FM radio band (88-108 MHz). Two receiver structures are explored: a single channel receiver and a filter bank channelizer. Both run in real time on NVIDIA GeForce GTX 1080 graphics card.

Monte Carlo-based fluorescence molecular tomography reconstruction method accelerated by a cluster of graphic processing units.

PubMed

Quan, Guotao; Gong, Hui; Deng, Yong; Fu, Jianwei; Luo, Qingming

2011-02-01

High-speed fluorescence molecular tomography (FMT) reconstruction for 3-D heterogeneous media is still one of the most challenging problems in diffusive optical fluorescence imaging. In this paper, we propose a fast FMT reconstruction method that is based on Monte Carlo (MC) simulation and accelerated by a cluster of graphics processing units (GPUs). Based on the Message Passing Interface standard, we modified the MC code for fast FMT reconstruction, and different Green's functions representing the flux distribution in media are calculated simultaneously by different GPUs in the cluster. A load-balancing method was also developed to increase the computational efficiency. By applying the Fréchet derivative, a Jacobian matrix is formed to reconstruct the distribution of the fluorochromes using the calculated Green's functions. Phantom experiments have shown that only 10 min are required to get reconstruction results with a cluster of 6 GPUs, rather than 6 h with a cluster of multiple dual opteron CPU nodes. Because of the advantages of high accuracy and suitability for 3-D heterogeneity media with refractive-index-unmatched boundaries from the MC simulation, the GPU cluster-accelerated method provides a reliable approach to high-speed reconstruction for FMT imaging.

Robust and fast characterization of OCT-based optical attenuation using a novel frequency-domain algorithm for brain cancer detection

NASA Astrophysics Data System (ADS)

Yuan, Wu; Kut, Carmen; Liang, Wenxuan; Li, Xingde

2017-03-01

Cancer is known to alter the local optical properties of tissues. The detection of OCT-based optical attenuation provides a quantitative method to efficiently differentiate cancer from non-cancer tissues. In particular, the intraoperative use of quantitative OCT is able to provide a direct visual guidance in real time for accurate identification of cancer tissues, especially these without any obvious structural layers, such as brain cancer. However, current methods are suboptimal in providing high-speed and accurate OCT attenuation mapping for intraoperative brain cancer detection. In this paper, we report a novel frequency-domain (FD) algorithm to enable robust and fast characterization of optical attenuation as derived from OCT intensity images. The performance of this FD algorithm was compared with traditional fitting methods by analyzing datasets containing images from freshly resected human brain cancer and from a silica phantom acquired by a 1310 nm swept-source OCT (SS-OCT) system. With graphics processing unit (GPU)-based CUDA C/C++ implementation, this new attenuation mapping algorithm can offer robust and accurate quantitative interpretation of OCT images in real time during brain surgery.

A parallel finite element procedure for contact-impact problems using edge-based smooth triangular element and GPU

NASA Astrophysics Data System (ADS)

Cai, Yong; Cui, Xiangyang; Li, Guangyao; Liu, Wenyang

2018-04-01

The edge-smooth finite element method (ES-FEM) can improve the computational accuracy of triangular shell elements and the mesh partition efficiency of complex models. In this paper, an approach is developed to perform explicit finite element simulations of contact-impact problems with a graphical processing unit (GPU) using a special edge-smooth triangular shell element based on ES-FEM. Of critical importance for this problem is achieving finer-grained parallelism to enable efficient data loading and to minimize communication between the device and host. Four kinds of parallel strategies are then developed to efficiently solve these ES-FEM based shell element formulas, and various optimization methods are adopted to ensure aligned memory access. Special focus is dedicated to developing an approach for the parallel construction of edge systems. A parallel hierarchy-territory contact-searching algorithm (HITA) and a parallel penalty function calculation method are embedded in this parallel explicit algorithm. Finally, the program flow is well designed, and a GPU-based simulation system is developed, using Nvidia's CUDA. Several numerical examples are presented to illustrate the high quality of the results obtained with the proposed methods. In addition, the GPU-based parallel computation is shown to significantly reduce the computing time.

Fast optically sectioned fluorescence HiLo endomicroscopy

PubMed Central

Lim, Daryl; Mertz, Jerome

2012-01-01

Abstract. We describe a nonscanning, fiber bundle endomicroscope that performs optically sectioned fluorescence imaging with fast frame rates and real-time processing. Our sectioning technique is based on HiLo imaging, wherein two widefield images are acquired under uniform and structured illumination and numerically processed to reject out-of-focus background. This work is an improvement upon an earlier demonstration of widefield optical sectioning through a flexible fiber bundle. The improved device features lateral and axial resolutions of 2.6 and 17 μm, respectively, a net frame rate of 9.5 Hz obtained by real-time image processing with a graphics processing unit (GPU) and significantly reduced motion artifacts obtained by the use of a double-shutter camera. We demonstrate the performance of our system with optically sectioned images and videos of a fluorescently labeled chorioallantoic membrane (CAM) in the developing G. gallus embryo. HiLo endomicroscopy is a candidate technique for low-cost, high-speed clinical optical biopsies. PMID:22463023

Fast Segmentation From Blurred Data in 3D Fluorescence Microscopy.

PubMed

Storath, Martin; Rickert, Dennis; Unser, Michael; Weinmann, Andreas

2017-10-01

We develop a fast algorithm for segmenting 3D images from linear measurements based on the Potts model (or piecewise constant Mumford-Shah model). To that end, we first derive suitable space discretizations of the 3D Potts model, which are capable of dealing with 3D images defined on non-cubic grids. Our discretization allows us to utilize a specific splitting approach, which results in decoupled subproblems of moderate size. The crucial point in the 3D setup is that the number of independent subproblems is so large that we can reasonably exploit the parallel processing capabilities of the graphics processing units (GPUs). Our GPU implementation is up to 18 times faster than the sequential CPU version. This allows to process even large volumes in acceptable runtimes. As a further contribution, we extend the algorithm in order to deal with non-negativity constraints. We demonstrate the efficiency of our method for combined image deconvolution and segmentation on simulated data and on real 3D wide field fluorescence microscopy data.

A new robust algorithm for computation of a triangle circumscribed sphere in E3 and a hypersphere simplex

NASA Astrophysics Data System (ADS)

Skala, Vaclav

2016-06-01

There are many applications in which a bounding sphere containing the given triangle E3 is needed, e.g. fast collision detection, ray-triangle intersecting in raytracing etc. This is a typical geometrical problem in E3 and it has also applications in computational problems in general. In this paper a new fast and robust algorithm of circumscribed sphere computation in the n-dimensional space is presented and specification for the E3 space is given, too. The presented method is convenient for use on GPU or with SSE or Intel's AVX instructions on a standard CPU.

GPU-BSM: A GPU-Based Tool to Map Bisulfite-Treated Reads

PubMed Central

Manconi, Andrea; Orro, Alessandro; Manca, Emanuele; Armano, Giuliano; Milanesi, Luciano

2014-01-01

Cytosine DNA methylation is an epigenetic mark implicated in several biological processes. Bisulfite treatment of DNA is acknowledged as the gold standard technique to study methylation. This technique introduces changes in the genomic DNA by converting cytosines to uracils while 5-methylcytosines remain nonreactive. During PCR amplification 5-methylcytosines are amplified as cytosine, whereas uracils and thymines as thymine. To detect the methylation levels, reads treated with the bisulfite must be aligned against a reference genome. Mapping these reads to a reference genome represents a significant computational challenge mainly due to the increased search space and the loss of information introduced by the treatment. To deal with this computational challenge we devised GPU-BSM, a tool based on modern Graphics Processing Units. Graphics Processing Units are hardware accelerators that are increasingly being used successfully to accelerate general-purpose scientific applications. GPU-BSM is a tool able to map bisulfite-treated reads from whole genome bisulfite sequencing and reduced representation bisulfite sequencing, and to estimate methylation levels, with the goal of detecting methylation. Due to the massive parallelization obtained by exploiting graphics cards, GPU-BSM aligns bisulfite-treated reads faster than other cutting-edge solutions, while outperforming most of them in terms of unique mapped reads. PMID:24842718

GPU based contouring method on grid DEM data

NASA Astrophysics Data System (ADS)

Tan, Liheng; Wan, Gang; Li, Feng; Chen, Xiaohui; Du, Wenlong

2017-08-01

This paper presents a novel method to generate contour lines from grid DEM data based on the programmable GPU pipeline. The previous contouring approaches often use CPU to construct a finite element mesh from the raw DEM data, and then extract contour segments from the elements. They also need a tracing or sorting strategy to generate the final continuous contours. These approaches can be heavily CPU-costing and time-consuming. Meanwhile the generated contours would be unsmooth if the raw data is sparsely distributed. Unlike the CPU approaches, we employ the GPU's vertex shader to generate a triangular mesh with arbitrary user-defined density, in which the height of each vertex is calculated through a third-order Cardinal spline function. Then in the same frame, segments are extracted from the triangles by the geometry shader, and translated to the CPU-side with an internal order in the GPU's transform feedback stage. Finally we propose a "Grid Sorting" algorithm to achieve the continuous contour lines by travelling the segments only once. Our method makes use of multiple stages of GPU pipeline for computation, which can generate smooth contour lines, and is significantly faster than the previous CPU approaches. The algorithm can be easily implemented with OpenGL 3.3 API or higher on consumer-level PCs.

Modeling parameterized geometry in GPU-based Monte Carlo particle transport simulation for radiotherapy.

PubMed

Chi, Yujie; Tian, Zhen; Jia, Xun

2016-08-07

Monte Carlo (MC) particle transport simulation on a graphics-processing unit (GPU) platform has been extensively studied recently due to the efficiency advantage achieved via massive parallelization. Almost all of the existing GPU-based MC packages were developed for voxelized geometry. This limited application scope of these packages. The purpose of this paper is to develop a module to model parametric geometry and integrate it in GPU-based MC simulations. In our module, each continuous region was defined by its bounding surfaces that were parameterized by quadratic functions. Particle navigation functions in this geometry were developed. The module was incorporated to two previously developed GPU-based MC packages and was tested in two example problems: (1) low energy photon transport simulation in a brachytherapy case with a shielded cylinder applicator and (2) MeV coupled photon/electron transport simulation in a phantom containing several inserts of different shapes. In both cases, the calculated dose distributions agreed well with those calculated in the corresponding voxelized geometry. The averaged dose differences were 1.03% and 0.29%, respectively. We also used the developed package to perform simulations of a Varian VS 2000 brachytherapy source and generated a phase-space file. The computation time under the parameterized geometry depended on the memory location storing the geometry data. When the data was stored in GPU's shared memory, the highest computational speed was achieved. Incorporation of parameterized geometry yielded a computation time that was ~3 times of that in the corresponding voxelized geometry. We also developed a strategy to use an auxiliary index array to reduce frequency of geometry calculations and hence improve efficiency. With this strategy, the computational time ranged in 1.75-2.03 times of the voxelized geometry for coupled photon/electron transport depending on the voxel dimension of the auxiliary index array, and in 0.69-1.23 times for photon only transport.

Nitrogen soft and hard X-ray emissions using different shapes of anodes in a 4-kJ plasma focus device

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mahtab, M., E-mail: m.mahtab.83@gmail.com; Habibi, M., E-mail: mortezahabibi@aut.ac.ir

2013-12-15

The effect of different anode tip geometries on the intensity of soft and hard X-rays emitted from a 4-kJ plasma focus device is investigated using five different anode tips. The shapes of the uppermost region of these anodes (tips) have been cylindrical-flat, cylindrical-hollow, spherical-convex, cone-flat, and cone-hollow. For time-resolved measurement of the emitted X-rays, several BPX-65 pin diodes covered by different filters and a fast plastic scintillator are used. Experimental results have shown that, the highest intensity of the both soft and hard X-ray is recorded in cone-flat, spherical-convex, and cone-hollow tips, respectively. The use of cone-flat anode tip hasmore » augmented the emitted X-ray three times.« less

Ramses-GPU: Second order MUSCL-Handcock finite volume fluid solver

NASA Astrophysics Data System (ADS)

Kestener, Pierre

2017-10-01

RamsesGPU is a reimplementation of RAMSES (ascl:1011.007) which drops the adaptive mesh refinement (AMR) features to optimize 3D uniform grid algorithms for modern graphics processor units (GPU) to provide an efficient software package for astrophysics applications that do not need AMR features but do require a very large number of integration time steps. RamsesGPU provides an very efficient C++/CUDA/MPI software implementation of a second order MUSCL-Handcock finite volume fluid solver for compressible hydrodynamics as a magnetohydrodynamics solver based on the constraint transport technique. Other useful modules includes static gravity, dissipative terms (viscosity, resistivity), and forcing source term for turbulence studies, and special care was taken to enhance parallel input/output performance by using state-of-the-art libraries such as HDF5 and parallel-netcdf.

Multi-core and GPU accelerated simulation of a radial star target imaged with equivalent t-number circular and Gaussian pupils

NASA Astrophysics Data System (ADS)

Greynolds, Alan W.

2013-09-01

Results from the GelOE optical engineering software are presented for the through-focus, monochromatic coherent and polychromatic incoherent imaging of a radial "star" target for equivalent t-number circular and Gaussian pupils. The FFT-based simulations are carried out using OpenMP threading on a multi-core desktop computer, with and without the aid of a many-core NVIDIA GPU accessing its cuFFT library. It is found that a custom FFT optimized for the 12-core host has similar performance to a simply implemented 256-core GPU FFT. A more sophisticated version of the latter but tuned to reduce overhead on a 448-core GPU is 20 to 28 times faster than a basic FFT implementation running on one CPU core.

A GPU-paralleled implementation of an enhanced face recognition algorithm

NASA Astrophysics Data System (ADS)

Chen, Hao; Liu, Xiyang; Shao, Shuai; Zan, Jiguo

2013-03-01

Face recognition algorithm based on compressed sensing and sparse representation is hotly argued in these years. The scheme of this algorithm increases recognition rate as well as anti-noise capability. However, the computational cost is expensive and has become a main restricting factor for real world applications. In this paper, we introduce a GPU-accelerated hybrid variant of face recognition algorithm named parallel face recognition algorithm (pFRA). We describe here how to carry out parallel optimization design to take full advantage of many-core structure of a GPU. The pFRA is tested and compared with several other implementations under different data sample size. Finally, Our pFRA, implemented with NVIDIA GPU and Computer Unified Device Architecture (CUDA) programming model, achieves a significant speedup over the traditional CPU implementations.

Compression Dynamics of an Indirect Drive Fast Ignition Target

NASA Astrophysics Data System (ADS)

Stephens, R. B.; Hatchett, S. A.; Turner, R. E.; Tanaka, K. A.; Kodama, R.; Soures, J.

2002-11-01

We have compared the compression of an indirectly driven cone-in-shell target, a type proposed for the fast ignition concept, with models. The experimental parameters -500 μm diameter plastic shell with 60 μm thick wall were a 1/5 scale realization of a fast ignition target designed for NIF (absorbing 180 kJ for compression and ˜30 kJ for ignition, and yielding ˜30 MJ) [1]. The implosion was backlit with 6.4 keV x-rays, and observed with a framing camera which captured the implosion from ˜2.6 to 3.3 ns after the onset. The collapsing structure was very similar to model predictions except that non-thermal m-band emissions from the hohlraum penetrated the shell and vaporized gold off the reentrant cone. This could be eliminated by changing the hohlraum composition. [1] S. Hatchett, et al., 5th Wkshp on Fast Ignition of Fusion Targets (Satellite Wkshp, 28th EPS Conf. on Contr. Fusion and Plasma Phys.), Madeira, Portugal (2001).

A hybrid parallel architecture for electrostatic interactions in the simulation of dissipative particle dynamics

NASA Astrophysics Data System (ADS)

Yang, Sheng-Chun; Lu, Zhong-Yuan; Qian, Hu-Jun; Wang, Yong-Lei; Han, Jie-Ping

2017-11-01

In this work, we upgraded the electrostatic interaction method of CU-ENUF (Yang, et al., 2016) which first applied CUNFFT (nonequispaced Fourier transforms based on CUDA) to the reciprocal-space electrostatic computation and made the computation of electrostatic interaction done thoroughly in GPU. The upgraded edition of CU-ENUF runs concurrently in a hybrid parallel way that enables the computation parallelizing on multiple computer nodes firstly, then further on the installed GPU in each computer. By this parallel strategy, the size of simulation system will be never restricted to the throughput of a single CPU or GPU. The most critical technical problem is how to parallelize a CUNFFT in the parallel strategy, which is conquered effectively by deep-seated research of basic principles and some algorithm skills. Furthermore, the upgraded method is capable of computing electrostatic interactions for both the atomistic molecular dynamics (MD) and the dissipative particle dynamics (DPD). Finally, the benchmarks conducted for validation and performance indicate that the upgraded method is able to not only present a good precision when setting suitable parameters, but also give an efficient way to compute electrostatic interactions for huge simulation systems. Program Files doi:http://dx.doi.org/10.17632/zncf24fhpv.1 Licensing provisions: GNU General Public License 3 (GPL) Programming language: C, C++, and CUDA C Supplementary material: The program is designed for effective electrostatic interactions of large-scale simulation systems, which runs on particular computers equipped with NVIDIA GPUs. It has been tested on (a) single computer node with Intel(R) Core(TM) i7-3770@ 3.40 GHz (CPU) and GTX 980 Ti (GPU), and (b) MPI parallel computer nodes with the same configurations. Nature of problem: For molecular dynamics simulation, the electrostatic interaction is the most time-consuming computation because of its long-range feature and slow convergence in simulation space, which approximately take up most of the total simulation time. Although the parallel method CU-ENUF (Yang et al., 2016) based on GPU has achieved a qualitative leap compared with previous methods in electrostatic interactions computation, the computation capability is limited to the throughput capacity of a single GPU for super-scale simulation system. Therefore, we should look for an effective method to handle the calculation of electrostatic interactions efficiently for a simulation system with super-scale size. Solution method: We constructed a hybrid parallel architecture, in which CPU and GPU are combined to accelerate the electrostatic computation effectively. Firstly, the simulation system is divided into many subtasks via domain-decomposition method. Then MPI (Message Passing Interface) is used to implement the CPU-parallel computation with each computer node corresponding to a particular subtask, and furthermore each subtask in one computer node will be executed in GPU in parallel efficiently. In this hybrid parallel method, the most critical technical problem is how to parallelize a CUNFFT (nonequispaced fast Fourier transform based on CUDA) in the parallel strategy, which is conquered effectively by deep-seated research of basic principles and some algorithm skills. Restrictions: The HP-ENUF is mainly oriented to super-scale system simulations, in which the performance superiority is shown adequately. However, for a small simulation system containing less than 106 particles, the mode of multiple computer nodes has no apparent efficiency advantage or even lower efficiency due to the serious network delay among computer nodes, than the mode of single computer node. References: (1) S.-C. Yang, H.-J. Qian, Z.-Y. Lu, Appl. Comput. Harmon. Anal. 2016, http://dx.doi.org/10.1016/j.acha.2016.04.009. (2) S.-C. Yang, Y.-L. Wang, G.-S. Jiao, H.-J. Qian, Z.-Y. Lu, J. Comput. Chem. 37 (2016) 378. (3) S.-C. Yang, Y.-L. Zhu, H.-J. Qian, Z.-Y. Lu, Appl. Chem. Res. Chin. Univ., 2017, http://dx.doi.org/10.1007/s40242-016-6354-5. (4) Y.-L. Zhu, H. Liu, Z.-W. Li, H.-J. Qian, G. Milano, Z.-Y. Lu, J. Comput. Chem. 34 (2013) 2197.

PBOOST: a GPU-based tool for parallel permutation tests in genome-wide association studies.

PubMed

Yang, Guangyuan; Jiang, Wei; Yang, Qiang; Yu, Weichuan

2015-05-01

The importance of testing associations allowing for interactions has been demonstrated by Marchini et al. (2005). A fast method detecting associations allowing for interactions has been proposed by Wan et al. (2010a). The method is based on likelihood ratio test with the assumption that the statistic follows the χ(2) distribution. Many single nucleotide polymorphism (SNP) pairs with significant associations allowing for interactions have been detected using their method. However, the assumption of χ(2) test requires the expected values in each cell of the contingency table to be at least five. This assumption is violated in some identified SNP pairs. In this case, likelihood ratio test may not be applicable any more. Permutation test is an ideal approach to checking the P-values calculated in likelihood ratio test because of its non-parametric nature. The P-values of SNP pairs having significant associations with disease are always extremely small. Thus, we need a huge number of permutations to achieve correspondingly high resolution for the P-values. In order to investigate whether the P-values from likelihood ratio tests are reliable, a fast permutation tool to accomplish large number of permutations is desirable. We developed a permutation tool named PBOOST. It is based on GPU with highly reliable P-value estimation. By using simulation data, we found that the P-values from likelihood ratio tests will have relative error of >100% when 50% cells in the contingency table have expected count less than five or when there is zero expected count in any of the contingency table cells. In terms of speed, PBOOST completed 10(7) permutations for a single SNP pair from the Wellcome Trust Case Control Consortium (WTCCC) genome data (Wellcome Trust Case Control Consortium, 2007) within 1 min on a single Nvidia Tesla M2090 device, while it took 60 min in a single CPU Intel Xeon E5-2650 to finish the same task. More importantly, when simultaneously testing 256 SNP pairs for 10(7) permutations, our tool took only 5 min, while the CPU program took 10 h. By permuting on a GPU cluster consisting of 40 nodes, we completed 10(12) permutations for all 280 SNP pairs reported with P-values smaller than 1.6 × 10⁻¹² in the WTCCC datasets in 1 week. The source code and sample data are available at http://bioinformatics.ust.hk/PBOOST.zip. gyang@ust.hk; eeyu@ust.hk Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Advances in free-energy-based simulations of protein folding and ligand binding.

PubMed

Perez, Alberto; Morrone, Joseph A; Simmerling, Carlos; Dill, Ken A

2016-02-01

Free-energy-based simulations are increasingly providing the narratives about the structures, dynamics and biological mechanisms that constitute the fabric of protein science. Here, we review two recent successes. It is becoming practical: first, to fold small proteins with free-energy methods without knowing substructures and second, to compute ligand-protein binding affinities, not just their binding poses. Over the past 40 years, the timescales that can be simulated by atomistic MD are doubling every 1.3 years--which is faster than Moore's law. Thus, these advances are not simply due to the availability of faster computers. Force fields, solvation models and simulation methodology have kept pace with computing advancements, and are now quite good. At the tip of the spear recently are GPU-based computing, improved fast-solvation methods, continued advances in force fields, and conformational sampling methods that harness external information. Copyright © 2015 Elsevier Ltd. All rights reserved.

Interactive brain shift compensation using GPU based programming

NASA Astrophysics Data System (ADS)

van der Steen, Sander; Noordmans, Herke Jan; Verdaasdonk, Rudolf

2009-02-01

Processing large images files or real-time video streams requires intense computational power. Driven by the gaming industry, the processing power of graphic process units (GPUs) has increased significantly. With the pixel shader model 4.0 the GPU can be used for image processing 10x faster than the CPU. Dedicated software was developed to deform 3D MR and CT image sets for real-time brain shift correction during navigated neurosurgery using landmarks or cortical surface traces defined by the navigation pointer. Feedback was given using orthogonal slices and an interactively raytraced 3D brain image. GPU based programming enables real-time processing of high definition image datasets and various applications can be developed in medicine, optics and image sciences.

Dosimetric comparison of helical tomotherapy treatment plans for total marrow irradiation created using GPU and CPU dose calculation engines.

PubMed

Nalichowski, Adrian; Burmeister, Jay

2013-07-01

To compare optimization characteristics, plan quality, and treatment delivery efficiency between total marrow irradiation (TMI) plans using the new TomoTherapy graphic processing unit (GPU) based dose engine and CPU/cluster based dose engine. Five TMI plans created on an anthropomorphic phantom were optimized and calculated with both dose engines. The planning treatment volume (PTV) included all the bones from head to mid femur except for upper extremities. Evaluated organs at risk (OAR) consisted of lung, liver, heart, kidneys, and brain. The following treatment parameters were used to generate the TMI plans: field widths of 2.5 and 5 cm, modulation factors of 2 and 2.5, and pitch of either 0.287 or 0.43. The optimization parameters were chosen based on the PTV and OAR priorities and the plans were optimized with a fixed number of iterations. The PTV constraint was selected to ensure that at least 95% of the PTV received the prescription dose. The plans were evaluated based on D80 and D50 (dose to 80% and 50% of the OAR volume, respectively) and hotspot volumes within the PTVs. Gamma indices (Γ) were also used to compare planar dose distributions between the two modalities. The optimization and dose calculation times were compared between the two systems. The treatment delivery times were also evaluated. The results showed very good dosimetric agreement between the GPU and CPU calculated plans for any of the evaluated planning parameters indicating that both systems converge on nearly identical plans. All D80 and D50 parameters varied by less than 3% of the prescription dose with an average difference of 0.8%. A gamma analysis Γ(3%, 3 mm) < 1 of the GPU plan resulted in over 90% of calculated voxels satisfying Γ < 1 criterion as compared to baseline CPU plan. The average number of voxels meeting the Γ < 1 criterion for all the plans was 97%. In terms of dose optimization/calculation efficiency, there was a 20-fold reduction in planning time with the new GPU system. The average optimization/dose calculation time utilizing the traditional CPU/cluster based system was 579 vs 26.8 min for the GPU based system. There was no difference in the calculated treatment delivery time per fraction. Beam-on time varied based on field width and pitch and ranged between 15 and 28 min. The TomoTherapy GPU based dose engine is capable of calculating TMI treatment plans with plan quality nearly identical to plans calculated using the traditional CPU/cluster based system, while significantly reducing the time required for optimization and dose calculation.

Quantum.Ligand.Dock: protein-ligand docking with quantum entanglement refinement on a GPU system.

PubMed

Kantardjiev, Alexander A

2012-07-01

Quantum.Ligand.Dock (protein-ligand docking with graphic processing unit (GPU) quantum entanglement refinement on a GPU system) is an original modern method for in silico prediction of protein-ligand interactions via high-performance docking code. The main flavour of our approach is a combination of fast search with a special account for overlooked physical interactions. On the one hand, we take care of self-consistency and proton equilibria mutual effects of docking partners. On the other hand, Quantum.Ligand.Dock is the the only docking server offering such a subtle supplement to protein docking algorithms as quantum entanglement contributions. The motivation for development and proposition of the method to the community hinges upon two arguments-the fundamental importance of quantum entanglement contribution in molecular interaction and the realistic possibility to implement it by the availability of supercomputing power. The implementation of sophisticated quantum methods is made possible by parallelization at several bottlenecks on a GPU supercomputer. The high-performance implementation will be of use for large-scale virtual screening projects, structural bioinformatics, systems biology and fundamental research in understanding protein-ligand recognition. The design of the interface is focused on feasibility and ease of use. Protein and ligand molecule structures are supposed to be submitted as atomic coordinate files in PDB format. A customization section is offered for addition of user-specified charges, extra ionogenic groups with intrinsic pK(a) values or fixed ions. Final predicted complexes are ranked according to obtained scores and provided in PDB format as well as interactive visualization in a molecular viewer. Quantum.Ligand.Dock server can be accessed at http://87.116.85.141/LigandDock.html.

BROCCOLI: Software for fast fMRI analysis on many-core CPUs and GPUs

PubMed Central

Eklund, Anders; Dufort, Paul; Villani, Mattias; LaConte, Stephen

2014-01-01

Analysis of functional magnetic resonance imaging (fMRI) data is becoming ever more computationally demanding as temporal and spatial resolutions improve, and large, publicly available data sets proliferate. Moreover, methodological improvements in the neuroimaging pipeline, such as non-linear spatial normalization, non-parametric permutation tests and Bayesian Markov Chain Monte Carlo approaches, can dramatically increase the computational burden. Despite these challenges, there do not yet exist any fMRI software packages which leverage inexpensive and powerful graphics processing units (GPUs) to perform these analyses. Here, we therefore present BROCCOLI, a free software package written in OpenCL (Open Computing Language) that can be used for parallel analysis of fMRI data on a large variety of hardware configurations. BROCCOLI has, for example, been tested with an Intel CPU, an Nvidia GPU, and an AMD GPU. These tests show that parallel processing of fMRI data can lead to significantly faster analysis pipelines. This speedup can be achieved on relatively standard hardware, but further, dramatic speed improvements require only a modest investment in GPU hardware. BROCCOLI (running on a GPU) can perform non-linear spatial normalization to a 1 mm3 brain template in 4–6 s, and run a second level permutation test with 10,000 permutations in about a minute. These non-parametric tests are generally more robust than their parametric counterparts, and can also enable more sophisticated analyses by estimating complicated null distributions. Additionally, BROCCOLI includes support for Bayesian first-level fMRI analysis using a Gibbs sampler. The new software is freely available under GNU GPL3 and can be downloaded from github (https://github.com/wanderine/BROCCOLI/). PMID:24672471

Moving magnets in a micromagnetic finite-difference framework

NASA Astrophysics Data System (ADS)

Rissanen, Ilari; Laurson, Lasse

2018-05-01

We present a method and an implementation for smooth linear motion in a finite-difference-based micromagnetic simulation code, to be used in simulating magnetic friction and other phenomena involving moving microscale magnets. Our aim is to accurately simulate the magnetization dynamics and relative motion of magnets while retaining high computational speed. To this end, we combine techniques for fast scalar potential calculation and cubic b-spline interpolation, parallelizing them on a graphics processing unit (GPU). The implementation also includes the possibility of explicitly simulating eddy currents in the case of conducting magnets. We test our implementation by providing numerical examples of stick-slip motion of thin films pulled by a spring and the effect of eddy currents on the switching time of magnetic nanocubes.

Revisiting Molecular Dynamics on a CPU/GPU system: Water Kernel and SHAKE Parallelization.

PubMed

Ruymgaart, A Peter; Elber, Ron

2012-11-13

We report Graphics Processing Unit (GPU) and Open-MP parallel implementations of water-specific force calculations and of bond constraints for use in Molecular Dynamics simulations. We focus on a typical laboratory computing-environment in which a CPU with a few cores is attached to a GPU. We discuss in detail the design of the code and we illustrate performance comparable to highly optimized codes such as GROMACS. Beside speed our code shows excellent energy conservation. Utilization of water-specific lists allows the efficient calculations of non-bonded interactions that include water molecules and results in a speed-up factor of more than 40 on the GPU compared to code optimized on a single CPU core for systems larger than 20,000 atoms. This is up four-fold from a factor of 10 reported in our initial GPU implementation that did not include a water-specific code. Another optimization is the implementation of constrained dynamics entirely on the GPU. The routine, which enforces constraints of all bonds, runs in parallel on multiple Open-MP cores or entirely on the GPU. It is based on Conjugate Gradient solution of the Lagrange multipliers (CG SHAKE). The GPU implementation is partially in double precision and requires no communication with the CPU during the execution of the SHAKE algorithm. The (parallel) implementation of SHAKE allows an increase of the time step to 2.0fs while maintaining excellent energy conservation. Interestingly, CG SHAKE is faster than the usual bond relaxation algorithm even on a single core if high accuracy is expected. The significant speedup of the optimized components transfers the computational bottleneck of the MD calculation to the reciprocal part of Particle Mesh Ewald (PME).

GPU-accelerated track reconstruction in the ALICE High Level Trigger

NASA Astrophysics Data System (ADS)

Rohr, David; Gorbunov, Sergey; Lindenstruth, Volker; ALICE Collaboration

2017-10-01

ALICE (A Large Heavy Ion Experiment) is one of the four major experiments at the Large Hadron Collider (LHC) at CERN. The High Level Trigger (HLT) is an online compute farm which reconstructs events measured by the ALICE detector in real-time. The most compute-intensive part is the reconstruction of particle trajectories called tracking and the most important detector for tracking is the Time Projection Chamber (TPC). The HLT uses a GPU-accelerated algorithm for TPC tracking that is based on the Cellular Automaton principle and on the Kalman filter. The GPU tracking has been running in 24/7 operation since 2012 in LHC Run 1 and 2. In order to better leverage the potential of the GPUs, and speed up the overall HLT reconstruction, we plan to bring more reconstruction steps (e.g. the tracking for other detectors) onto the GPUs. There are several tasks running so far on the CPU that could benefit from cooperation with the tracking, which is hardly feasible at the moment due to the delay of the PCI Express transfers. Moving more steps onto the GPU, and processing them on the GPU at once, will reduce PCI Express transfers and free up CPU resources. On top of that, modern GPUs and GPU programming APIs provide new features which are not yet exploited by the TPC tracking. We present our new developments for GPU reconstruction, both with a focus on the online reconstruction on GPU for the online offline computing upgrade in ALICE during LHC Run 3, and also taking into account how the current HLT in Run 2 can profit from these improvements.

GPU color space conversion

NASA Astrophysics Data System (ADS)

Chase, Patrick; Vondran, Gary

2011-01-01

Tetrahedral interpolation is commonly used to implement continuous color space conversions from sparse 3D and 4D lookup tables. We investigate the implementation and optimization of tetrahedral interpolation algorithms for GPUs, and compare to the best known CPU implementations as well as to a well known GPU-based trilinear implementation. We show that a 500 NVIDIA GTX-580 GPU is 3x faster than a 1000 Intel Core i7 980X CPU for 3D interpolation, and 9x faster for 4D interpolation. Performance-relevant GPU attributes are explored including thread scheduling, local memory characteristics, global memory hierarchy, and cache behaviors. We consider existing tetrahedral interpolation algorithms and tune based on the structure and branching capabilities of current GPUs. Global memory performance is improved by reordering and expanding the lookup table to ensure optimal access behaviors. Per multiprocessor local memory is exploited to implement optimally coalesced global memory accesses, and local memory addressing is optimized to minimize bank conflicts. We explore the impacts of lookup table density upon computation and memory access costs. Also presented are CPU-based 3D and 4D interpolators, using SSE vector operations that are faster than any previously published solution.

Large Scale Document Inversion using a Multi-threaded Computing System

PubMed Central

Jung, Sungbo; Chang, Dar-Jen; Park, Juw Won

2018-01-01

Current microprocessor architecture is moving towards multi-core/multi-threaded systems. This trend has led to a surge of interest in using multi-threaded computing devices, such as the Graphics Processing Unit (GPU), for general purpose computing. We can utilize the GPU in computation as a massive parallel coprocessor because the GPU consists of multiple cores. The GPU is also an affordable, attractive, and user-programmable commodity. Nowadays a lot of information has been flooded into the digital domain around the world. Huge volume of data, such as digital libraries, social networking services, e-commerce product data, and reviews, etc., is produced or collected every moment with dramatic growth in size. Although the inverted index is a useful data structure that can be used for full text searches or document retrieval, a large number of documents will require a tremendous amount of time to create the index. The performance of document inversion can be improved by multi-thread or multi-core GPU. Our approach is to implement a linear-time, hash-based, single program multiple data (SPMD), document inversion algorithm on the NVIDIA GPU/CUDA programming platform utilizing the huge computational power of the GPU, to develop high performance solutions for document indexing. Our proposed parallel document inversion system shows 2-3 times faster performance than a sequential system on two different test datasets from PubMed abstract and e-commerce product reviews. CCS Concepts •Information systems➝Information retrieval • Computing methodologies➝Massively parallel and high-performance simulations. PMID:29861701

Novel hybrid GPU-CPU implementation of parallelized Monte Carlo parametric expectation maximization estimation method for population pharmacokinetic data analysis.

PubMed

Ng, C M

2013-10-01

The development of a population PK/PD model, an essential component for model-based drug development, is both time- and labor-intensive. A graphical-processing unit (GPU) computing technology has been proposed and used to accelerate many scientific computations. The objective of this study was to develop a hybrid GPU-CPU implementation of parallelized Monte Carlo parametric expectation maximization (MCPEM) estimation algorithm for population PK data analysis. A hybrid GPU-CPU implementation of the MCPEM algorithm (MCPEMGPU) and identical algorithm that is designed for the single CPU (MCPEMCPU) were developed using MATLAB in a single computer equipped with dual Xeon 6-Core E5690 CPU and a NVIDIA Tesla C2070 GPU parallel computing card that contained 448 stream processors. Two different PK models with rich/sparse sampling design schemes were used to simulate population data in assessing the performance of MCPEMCPU and MCPEMGPU. Results were analyzed by comparing the parameter estimation and model computation times. Speedup factor was used to assess the relative benefit of parallelized MCPEMGPU over MCPEMCPU in shortening model computation time. The MCPEMGPU consistently achieved shorter computation time than the MCPEMCPU and can offer more than 48-fold speedup using a single GPU card. The novel hybrid GPU-CPU implementation of parallelized MCPEM algorithm developed in this study holds a great promise in serving as the core for the next-generation of modeling software for population PK/PD analysis.

Large Scale Document Inversion using a Multi-threaded Computing System.

PubMed

Jung, Sungbo; Chang, Dar-Jen; Park, Juw Won

2017-06-01

Current microprocessor architecture is moving towards multi-core/multi-threaded systems. This trend has led to a surge of interest in using multi-threaded computing devices, such as the Graphics Processing Unit (GPU), for general purpose computing. We can utilize the GPU in computation as a massive parallel coprocessor because the GPU consists of multiple cores. The GPU is also an affordable, attractive, and user-programmable commodity. Nowadays a lot of information has been flooded into the digital domain around the world. Huge volume of data, such as digital libraries, social networking services, e-commerce product data, and reviews, etc., is produced or collected every moment with dramatic growth in size. Although the inverted index is a useful data structure that can be used for full text searches or document retrieval, a large number of documents will require a tremendous amount of time to create the index. The performance of document inversion can be improved by multi-thread or multi-core GPU. Our approach is to implement a linear-time, hash-based, single program multiple data (SPMD), document inversion algorithm on the NVIDIA GPU/CUDA programming platform utilizing the huge computational power of the GPU, to develop high performance solutions for document indexing. Our proposed parallel document inversion system shows 2-3 times faster performance than a sequential system on two different test datasets from PubMed abstract and e-commerce product reviews. •Information systems➝Information retrieval • Computing methodologies➝Massively parallel and high-performance simulations.

Mass production alternatives for fast-growing spruce hybrids

Treesearch

Hans Nienstaedt

1977-01-01

A reciptrocal crossing program of Picea glauca and P. omorika, and P. mariana and P. omorika with adequate intraspecific control crosses was carried out. Yields of full seed/cone ranged from 0 to 2.6 for the P. glauca-P. omorika combination. These low seed sets and a 9-day difference in female receptivity rule out producing this hybrid via seed. The full seed/cone...

Fast and flexible gpu accelerated binding free energy calculations within the amber molecular dynamics package.

PubMed

Mermelstein, Daniel J; Lin, Charles; Nelson, Gard; Kretsch, Rachael; McCammon, J Andrew; Walker, Ross C

2018-07-15

Alchemical free energy (AFE) calculations based on molecular dynamics (MD) simulations are key tools in both improving our understanding of a wide variety of biological processes and accelerating the design and optimization of therapeutics for numerous diseases. Computing power and theory have, however, long been insufficient to enable AFE calculations to be routinely applied in early stage drug discovery. One of the major difficulties in performing AFE calculations is the length of time required for calculations to converge to an ensemble average. CPU implementations of MD-based free energy algorithms can effectively only reach tens of nanoseconds per day for systems on the order of 50,000 atoms, even running on massively parallel supercomputers. Therefore, converged free energy calculations on large numbers of potential lead compounds are often untenable, preventing researchers from gaining crucial insight into molecular recognition, potential druggability and other crucial areas of interest. Graphics Processing Units (GPUs) can help address this. We present here a seamless GPU implementation, within the PMEMD module of the AMBER molecular dynamics package, of thermodynamic integration (TI) capable of reaching speeds of >140 ns/day for a 44,907-atom system, with accuracy equivalent to the existing CPU implementation in AMBER. The implementation described here is currently part of the AMBER 18 beta code and will be an integral part of the upcoming version 18 release of AMBER. © 2018 Wiley Periodicals, Inc. © 2018 Wiley Periodicals, Inc.

Toward GPGPU accelerated human electromechanical cardiac simulations

PubMed Central

Vigueras, Guillermo; Roy, Ishani; Cookson, Andrew; Lee, Jack; Smith, Nicolas; Nordsletten, David

2014-01-01

In this paper, we look at the acceleration of weakly coupled electromechanics using the graphics processing unit (GPU). Specifically, we port to the GPU a number of components of Heart—a CPU-based finite element code developed for simulating multi-physics problems. On the basis of a criterion of computational cost, we implemented on the GPU the ODE and PDE solution steps for the electrophysiology problem and the Jacobian and residual evaluation for the mechanics problem. Performance of the GPU implementation is then compared with single core CPU (SC) execution as well as multi-core CPU (MC) computations with equivalent theoretical performance. Results show that for a human scale left ventricle mesh, GPU acceleration of the electrophysiology problem provided speedups of 164 × compared with SC and 5.5 times compared with MC for the solution of the ODE model. Speedup of up to 72 × compared with SC and 2.6 × compared with MC was also observed for the PDE solve. Using the same human geometry, the GPU implementation of mechanics residual/Jacobian computation provided speedups of up to 44 × compared with SC and 2.0 × compared with MC. © 2013 The Authors. International Journal for Numerical Methods in Biomedical Engineering published by John Wiley & Sons, Ltd. PMID:24115492

Visual Media Reasoning - Terrain-based Geolocation

DTIC Science & Technology

2015-06-01

the drawings, specifications, or other data does not license the holder or any other person or corporation ; or convey any rights or permission to...3.4 Alternative Metric Investigation This section describes a graphics processor unit (GPU) based implementation in the NVIDIA CUDA programming...utilizing 2 concurrent CPU cores, each controlling a single Nvidia C2075 Tesla Fermi CUDA card. Figure 22 shows a comparison of the CPU and the GPU powered

Rapid simulation of X-ray transmission imaging for baggage inspection via GPU-based ray-tracing

NASA Astrophysics Data System (ADS)

Gong, Qian; Stoian, Razvan-Ionut; Coccarelli, David S.; Greenberg, Joel A.; Vera, Esteban; Gehm, Michael E.

2018-01-01

We present a pipeline that rapidly simulates X-ray transmission imaging for arbitrary system architectures using GPU-based ray-tracing techniques. The purpose of the pipeline is to enable statistical analysis of threat detection in the context of airline baggage inspection. As a faster alternative to Monte Carlo methods, we adopt a deterministic approach for simulating photoelectric absorption-based imaging. The highly-optimized NVIDIA OptiX API is used to implement ray-tracing, greatly speeding code execution. In addition, we implement the first hierarchical representation structure to determine the interaction path length of rays traversing heterogeneous media described by layered polygons. The accuracy of the pipeline has been validated by comparing simulated data with experimental data collected using a heterogenous phantom and a laboratory X-ray imaging system. On a single computer, our approach allows us to generate over 400 2D transmission projections (125 × 125 pixels per frame) per hour for a bag packed with hundreds of everyday objects. By implementing our approach on cloud-based GPU computing platforms, we find that the same 2D projections of approximately 3.9 million bags can be obtained in a single day using 400 GPU instances, at a cost of only 0.001 per bag.

GPU-based multi-volume ray casting within VTK for medical applications.

PubMed

Bozorgi, Mohammadmehdi; Lindseth, Frank

2015-03-01

Multi-volume visualization is important for displaying relevant information in multimodal or multitemporal medical imaging studies. The main objective with the current study was to develop an efficient GPU-based multi-volume ray caster (MVRC) and validate the proposed visualization system in the context of image-guided surgical navigation. Ray casting can produce high-quality 2D images from 3D volume data but the method is computationally demanding, especially when multiple volumes are involved, so a parallel GPU version has been implemented. In the proposed MVRC, imaginary rays are sent through the volumes (one ray for each pixel in the view), and at equal and short intervals along the rays, samples are collected from each volume. Samples from all the volumes are composited using front to back α-blending. Since all the rays can be processed simultaneously, the MVRC was implemented in parallel on the GPU to achieve acceptable interactive frame rates. The method is fully integrated within the visualization toolkit (VTK) pipeline with the ability to apply different operations (e.g., transformations, clipping, and cropping) on each volume separately. The implemented method is cross-platform (Windows, Linux and Mac OSX) and runs on different graphics card (NVidia and AMD). The speed of the MVRC was tested with one to five volumes of varying sizes: 128(3), 256(3), and 512(3). A Tesla C2070 GPU was used, and the output image size was 600 × 600 pixels. The original VTK single-volume ray caster and the MVRC were compared when rendering only one volume. The multi-volume rendering system achieved an interactive frame rate (> 15 fps) when rendering five small volumes (128 (3) voxels), four medium-sized volumes (256(3) voxels), and two large volumes (512(3) voxels). When rendering single volumes, the frame rate of the MVRC was comparable to the original VTK ray caster for small and medium-sized datasets but was approximately 3 frames per second slower for large datasets. The MVRC was successfully integrated in an existing surgical navigation system and was shown to be clinically useful during an ultrasound-guided neurosurgical tumor resection. A GPU-based MVRC for VTK is a useful tool in medical visualization. The proposed multi-volume GPU-based ray caster for VTK provided high-quality images at reasonable frame rates. The MVRC was effective when used in a neurosurgical navigation application.

Digital image processing using parallel computing based on CUDA technology

NASA Astrophysics Data System (ADS)

Skirnevskiy, I. P.; Pustovit, A. V.; Abdrashitova, M. O.

2017-01-01

This article describes expediency of using a graphics processing unit (GPU) in big data processing in the context of digital images processing. It provides a short description of a parallel computing technology and its usage in different areas, definition of the image noise and a brief overview of some noise removal algorithms. It also describes some basic requirements that should be met by certain noise removal algorithm in the projection to computer tomography. It provides comparison of the performance with and without using GPU as well as with different percentage of using CPU and GPU.

permGPU: Using graphics processing units in RNA microarray association studies.

PubMed

Shterev, Ivo D; Jung, Sin-Ho; George, Stephen L; Owzar, Kouros

2010-06-16

Many analyses of microarray association studies involve permutation, bootstrap resampling and cross-validation, that are ideally formulated as embarrassingly parallel computing problems. Given that these analyses are computationally intensive, scalable approaches that can take advantage of multi-core processor systems need to be developed. We have developed a CUDA based implementation, permGPU, that employs graphics processing units in microarray association studies. We illustrate the performance and applicability of permGPU within the context of permutation resampling for a number of test statistics. An extensive simulation study demonstrates a dramatic increase in performance when using permGPU on an NVIDIA GTX 280 card compared to an optimized C/C++ solution running on a conventional Linux server. permGPU is available as an open-source stand-alone application and as an extension package for the R statistical environment. It provides a dramatic increase in performance for permutation resampling analysis in the context of microarray association studies. The current version offers six test statistics for carrying out permutation resampling analyses for binary, quantitative and censored time-to-event traits.

Multi-GPU parallel algorithm design and analysis for improved inversion of probability tomography with gravity gradiometry data

NASA Astrophysics Data System (ADS)

Hou, Zhenlong; Huang, Danian

2017-09-01

In this paper, we make a study on the inversion of probability tomography (IPT) with gravity gradiometry data at first. The space resolution of the results is improved by multi-tensor joint inversion, depth weighting matrix and the other methods. Aiming at solving the problems brought by the big data in the exploration, we present the parallel algorithm and the performance analysis combining Compute Unified Device Architecture (CUDA) with Open Multi-Processing (OpenMP) based on Graphics Processing Unit (GPU) accelerating. In the test of the synthetic model and real data from Vinton Dome, we get the improved results. It is also proved that the improved inversion algorithm is effective and feasible. The performance of parallel algorithm we designed is better than the other ones with CUDA. The maximum speedup could be more than 200. In the performance analysis, multi-GPU speedup and multi-GPU efficiency are applied to analyze the scalability of the multi-GPU programs. The designed parallel algorithm is demonstrated to be able to process larger scale of data and the new analysis method is practical.

Accelerating Monte Carlo simulations of photon transport in a voxelized geometry using a massively parallel graphics processing unit.

PubMed

Badal, Andreu; Badano, Aldo

2009-11-01

It is a known fact that Monte Carlo simulations of radiation transport are computationally intensive and may require long computing times. The authors introduce a new paradigm for the acceleration of Monte Carlo simulations: The use of a graphics processing unit (GPU) as the main computing device instead of a central processing unit (CPU). A GPU-based Monte Carlo code that simulates photon transport in a voxelized geometry with the accurate physics models from PENELOPE has been developed using the CUDATM programming model (NVIDIA Corporation, Santa Clara, CA). An outline of the new code and a sample x-ray imaging simulation with an anthropomorphic phantom are presented. A remarkable 27-fold speed up factor was obtained using a GPU compared to a single core CPU. The reported results show that GPUs are currently a good alternative to CPUs for the simulation of radiation transport. Since the performance of GPUs is currently increasing at a faster pace than that of CPUs, the advantages of GPU-based software are likely to be more pronounced in the future.

GPURFSCREEN: a GPU based virtual screening tool using random forest classifier.

PubMed

Jayaraj, P B; Ajay, Mathias K; Nufail, M; Gopakumar, G; Jaleel, U C A

2016-01-01

In-silico methods are an integral part of modern drug discovery paradigm. Virtual screening, an in-silico method, is used to refine data models and reduce the chemical space on which wet lab experiments need to be performed. Virtual screening of a ligand data model requires large scale computations, making it a highly time consuming task. This process can be speeded up by implementing parallelized algorithms on a Graphical Processing Unit (GPU). Random Forest is a robust classification algorithm that can be employed in the virtual screening. A ligand based virtual screening tool (GPURFSCREEN) that uses random forests on GPU systems has been proposed and evaluated in this paper. This tool produces optimized results at a lower execution time for large bioassay data sets. The quality of results produced by our tool on GPU is same as that on a regular serial environment. Considering the magnitude of data to be screened, the parallelized virtual screening has a significantly lower running time at high throughput. The proposed parallel tool outperforms its serial counterpart by successfully screening billions of molecules in training and prediction phases.

Performance and scalability of Fourier domain optical coherence tomography acceleration using graphics processing units.

PubMed

Li, Jian; Bloch, Pavel; Xu, Jing; Sarunic, Marinko V; Shannon, Lesley

2011-05-01

Fourier domain optical coherence tomography (FD-OCT) provides faster line rates, better resolution, and higher sensitivity for noninvasive, in vivo biomedical imaging compared to traditional time domain OCT (TD-OCT). However, because the signal processing for FD-OCT is computationally intensive, real-time FD-OCT applications demand powerful computing platforms to deliver acceptable performance. Graphics processing units (GPUs) have been used as coprocessors to accelerate FD-OCT by leveraging their relatively simple programming model to exploit thread-level parallelism. Unfortunately, GPUs do not "share" memory with their host processors, requiring additional data transfers between the GPU and CPU. In this paper, we implement a complete FD-OCT accelerator on a consumer grade GPU/CPU platform. Our data acquisition system uses spectrometer-based detection and a dual-arm interferometer topology with numerical dispersion compensation for retinal imaging. We demonstrate that the maximum line rate is dictated by the memory transfer time and not the processing time due to the GPU platform's memory model. Finally, we discuss how the performance trends of GPU-based accelerators compare to the expected future requirements of FD-OCT data rates.

A new approach to integrate GPU-based Monte Carlo simulation into inverse treatment plan optimization for proton therapy.

PubMed

Li, Yongbao; Tian, Zhen; Song, Ting; Wu, Zhaoxia; Liu, Yaqiang; Jiang, Steve; Jia, Xun

2017-01-07

Monte Carlo (MC)-based spot dose calculation is highly desired for inverse treatment planning in proton therapy because of its accuracy. Recent studies on biological optimization have also indicated the use of MC methods to compute relevant quantities of interest, e.g. linear energy transfer. Although GPU-based MC engines have been developed to address inverse optimization problems, their efficiency still needs to be improved. Also, the use of a large number of GPUs in MC calculation is not favorable for clinical applications. The previously proposed adaptive particle sampling (APS) method can improve the efficiency of MC-based inverse optimization by using the computationally expensive MC simulation more effectively. This method is more efficient than the conventional approach that performs spot dose calculation and optimization in two sequential steps. In this paper, we propose a computational library to perform MC-based spot dose calculation on GPU with the APS scheme. The implemented APS method performs a non-uniform sampling of the particles from pencil beam spots during the optimization process, favoring those from the high intensity spots. The library also conducts two computationally intensive matrix-vector operations frequently used when solving an optimization problem. This library design allows a streamlined integration of the MC-based spot dose calculation into an existing proton therapy inverse planning process. We tested the developed library in a typical inverse optimization system with four patient cases. The library achieved the targeted functions by supporting inverse planning in various proton therapy schemes, e.g. single field uniform dose, 3D intensity modulated proton therapy, and distal edge tracking. The efficiency was 41.6  ±  15.3% higher than the use of a GPU-based MC package in a conventional calculation scheme. The total computation time ranged between 2 and 50 min on a single GPU card depending on the problem size.

A new approach to integrate GPU-based Monte Carlo simulation into inverse treatment plan optimization for proton therapy

NASA Astrophysics Data System (ADS)

Li, Yongbao; Tian, Zhen; Song, Ting; Wu, Zhaoxia; Liu, Yaqiang; Jiang, Steve; Jia, Xun

2017-01-01

Monte Carlo (MC)-based spot dose calculation is highly desired for inverse treatment planning in proton therapy because of its accuracy. Recent studies on biological optimization have also indicated the use of MC methods to compute relevant quantities of interest, e.g. linear energy transfer. Although GPU-based MC engines have been developed to address inverse optimization problems, their efficiency still needs to be improved. Also, the use of a large number of GPUs in MC calculation is not favorable for clinical applications. The previously proposed adaptive particle sampling (APS) method can improve the efficiency of MC-based inverse optimization by using the computationally expensive MC simulation more effectively. This method is more efficient than the conventional approach that performs spot dose calculation and optimization in two sequential steps. In this paper, we propose a computational library to perform MC-based spot dose calculation on GPU with the APS scheme. The implemented APS method performs a non-uniform sampling of the particles from pencil beam spots during the optimization process, favoring those from the high intensity spots. The library also conducts two computationally intensive matrix-vector operations frequently used when solving an optimization problem. This library design allows a streamlined integration of the MC-based spot dose calculation into an existing proton therapy inverse planning process. We tested the developed library in a typical inverse optimization system with four patient cases. The library achieved the targeted functions by supporting inverse planning in various proton therapy schemes, e.g. single field uniform dose, 3D intensity modulated proton therapy, and distal edge tracking. The efficiency was 41.6  ±  15.3% higher than the use of a GPU-based MC package in a conventional calculation scheme. The total computation time ranged between 2 and 50 min on a single GPU card depending on the problem size.

A New Approach to Integrate GPU-based Monte Carlo Simulation into Inverse Treatment Plan Optimization for Proton Therapy

PubMed Central

Li, Yongbao; Tian, Zhen; Song, Ting; Wu, Zhaoxia; Liu, Yaqiang; Jiang, Steve; Jia, Xun

2016-01-01

Monte Carlo (MC)-based spot dose calculation is highly desired for inverse treatment planning in proton therapy because of its accuracy. Recent studies on biological optimization have also indicated the use of MC methods to compute relevant quantities of interest, e.g. linear energy transfer. Although GPU-based MC engines have been developed to address inverse optimization problems, their efficiency still needs to be improved. Also, the use of a large number of GPUs in MC calculation is not favorable for clinical applications. The previously proposed adaptive particle sampling (APS) method can improve the efficiency of MC-based inverse optimization by using the computationally expensive MC simulation more effectively. This method is more efficient than the conventional approach that performs spot dose calculation and optimization in two sequential steps. In this paper, we propose a computational library to perform MC-based spot dose calculation on GPU with the APS scheme. The implemented APS method performs a non-uniform sampling of the particles from pencil beam spots during the optimization process, favoring those from the high intensity spots. The library also conducts two computationally intensive matrix-vector operations frequently used when solving an optimization problem. This library design allows a streamlined integration of the MC-based spot dose calculation into an existing proton therapy inverse planning process. We tested the developed library in a typical inverse optimization system with four patient cases. The library achieved the targeted functions by supporting inverse planning in various proton therapy schemes, e.g. single field uniform dose, 3D intensity modulated proton therapy, and distal edge tracking. The efficiency was 41.6±15.3% higher than the use of a GPU-based MC package in a conventional calculation scheme. The total computation time ranged between 2 and 50 min on a single GPU card depending on the problem size. PMID:27991456

Using Graphical Processing Units to Accelerate Orthorectification, Atmospheric Correction and Transformations for Big Data

NASA Astrophysics Data System (ADS)

O'Connor, A. S.; Justice, B.; Harris, A. T.

2013-12-01

Graphics Processing Units (GPUs) are high-performance multiple-core processors capable of very high computational speeds and large data throughput. Modern GPUs are inexpensive and widely available commercially. These are general-purpose parallel processors with support for a variety of programming interfaces, including industry standard languages such as C. GPU implementations of algorithms that are well suited for parallel processing can often achieve speedups of several orders of magnitude over optimized CPU codes. Significant improvements in speeds for imagery orthorectification, atmospheric correction, target detection and image transformations like Independent Components Analsyis (ICA) have been achieved using GPU-based implementations. Additional optimizations, when factored in with GPU processing capabilities, can provide 50x - 100x reduction in the time required to process large imagery. Exelis Visual Information Solutions (VIS) has implemented a CUDA based GPU processing frame work for accelerating ENVI and IDL processes that can best take advantage of parallelization. Testing Exelis VIS has performed shows that orthorectification can take as long as two hours with a WorldView1 35,0000 x 35,000 pixel image. With GPU orthorecification, the same orthorectification process takes three minutes. By speeding up image processing, imagery can successfully be used by first responders, scientists making rapid discoveries with near real time data, and provides an operational component to data centers needing to quickly process and disseminate data.

Fast GPU-based Monte Carlo code for SPECT/CT reconstructions generates improved 177Lu images.

PubMed

Rydén, T; Heydorn Lagerlöf, J; Hemmingsson, J; Marin, I; Svensson, J; Båth, M; Gjertsson, P; Bernhardt, P

2018-01-04

Full Monte Carlo (MC)-based SPECT reconstructions have a strong potential for correcting for image degrading factors, but the reconstruction times are long. The objective of this study was to develop a highly parallel Monte Carlo code for fast, ordered subset expectation maximum (OSEM) reconstructions of SPECT/CT images. The MC code was written in the Compute Unified Device Architecture language for a computer with four graphics processing units (GPUs) (GeForce GTX Titan X, Nvidia, USA). This enabled simulations of parallel photon emissions from the voxels matrix (128 3 or 256 3 ). Each computed tomography (CT) number was converted to attenuation coefficients for photo absorption, coherent scattering, and incoherent scattering. For photon scattering, the deflection angle was determined by the differential scattering cross sections. An angular response function was developed and used to model the accepted angles for photon interaction with the crystal, and a detector scattering kernel was used for modeling the photon scattering in the detector. Predefined energy and spatial resolution kernels for the crystal were used. The MC code was implemented in the OSEM reconstruction of clinical and phantom 177 Lu SPECT/CT images. The Jaszczak image quality phantom was used to evaluate the performance of the MC reconstruction in comparison with attenuated corrected (AC) OSEM reconstructions and attenuated corrected OSEM reconstructions with resolution recovery corrections (RRC). The performance of the MC code was 3200 million photons/s. The required number of photons emitted per voxel to obtain a sufficiently low noise level in the simulated image was 200 for a 128 3 voxel matrix. With this number of emitted photons/voxel, the MC-based OSEM reconstruction with ten subsets was performed within 20 s/iteration. The images converged after around six iterations. Therefore, the reconstruction time was around 3 min. The activity recovery for the spheres in the Jaszczak phantom was clearly improved with MC-based OSEM reconstruction, e.g., the activity recovery was 88% for the largest sphere, while it was 66% for AC-OSEM and 79% for RRC-OSEM. The GPU-based MC code generated an MC-based SPECT/CT reconstruction within a few minutes, and reconstructed patient images of 177 Lu-DOTATATE treatments revealed clearly improved resolution and contrast.

Protein alignment algorithms with an efficient backtracking routine on multiple GPUs.

PubMed

Blazewicz, Jacek; Frohmberg, Wojciech; Kierzynka, Michal; Pesch, Erwin; Wojciechowski, Pawel

2011-05-20

Pairwise sequence alignment methods are widely used in biological research. The increasing number of sequences is perceived as one of the upcoming challenges for sequence alignment methods in the nearest future. To overcome this challenge several GPU (Graphics Processing Unit) computing approaches have been proposed lately. These solutions show a great potential of a GPU platform but in most cases address the problem of sequence database scanning and computing only the alignment score whereas the alignment itself is omitted. Thus, the need arose to implement the global and semiglobal Needleman-Wunsch, and Smith-Waterman algorithms with a backtracking procedure which is needed to construct the alignment. In this paper we present the solution that performs the alignment of every given sequence pair, which is a required step for progressive multiple sequence alignment methods, as well as for DNA recognition at the DNA assembly stage. Performed tests show that the implementation, with performance up to 6.3 GCUPS on a single GPU for affine gap penalties, is very efficient in comparison to other CPU and GPU-based solutions. Moreover, multiple GPUs support with load balancing makes the application very scalable. The article shows that the backtracking procedure of the sequence alignment algorithms may be designed to fit in with the GPU architecture. Therefore, our algorithm, apart from scores, is able to compute pairwise alignments. This opens a wide range of new possibilities, allowing other methods from the area of molecular biology to take advantage of the new computational architecture. Performed tests show that the efficiency of the implementation is excellent. Moreover, the speed of our GPU-based algorithms can be almost linearly increased when using more than one graphics card.

The normal vaginal and uterine bacterial microbiome in giant pandas (Ailuropoda melanoleuca).

PubMed

Yang, Xin; Cheng, Guangyang; Li, Caiwu; Yang, Jiang; Li, Jianan; Chen, Danyu; Zou, Wencheng; Jin, SenYan; Zhang, Hemin; Li, Desheng; He, Yongguo; Wang, Chengdong; Wang, Min; Wang, Hongning

2017-06-01

While the health effects of the colonization of the reproductive tracts of mammals by bacterial communities are widely known, there is a dearth of knowledge specifically in relation to giant panda microbiomes. In order to investigate the vaginal and uterine bacterial diversity of healthy giant pandas, we used high-throughput sequence analysis of portions of the 16S rRNA gene, based on samples taken from the vaginas (GPV group) and uteri (GPU group) of these animals. Results showed that the four most abundant phyla, which contained in excess of 98% of the total sequences, were Proteobacteria (59.2% for GPV and 51.4% for GPU), Firmicutes (34.4% for GPV and 23.3% for GPU), Actinobacteria (5.2% for GPV and 14.0% for GPU) and Bacteroidetes (0.3% for GPV and 10.3% for GPU). At the genus level, Escherichia was most abundant (11.0%) in the GPV, followed by Leuconostoc (8.7%), Pseudomonas (8.0%), Acinetobacter (7.3%), Streptococcus (6.3%) and Lactococcus (6.0%). In relation to the uterine samples, Janthinobacterium had the highest prevalence rate (20.2%), followed by Corynebacterium (13.2%), Streptococcus (19.6%), Psychrobacter (9.3%), Escherichia (7.5%) and Bacteroides (6.2%). Moreover, both Chao1 and abundance-based coverage estimator (ACE) species richness indices, which were operating at the same sequencing depth for each sample, demonstrated that GPV had more species richness than GPU, while Simpson and Shannon indices of diversity indicated that GPV had the higher bacterial diversity. These findings contribute to our understanding of the potential influence abnormal reproductive tract microbial communities have on negative pregnancy outcomes in giant pandas. Copyright © 2017 Elsevier GmbH. All rights reserved.

X-ray cone-beam computed tomography: principles, applications, challenges and solutions

NASA Astrophysics Data System (ADS)

Noo, Frederic

2010-03-01

In the nineties, x-ray computed tomography, commonly referred to as CT, seemed to be on the track to become old technology, bound to be replaced by more sophisticated techniques such as magnetic resonance imaging, due in particular to the harmful effects of x-ray radiation exposure. Yet, the new century brought with it new technology that allowed a complete change in trends and re-affirmed CT as an essential tool in radiology. For instance, the popularity of CT in 2007 was such that approximately 68.7 million CT examinations were performed in the United States, which was nearly 2.5 times the number of magnetic resonance (MRI) examinations. More than that, CT has expanded beyond its conventional diagnostic role; CT is now used routinely in interventional radiology and also in radiation therapy treatment. The technology advances that allowed the revival of CT are those that made fast, accurate cone-beam data acquisition possible. Nowadays, cone-beam data acquisition allows scanning large volumes with isotropic sub-millimeter spatial resolution in a very fast time, which can be as short as 500ms for cardiac imaging. The principles of cone-beam imaging will be first reviewed. Then a discussion of its applications will be given. Old and new challenges will be presented along the way with current solutions.

SU-G-IeP3-10: Molecular Imaging with Clinical X-Ray Sources and Compton Cameras

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vernekohl, D; Ahmad, M; Chinn, G

2016-06-15

Purpose: The application of Compton cameras (CC) is a novel approach translating XFCT to a practical modality realized with clinical CT systems without the restriction of pencil beams. The dual modality design offers additional information without extra patient dose. The purpose of this work is to investigate the feasibility and efficacy of using CCs for volumetric x-ray fluorescence (XF) imaging by Monte Carlo (MC) simulations and statistical image reconstruction. Methods: The feasibility of a CC for imaging x-ray fluorescence emitted from targeted lesions is examined by MC simulations. 3 mm diameter water spheres with various gold concentrations and detector distancesmore » are placed inside the lung of an adult human phantom (MIRD) and are irradiated with both fan and cone-beam geometries. A sandwich design CC composed of Silicon and CdTe is used to image the gold nanoparticle distribution. The detection system comprises four 16×26 cm{sup 2} detector panels placed on the chest of a MIRD phantom. Constraints of energy-, spatial-resolution, clinical geometries and Doppler broadening are taken into account. Image reconstruction is performed with a list-mode MLEM algorithm with cone-projector on a GPU. Results: The comparison of reconstruction of cone- and fan-beam excitation shows that the spatial resolution is improved by 23% for fan-beams with significantly decreased processing time. Cone-beam excitation increases scatter content disturbing quantification of lesions near the body surface. Spatial resolution and detectability limit in the center of the lung is 8.7 mm and 20 fM for 50 nm diameter gold nanoparticles at 20 mGy. Conclusion: The implementation of XFCT with a CC is a feasible method for molecular imaging with high atomic number probes. Given constrains of detector resolutions, Doppler broadening, and limited exposure dose, spatial resolutions comparable with PET and molecular sensitivities in the fM range are realizable with current detector technology.« less

Real time mitigation of atmospheric turbulence in long distance imaging using the lucky region fusion algorithm with FPGA and GPU hardware acceleration

NASA Astrophysics Data System (ADS)

Jackson, Christopher Robert

"Lucky-region" fusion (LRF) is a synthetic imaging technique that has proven successful in enhancing the quality of images distorted by atmospheric turbulence. The LRF algorithm selects sharp regions of an image obtained from a series of short exposure frames, and fuses the sharp regions into a final, improved image. In previous research, the LRF algorithm had been implemented on a PC using the C programming language. However, the PC did not have sufficient sequential processing power to handle real-time extraction, processing and reduction required when the LRF algorithm was applied to real-time video from fast, high-resolution image sensors. This thesis describes two hardware implementations of the LRF algorithm to achieve real-time image processing. The first was created with a VIRTEX-7 field programmable gate array (FPGA). The other developed using the graphics processing unit (GPU) of a NVIDIA GeForce GTX 690 video card. The novelty in the FPGA approach is the creation of a "black box" LRF video processing system with a general camera link input, a user controller interface, and a camera link video output. We also describe a custom hardware simulation environment we have built to test the FPGA LRF implementation. The advantage of the GPU approach is significantly improved development time, integration of image stabilization into the system, and comparable atmospheric turbulence mitigation.

Gpu Implementation of a Viscous Flow Solver on Unstructured Grids

NASA Astrophysics Data System (ADS)

Xu, Tianhao; Chen, Long

2016-06-01

Graphics processing units have gained popularities in scientific computing over past several years due to their outstanding parallel computing capability. Computational fluid dynamics applications involve large amounts of calculations, therefore a latest GPU card is preferable of which the peak computing performance and memory bandwidth are much better than a contemporary high-end CPU. We herein focus on the detailed implementation of our GPU targeting Reynolds-averaged Navier-Stokes equations solver based on finite-volume method. The solver employs a vertex-centered scheme on unstructured grids for the sake of being capable of handling complex topologies. Multiple optimizations are carried out to improve the memory accessing performance and kernel utilization. Both steady and unsteady flow simulation cases are carried out using explicit Runge-Kutta scheme. The solver with GPU acceleration in this paper is demonstrated to have competitive advantages over the CPU targeting one.

Strong scaling of general-purpose molecular dynamics simulations on GPUs

NASA Astrophysics Data System (ADS)

Glaser, Jens; Nguyen, Trung Dac; Anderson, Joshua A.; Lui, Pak; Spiga, Filippo; Millan, Jaime A.; Morse, David C.; Glotzer, Sharon C.

2015-07-01

We describe a highly optimized implementation of MPI domain decomposition in a GPU-enabled, general-purpose molecular dynamics code, HOOMD-blue (Anderson and Glotzer, 2013). Our approach is inspired by a traditional CPU-based code, LAMMPS (Plimpton, 1995), but is implemented within a code that was designed for execution on GPUs from the start (Anderson et al., 2008). The software supports short-ranged pair force and bond force fields and achieves optimal GPU performance using an autotuning algorithm. We are able to demonstrate equivalent or superior scaling on up to 3375 GPUs in Lennard-Jones and dissipative particle dynamics (DPD) simulations of up to 108 million particles. GPUDirect RDMA capabilities in recent GPU generations provide better performance in full double precision calculations. For a representative polymer physics application, HOOMD-blue 1.0 provides an effective GPU vs. CPU node speed-up of 12.5 ×.

Multi-GPU and multi-CPU accelerated FDTD scheme for vibroacoustic applications

NASA Astrophysics Data System (ADS)

Francés, J.; Otero, B.; Bleda, S.; Gallego, S.; Neipp, C.; Márquez, A.; Beléndez, A.

2015-06-01

The Finite-Difference Time-Domain (FDTD) method is applied to the analysis of vibroacoustic problems and to study the propagation of longitudinal and transversal waves in a stratified media. The potential of the scheme and the relevance of each acceleration strategy for massively computations in FDTD are demonstrated in this work. In this paper, we propose two new specific implementations of the bi-dimensional scheme of the FDTD method using multi-CPU and multi-GPU, respectively. In the first implementation, an open source message passing interface (OMPI) has been included in order to massively exploit the resources of a biprocessor station with two Intel Xeon processors. Moreover, regarding CPU code version, the streaming SIMD extensions (SSE) and also the advanced vectorial extensions (AVX) have been included with shared memory approaches that take advantage of the multi-core platforms. On the other hand, the second implementation called the multi-GPU code version is based on Peer-to-Peer communications available in CUDA on two GPUs (NVIDIA GTX 670). Subsequently, this paper presents an accurate analysis of the influence of the different code versions including shared memory approaches, vector instructions and multi-processors (both CPU and GPU) and compares them in order to delimit the degree of improvement of using distributed solutions based on multi-CPU and multi-GPU. The performance of both approaches was analysed and it has been demonstrated that the addition of shared memory schemes to CPU computing improves substantially the performance of vector instructions enlarging the simulation sizes that use efficiently the cache memory of CPUs. In this case GPU computing is slightly twice times faster than the fine tuned CPU version in both cases one and two nodes. However, for massively computations explicit vector instructions do not worth it since the memory bandwidth is the limiting factor and the performance tends to be the same than the sequential version with auto-vectorisation and also shared memory approach. In this scenario GPU computing is the best option since it provides a homogeneous behaviour. More specifically, the speedup of GPU computing achieves an upper limit of 12 for both one and two GPUs, whereas the performance reaches peak values of 80 GFlops and 146 GFlops for the performance for one GPU and two GPUs respectively. Finally, the method is applied to an earth crust profile in order to demonstrate the potential of our approach and the necessity of applying acceleration strategies in these type of applications.

Accelerating three-dimensional FDTD calculations on GPU clusters for electromagnetic field simulation.

PubMed

Nagaoka, Tomoaki; Watanabe, Soichi

2012-01-01

Electromagnetic simulation with anatomically realistic computational human model using the finite-difference time domain (FDTD) method has recently been performed in a number of fields in biomedical engineering. To improve the method's calculation speed and realize large-scale computing with the computational human model, we adapt three-dimensional FDTD code to a multi-GPU cluster environment with Compute Unified Device Architecture and Message Passing Interface. Our multi-GPU cluster system consists of three nodes. The seven GPU boards (NVIDIA Tesla C2070) are mounted on each node. We examined the performance of the FDTD calculation on multi-GPU cluster environment. We confirmed that the FDTD calculation on the multi-GPU clusters is faster than that on a multi-GPU (a single workstation), and we also found that the GPU cluster system calculate faster than a vector supercomputer. In addition, our GPU cluster system allowed us to perform the large-scale FDTD calculation because were able to use GPU memory of over 100 GB.

GPUbased, Microsecond Latency, HectoChannel MIMO Feedback Control of Magnetically Confined Plasmas

NASA Astrophysics Data System (ADS)

Rath, Nikolaus

Feedback control has become a crucial tool in the research on magnetic confinement of plasmas for achieving controlled nuclear fusion. This thesis presents a novel plasma feedback control system that, for the first time, employs a Graphics Processing Unit (GPU) for microsecond-latency, real-time control computations. This novel application area for GPU computing is opened up by a new system architecture that is optimized for low-latency computations on less than kilobyte sized data samples as they occur in typical plasma control algorithms. In contrast to traditional GPU computing approaches that target complex, high-throughput computations with massive amounts of data, the architecture presented in this thesis uses the GPU as the primary processing unit rather than as an auxiliary of the CPU, and data is transferred from A-D/D-A converters directly into GPU memory using peer-to-peer PCI Express transfers. The described design has been implemented in a new, GPU-based control system for the High-Beta Tokamak - Extended Pulse (HBT-EP) device. The system is built from commodity hardware and uses an NVIDIA GeForce GPU and D-TACQ A-D/D-A converters providing a total of 96 input and 64 output channels. The system is able to run with sampling periods down to 4 μs and latencies down to 8 μs. The GPU provides a total processing power of 1.5 x 1012 floating point operations per second. To illustrate the performance and versatility of both the general architecture and concrete implementation, a new control algorithm has been developed. The algorithm is designed for the control of multiple rotating magnetic perturbations in situations where the plasma equilibrium is not known exactly and features an adaptive system model: instead of requiring the rotation frequencies and growth rates embedded in the system model to be set a priori, the adaptive algorithm derives these parameters from the evolution of the perturbation amplitudes themselves. This results in non-linear control computations with high computational demands, but is handled easily by the GPU based system. Both digital processing latency and an arbitrary multi-pole response of amplifiers and control coils is fully taken into account for the generation of control signals. To separate sensor signals into perturbed and equilibrium components without knowledge of the equilibrium fields, a new separation method based on biorthogonal decomposition is introduced and used to derive a filter that performs the separation in real-time. The control algorithm has been implemented and tested on the new, GPU-based feedback control system of the HBT-EP tokamak. In this instance, the algorithm was set up to control four rotating n = 1 perturbations at different poloidal angles. The perturbations were treated as coupled in frequency but independent in amplitude and phase, so that the system effectively controls a helical n = 1 perturbation with unknown poloidal spectrum. Depending on the plasma's edge safety factor and rotation frequency, the control system is shown to be able to suppress the amplitude of the dominant 8 kHz mode by up to 60% or amplify the saturated amplitude by a factor of up to two. Intermediate feedback phases combine suppression and amplification with a speed up or slow down of the mode rotation frequency. Increasing feedback gain results in the excitation of an additional, slowly rotating 1.4 kHz mode without further effects on the 8 kHz mode. The feedback performance is found to exceed previous results obtained with an FPGA- and Kalman-filter based control system without requiring any tuning of system model parameters. Experimental results are compared with simulations based on a combination of the Boozer surface current model and the Fitzpatrick-Aydemir model. Within the subset of phenomena that can be represented by the model as well as determined experimentally, qualitative agreement is found.

Problems Related to Parallelization of CFD Algorithms on GPU, Multi-GPU and Hybrid Architectures

NASA Astrophysics Data System (ADS)

Biazewicz, Marek; Kurowski, Krzysztof; Ludwiczak, Bogdan; Napieraia, Krystyna

2010-09-01

Computational Fluid Dynamics (CFD) is one of the branches of fluid mechanics, which uses numerical methods and algorithms to solve and analyze fluid flows. CFD is used in various domains, such as oil and gas reservoir uncertainty analysis, aerodynamic body shapes optimization (e.g. planes, cars, ships, sport helmets, skis), natural phenomena analysis, numerical simulation for weather forecasting or realistic visualizations. CFD problem is very complex and needs a lot of computational power to obtain the results in a reasonable time. We have implemented a parallel application for two-dimensional CFD simulation with a free surface approximation (MAC method) using new hardware architectures, in particular multi-GPU and hybrid computing environments. For this purpose we decided to use NVIDIA graphic cards with CUDA environment due to its simplicity of programming and good computations performance. We used finite difference discretization of Navier-Stokes equations, where fluid is propagated over an Eulerian Grid. In this model, the behavior of the fluid inside the cell depends only on the properties of local, surrounding cells, therefore it is well suited for the GPU-based architecture. In this paper we demonstrate how to use efficiently the computing power of GPUs for CFD. Additionally, we present some best practices to help users analyze and improve the performance of CFD applications executed on GPU. Finally, we discuss various challenges around the multi-GPU implementation on the example of matrix multiplication.

The novel implicit LU-SGS parallel iterative method based on the diffusion equation of a nuclear reactor on a GPU cluster

NASA Astrophysics Data System (ADS)

Zhang, Jilin; Sha, Chaoqun; Wu, Yusen; Wan, Jian; Zhou, Li; Ren, Yongjian; Si, Huayou; Yin, Yuyu; Jing, Ya

2017-02-01

GPU not only is used in the field of graphic technology but also has been widely used in areas needing a large number of numerical calculations. In the energy industry, because of low carbon, high energy density, high duration and other characteristics, the development of nuclear energy cannot easily be replaced by other energy sources. Management of core fuel is one of the major areas of concern in a nuclear power plant, and it is directly related to the economic benefits and cost of nuclear power. The large-scale reactor core expansion equation is large and complicated, so the calculation of the diffusion equation is crucial in the core fuel management process. In this paper, we use CUDA programming technology on a GPU cluster to run the LU-SGS parallel iterative calculation against the background of the diffusion equation of the reactor. We divide one-dimensional and two-dimensional mesh into a plurality of domains, with each domain evenly distributed on the GPU blocks. A parallel collision scheme is put forward that defines the virtual boundary of the grid exchange information and data transmission by non-stop collision. Compared with the serial program, the experiment shows that GPU greatly improves the efficiency of program execution and verifies that GPU is playing a much more important role in the field of numerical calculations.

Compressed sensing with gradient total variation for low-dose CBCT reconstruction

NASA Astrophysics Data System (ADS)

Seo, Chang-Woo; Cha, Bo Kyung; Jeon, Seongchae; Huh, Young; Park, Justin C.; Lee, Byeonghun; Baek, Junghee; Kim, Eunyoung

2015-06-01

This paper describes the improvement of convergence speed with gradient total variation (GTV) in compressed sensing (CS) for low-dose cone-beam computed tomography (CBCT) reconstruction. We derive a fast algorithm for the constrained total variation (TV)-based a minimum number of noisy projections. To achieve this task we combine the GTV with a TV-norm regularization term to promote an accelerated sparsity in the X-ray attenuation characteristics of the human body. The GTV is derived from a TV and enforces more efficient computationally and faster in convergence until a desired solution is achieved. The numerical algorithm is simple and derives relatively fast convergence. We apply a gradient projection algorithm that seeks a solution iteratively in the direction of the projected gradient while enforcing a non-negatively of the found solution. In comparison with the Feldkamp, Davis, and Kress (FDK) and conventional TV algorithms, the proposed GTV algorithm showed convergence in ≤18 iterations, whereas the original TV algorithm needs at least 34 iterations in reducing 50% of the projections compared with the FDK algorithm in order to reconstruct the chest phantom images. Future investigation includes improving imaging quality, particularly regarding X-ray cone-beam scatter, and motion artifacts of CBCT reconstruction.

Toward Fast and Accurate Binding Affinity Prediction with pmemdGTI: An Efficient Implementation of GPU-Accelerated Thermodynamic Integration.

PubMed

Lee, Tai-Sung; Hu, Yuan; Sherborne, Brad; Guo, Zhuyan; York, Darrin M

2017-07-11

We report the implementation of the thermodynamic integration method on the pmemd module of the AMBER 16 package on GPUs (pmemdGTI). The pmemdGTI code typically delivers over 2 orders of magnitude of speed-up relative to a single CPU core for the calculation of ligand-protein binding affinities with no statistically significant numerical differences and thus provides a powerful new tool for drug discovery applications.

A review of GPU-based medical image reconstruction.

PubMed

Després, Philippe; Jia, Xun

2017-10-01

Tomographic image reconstruction is a computationally demanding task, even more so when advanced models are used to describe a more complete and accurate picture of the image formation process. Such advanced modeling and reconstruction algorithms can lead to better images, often with less dose, but at the price of long calculation times that are hardly compatible with clinical workflows. Fortunately, reconstruction tasks can often be executed advantageously on Graphics Processing Units (GPUs), which are exploited as massively parallel computational engines. This review paper focuses on recent developments made in GPU-based medical image reconstruction, from a CT, PET, SPECT, MRI and US perspective. Strategies and approaches to get the most out of GPUs in image reconstruction are presented as well as innovative applications arising from an increased computing capacity. The future of GPU-based image reconstruction is also envisioned, based on current trends in high-performance computing. Copyright © 2017 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

NaNet: a configurable NIC bridging the gap between HPC and real-time HEP GPU computing

NASA Astrophysics Data System (ADS)

Lonardo, A.; Ameli, F.; Ammendola, R.; Biagioni, A.; Cotta Ramusino, A.; Fiorini, M.; Frezza, O.; Lamanna, G.; Lo Cicero, F.; Martinelli, M.; Neri, I.; Paolucci, P. S.; Pastorelli, E.; Pontisso, L.; Rossetti, D.; Simeone, F.; Simula, F.; Sozzi, M.; Tosoratto, L.; Vicini, P.

2015-04-01

NaNet is a FPGA-based PCIe Network Interface Card (NIC) design with GPUDirect and Remote Direct Memory Access (RDMA) capabilities featuring a configurable and extensible set of network channels. The design currently supports both standard—Gbe (1000BASE-T) and 10GbE (10Base-R)—and custom—34 Gbps APElink and 2.5 Gbps deterministic latency KM3link—channels, but its modularity allows for straightforward inclusion of other link technologies. The GPUDirect feature combined with a transport layer offload module and a data stream processing stage makes NaNet a low-latency NIC suitable for real-time GPU processing. In this paper we describe the NaNet architecture and its performances, exhibiting two of its use cases: the GPU-based low-level trigger for the RICH detector in the NA62 experiment at CERN and the on-/off-shore data transport system for the KM3NeT-IT underwater neutrino telescope.

A fast parallel clustering algorithm for molecular simulation trajectories.

PubMed

Zhao, Yutong; Sheong, Fu Kit; Sun, Jian; Sander, Pedro; Huang, Xuhui

2013-01-15

We implemented a GPU-powered parallel k-centers algorithm to perform clustering on the conformations of molecular dynamics (MD) simulations. The algorithm is up to two orders of magnitude faster than the CPU implementation. We tested our algorithm on four protein MD simulation datasets ranging from the small Alanine Dipeptide to a 370-residue Maltose Binding Protein (MBP). It is capable of grouping 250,000 conformations of the MBP into 4000 clusters within 40 seconds. To achieve this, we effectively parallelized the code on the GPU and utilize the triangle inequality of metric spaces. Furthermore, the algorithm's running time is linear with respect to the number of cluster centers. In addition, we found the triangle inequality to be less effective in higher dimensions and provide a mathematical rationale. Finally, using Alanine Dipeptide as an example, we show a strong correlation between cluster populations resulting from the k-centers algorithm and the underlying density. © 2012 Wiley Periodicals, Inc. Copyright © 2012 Wiley Periodicals, Inc.

Efficient Scalable Median Filtering Using Histogram-Based Operations.

PubMed

Green, Oded

2018-05-01

Median filtering is a smoothing technique for noise removal in images. While there are various implementations of median filtering for a single-core CPU, there are few implementations for accelerators and multi-core systems. Many parallel implementations of median filtering use a sorting algorithm for rearranging the values within a filtering window and taking the median of the sorted value. While using sorting algorithms allows for simple parallel implementations, the cost of the sorting becomes prohibitive as the filtering windows grow. This makes such algorithms, sequential and parallel alike, inefficient. In this work, we introduce the first software parallel median filtering that is non-sorting-based. The new algorithm uses efficient histogram-based operations. These reduce the computational requirements of the new algorithm while also accessing the image fewer times. We show an implementation of our algorithm for both the CPU and NVIDIA's CUDA supported graphics processing unit (GPU). The new algorithm is compared with several other leading CPU and GPU implementations. The CPU implementation has near perfect linear scaling with a speedup on a quad-core system. The GPU implementation is several orders of magnitude faster than the other GPU implementations for mid-size median filters. For small kernels, and , comparison-based approaches are preferable as fewer operations are required. Lastly, the new algorithm is open-source and can be found in the OpenCV library.

Advanced Offshore Wind Turbine/Foundation Concept for the Great Lakes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Afjeh, Abdollah A.; Windpower, Nautica; Marrone, Joseph

2013-08-29

This project investigated a conceptual 2-bladed rotor wind turbine design and assessed its feasibility for installation in the Great Lakes. The levelized cost of energy was used for this purpose. A location in Lake Erie near the coast of Cleveland, Ohio was selected as the application site. The loading environment was defined using wind and wave data collected at a weather station in Lake Erie near Cleveland. In addition, the probability distributions of the annual significant wave height and wind speed were determined. A model of the dependence of the above two quantities was also developed and used in themore » study of wind turbine system loads. Loads from ice floes and ridges were also included.The NREL 5 MW 3-bladed rotor wind turbine concept was used as the baseline design. The proposed turbine design employs variable pitch blade control with tip-brakes and a teeter mechanism. The rotor diameter, rated power and the tower dimensions were selected to closely match those of the NREL 5 MW wind turbine.A semi-floating gravity base foundation was designed for this project primarily to adapt to regional logistical constraints to transport and install the gravity base foundation. This foundation consists of, from bottom to top, a base plate, a buoyancy chamber, a taper zone, a column (with ice cone), and a service platform. A compound upward-downward ice cone was selected to secure the foundation from moving because of ice impact.The turbine loads analysis was based on International ElectroTechnical Committee (IEC) Standard 61400-1, Class III winds. The NREL software FAST was the primary computational tool used in this study to determine all design load cases. An initial set of studies of the dynamics of wind turbines using Automatic Dynamic Analysis of Mechanical Systems (ADAMS) demonstrated that FAST and ADAMS load predictions were comparable. Because of its relative simplicity and short run times, FAST was selected for this study. For ice load calculations, a method was developed and implemented in FAST to extend its capability for ice load modeling.Both upwind and downwind 2-bladed rotor wind turbine designs were developed and studied. The new rotor blade uses a new twist angle distribution design and a new pitch control algorithm compared with the baseline model. The coning and tilt angles were selected for both the upwind and downwind configurations to maximize the annual energy production. The risk of blade-tower impact is greater for the downwind design, particularly under a power grid fault; however, this risk was effectively reduced by adjusting the tilt angle for the downwind configuration.« less

CUDA programs for the GPU computing of the Swendsen-Wang multi-cluster spin flip algorithm: 2D and 3D Ising, Potts, and XY models

NASA Astrophysics Data System (ADS)

Komura, Yukihiro; Okabe, Yutaka

2014-03-01

We present sample CUDA programs for the GPU computing of the Swendsen-Wang multi-cluster spin flip algorithm. We deal with the classical spin models; the Ising model, the q-state Potts model, and the classical XY model. As for the lattice, both the 2D (square) lattice and the 3D (simple cubic) lattice are treated. We already reported the idea of the GPU implementation for 2D models (Komura and Okabe, 2012). We here explain the details of sample programs, and discuss the performance of the present GPU implementation for the 3D Ising and XY models. We also show the calculated results of the moment ratio for these models, and discuss phase transitions. Catalogue identifier: AERM_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AERM_v1_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 5632 No. of bytes in distributed program, including test data, etc.: 14688 Distribution format: tar.gz Programming language: C, CUDA. Computer: System with an NVIDIA CUDA enabled GPU. Operating system: System with an NVIDIA CUDA enabled GPU. Classification: 23. External routines: NVIDIA CUDA Toolkit 3.0 or newer Nature of problem: Monte Carlo simulation of classical spin systems. Ising, q-state Potts model, and the classical XY model are treated for both two-dimensional and three-dimensional lattices. Solution method: GPU-based Swendsen-Wang multi-cluster spin flip Monte Carlo method. The CUDA implementation for the cluster-labeling is based on the work by Hawick et al. [1] and that by Kalentev et al. [2]. Restrictions: The system size is limited depending on the memory of a GPU. Running time: For the parameters used in the sample programs, it takes about a minute for each program. Of course, it depends on the system size, the number of Monte Carlo steps, etc. References: [1] K.A. Hawick, A. Leist, and D. P. Playne, Parallel Computing 36 (2010) 655-678 [2] O. Kalentev, A. Rai, S. Kemnitzb, and R. Schneider, J. Parallel Distrib. Comput. 71 (2011) 615-620

NLSEmagic: Nonlinear Schrödinger equation multi-dimensional Matlab-based GPU-accelerated integrators using compact high-order schemes

NASA Astrophysics Data System (ADS)

Caplan, R. M.

2013-04-01

We present a simple to use, yet powerful code package called NLSEmagic to numerically integrate the nonlinear Schrödinger equation in one, two, and three dimensions. NLSEmagic is a high-order finite-difference code package which utilizes graphic processing unit (GPU) parallel architectures. The codes running on the GPU are many times faster than their serial counterparts, and are much cheaper to run than on standard parallel clusters. The codes are developed with usability and portability in mind, and therefore are written to interface with MATLAB utilizing custom GPU-enabled C codes with the MEX-compiler interface. The packages are freely distributed, including user manuals and set-up files. Catalogue identifier: AEOJ_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEOJ_v1_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 124453 No. of bytes in distributed program, including test data, etc.: 4728604 Distribution format: tar.gz Programming language: C, CUDA, MATLAB. Computer: PC, MAC. Operating system: Windows, MacOS, Linux. Has the code been vectorized or parallelized?: Yes. Number of processors used: Single CPU, number of GPU processors dependent on chosen GPU card (max is currently 3072 cores on GeForce GTX 690). Supplementary material: Setup guide, Installation guide. RAM: Highly dependent on dimensionality and grid size. For typical medium-large problem size in three dimensions, 4GB is sufficient. Keywords: Nonlinear Schröodinger Equation, GPU, high-order finite difference, Bose-Einstien condensates. Classification: 4.3, 7.7. Nature of problem: Integrate solutions of the time-dependent one-, two-, and three-dimensional cubic nonlinear Schrödinger equation. Solution method: The integrators utilize a fully-explicit fourth-order Runge-Kutta scheme in time and both second- and fourth-order differencing in space. The integrators are written to run on NVIDIA GPUs and are interfaced with MATLAB including built-in visualization and analysis tools. Restrictions: The main restriction for the GPU integrators is the amount of RAM on the GPU as the code is currently only designed for running on a single GPU. Unusual features: Ability to visualize real-time simulations through the interaction of MATLAB and the compiled GPU integrators. Additional comments: Setup guide and Installation guide provided. Program has a dedicated web site at www.nlsemagic.com. Running time: A three-dimensional run with a grid dimension of 87×87×203 for 3360 time steps (100 non-dimensional time units) takes about one and a half minutes on a GeForce GTX 580 GPU card.

LASSIE: simulating large-scale models of biochemical systems on GPUs.

PubMed

Tangherloni, Andrea; Nobile, Marco S; Besozzi, Daniela; Mauri, Giancarlo; Cazzaniga, Paolo

2017-05-10

Mathematical modeling and in silico analysis are widely acknowledged as complementary tools to biological laboratory methods, to achieve a thorough understanding of emergent behaviors of cellular processes in both physiological and perturbed conditions. Though, the simulation of large-scale models-consisting in hundreds or thousands of reactions and molecular species-can rapidly overtake the capabilities of Central Processing Units (CPUs). The purpose of this work is to exploit alternative high-performance computing solutions, such as Graphics Processing Units (GPUs), to allow the investigation of these models at reduced computational costs. LASSIE is a "black-box" GPU-accelerated deterministic simulator, specifically designed for large-scale models and not requiring any expertise in mathematical modeling, simulation algorithms or GPU programming. Given a reaction-based model of a cellular process, LASSIE automatically generates the corresponding system of Ordinary Differential Equations (ODEs), assuming mass-action kinetics. The numerical solution of the ODEs is obtained by automatically switching between the Runge-Kutta-Fehlberg method in the absence of stiffness, and the Backward Differentiation Formulae of first order in presence of stiffness. The computational performance of LASSIE are assessed using a set of randomly generated synthetic reaction-based models of increasing size, ranging from 64 to 8192 reactions and species, and compared to a CPU-implementation of the LSODA numerical integration algorithm. LASSIE adopts a novel fine-grained parallelization strategy to distribute on the GPU cores all the calculations required to solve the system of ODEs. By virtue of this implementation, LASSIE achieves up to 92× speed-up with respect to LSODA, therefore reducing the running time from approximately 1 month down to 8 h to simulate models consisting in, for instance, four thousands of reactions and species. Notably, thanks to its smaller memory footprint, LASSIE is able to perform fast simulations of even larger models, whereby the tested CPU-implementation of LSODA failed to reach termination. LASSIE is therefore expected to make an important breakthrough in Systems Biology applications, for the execution of faster and in-depth computational analyses of large-scale models of complex biological systems.

Transformations based on continuous piecewise-affine velocity fields

DOE PAGES

Freifeld, Oren; Hauberg, Soren; Batmanghelich, Kayhan; ...

2017-01-11

Here, we propose novel finite-dimensional spaces of well-behaved Rn → Rn transformations. The latter are obtained by (fast and highly-accurate) integration of continuous piecewise-affine velocity fields. The proposed method is simple yet highly expressive, effortlessly handles optional constraints (e.g., volume preservation and/or boundary conditions), and supports convenient modeling choices such as smoothing priors and coarse-to-fine analysis. Importantly, the proposed approach, partly due to its rapid likelihood evaluations and partly due to its other properties, facilitates tractable inference over rich transformation spaces, including using Markov-Chain Monte-Carlo methods. Its applications include, but are not limited to: monotonic regression (more generally, optimization overmore » monotonic functions); modeling cumulative distribution functions or histograms; time-warping; image warping; image registration; real-time diffeomorphic image editing; data augmentation for image classifiers. Our GPU-based code is publicly available.« less

Transformations Based on Continuous Piecewise-Affine Velocity Fields

PubMed Central

Freifeld, Oren; Hauberg, Søren; Batmanghelich, Kayhan; Fisher, Jonn W.

2018-01-01

We propose novel finite-dimensional spaces of well-behaved ℝn → ℝn transformations. The latter are obtained by (fast and highly-accurate) integration of continuous piecewise-affine velocity fields. The proposed method is simple yet highly expressive, effortlessly handles optional constraints (e.g., volume preservation and/or boundary conditions), and supports convenient modeling choices such as smoothing priors and coarse-to-fine analysis. Importantly, the proposed approach, partly due to its rapid likelihood evaluations and partly due to its other properties, facilitates tractable inference over rich transformation spaces, including using Markov-Chain Monte-Carlo methods. Its applications include, but are not limited to: monotonic regression (more generally, optimization over monotonic functions); modeling cumulative distribution functions or histograms; time-warping; image warping; image registration; real-time diffeomorphic image editing; data augmentation for image classifiers. Our GPU-based code is publicly available. PMID:28092517

Hypergraph partitioning implementation for parallelizing matrix-vector multiplication using CUDA GPU-based parallel computing

NASA Astrophysics Data System (ADS)

Murni, Bustamam, A.; Ernastuti, Handhika, T.; Kerami, D.

2017-07-01

Calculation of the matrix-vector multiplication in the real-world problems often involves large matrix with arbitrary size. Therefore, parallelization is needed to speed up the calculation process that usually takes a long time. Graph partitioning techniques that have been discussed in the previous studies cannot be used to complete the parallelized calculation of matrix-vector multiplication with arbitrary size. This is due to the assumption of graph partitioning techniques that can only solve the square and symmetric matrix. Hypergraph partitioning techniques will overcome the shortcomings of the graph partitioning technique. This paper addresses the efficient parallelization of matrix-vector multiplication through hypergraph partitioning techniques using CUDA GPU-based parallel computing. CUDA (compute unified device architecture) is a parallel computing platform and programming model that was created by NVIDIA and implemented by the GPU (graphics processing unit).

Kinetics of Exocytosis Is Faster in Cones Than in Rods

PubMed Central

Rabl, Katalin; Cadetti, Lucia; Thoreson, Wallace B.

2006-01-01

Cone-driven responses of second-order retinal neurons are considerably faster than rod-driven responses. We examined whether differences in the kinetics of synaptic transmitter release from rods and cones may contribute to differences in postsynaptic response kinetics. Exocytosis from rods and cones was triggered by membrane depolarization and monitored in two ways: (1) by measuring EPSCs evoked in second-order neurons by depolarizing steps applied to presynaptic rods or cones during simultaneous paired whole-cell recordings or (2) by direct measurements of exocytotic increases in membrane capacitance. The kinetics of release was assessed by varying the length of the depolarizing test step. Both measures of release revealed two kinetic components to the increase in exocytosis as a function of the duration of a step depolarization. In addition to slow sustained components in both cell types, the initial fast component of exocytosis had a time constant of <5 ms in cones, >10-fold faster than that of rods. Rod/cone differences in the kinetics of release were substantiated by a linear correlation between depolarization-evoked capacitance increases and EPSC charge transfer. Experiments on isolated rods indicate that the slower kinetics of exocytosis from rods was not a result of rod–rod coupling. The initial rapid release of vesicles from cones can shape the postsynaptic response and may contribute to the faster responses of cone-driven cells observed at light offset. PMID:15872111

Accelerated event-by-event Monte Carlo microdosimetric calculations of electrons and protons tracks on a multi-core CPU and a CUDA-enabled GPU.

PubMed

Kalantzis, Georgios; Tachibana, Hidenobu

2014-01-01

For microdosimetric calculations event-by-event Monte Carlo (MC) methods are considered the most accurate. The main shortcoming of those methods is the extensive requirement for computational time. In this work we present an event-by-event MC code of low projectile energy electron and proton tracks for accelerated microdosimetric MC simulations on a graphic processing unit (GPU). Additionally, a hybrid implementation scheme was realized by employing OpenMP and CUDA in such a way that both GPU and multi-core CPU were utilized simultaneously. The two implementation schemes have been tested and compared with the sequential single threaded MC code on the CPU. Performance comparison was established on the speed-up for a set of benchmarking cases of electron and proton tracks. A maximum speedup of 67.2 was achieved for the GPU-based MC code, while a further improvement of the speedup up to 20% was achieved for the hybrid approach. The results indicate the capability of our CPU-GPU implementation for accelerated MC microdosimetric calculations of both electron and proton tracks without loss of accuracy. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Advantages of GPU technology in DFT calculations of intercalated graphene

NASA Astrophysics Data System (ADS)

Pešić, J.; Gajić, R.

2014-09-01

Over the past few years, the expansion of general-purpose graphic-processing unit (GPGPU) technology has had a great impact on computational science. GPGPU is the utilization of a graphics-processing unit (GPU) to perform calculations in applications usually handled by the central processing unit (CPU). Use of GPGPUs as a way to increase computational power in the material sciences has significantly decreased computational costs in already highly demanding calculations. A level of the acceleration and parallelization depends on the problem itself. Some problems can benefit from GPU acceleration and parallelization, such as the finite-difference time-domain algorithm (FTDT) and density-functional theory (DFT), while others cannot take advantage of these modern technologies. A number of GPU-supported applications had emerged in the past several years (www.nvidia.com/object/gpu-applications.html). Quantum Espresso (QE) is reported as an integrated suite of open source computer codes for electronic-structure calculations and materials modeling at the nano-scale. It is based on DFT, the use of a plane-waves basis and a pseudopotential approach. Since the QE 5.0 version, it has been implemented as a plug-in component for standard QE packages that allows exploiting the capabilities of Nvidia GPU graphic cards (www.qe-forge.org/gf/proj). In this study, we have examined the impact of the usage of GPU acceleration and parallelization on the numerical performance of DFT calculations. Graphene has been attracting attention worldwide and has already shown some remarkable properties. We have studied an intercalated graphene, using the QE package PHonon, which employs GPU. The term ‘intercalation’ refers to a process whereby foreign adatoms are inserted onto a graphene lattice. In addition, by intercalating different atoms between graphene layers, it is possible to tune their physical properties. Our experiments have shown there are benefits from using GPUs, and we reached an acceleration of several times compared to standard CPU calculations.

Evaluating the Usefulness of a Novel 10B-Carrier Conjugated With Cyclic RGD Peptide in Boron Neutron Capture Therapy

PubMed Central

Masunaga, Shin-ichiro; Kimura, Sadaaki; Harada, Tomohiro; Okuda, Kensuke; Sakurai, Yoshinori; Tanaka, Hiroki; Suzuki, Minoru; Kondo, Natsuko; Maruhashi, Akira; Nagasawa, Hideko; Ono, Koji

2012-01-01

Background To evaluate the usefulness of a novel 10B-carrier conjugated with an integrin-binding cyclic RGD peptide (GPU-201) in boron neutron capture therapy (BNCT). Methods GPU-201 was synthesized from integrin-binding Arg-Gly-Asp (RGD) consensus sequence of matrix proteins and a 10B cluster 1, 2-dicarba-closo-dodecaborane-10B. Mercaptododecaborate-10B (BSH) dissolved in physiological saline and BSH and GPU-201 dissolved with cyclodextrin (CD) as a solubilizing and dispersing agent were intraperitoneally administered to SCC VII tumor-bearing mice. Then, the 10B concentrations in the tumors and normal tissues were measured by γ-ray spectrometry. Meanwhile, tumor-bearing mice were continuously given 5-bromo-2’-deoxyuridine (BrdU) to label all proliferating (P) cells in the tumors, then treated with GPU-201, BSH-CD, or BSH. Immediately after reactor neutron beam or γ-ray irradiation, during which intratumor 10B concentrations were kept at levels similar to each other, cells from some tumors were isolated and incubated with a cytokinesis blocker. The responses of the Q and total (= P + Q) cell populations were assessed based on the frequency of micronuclei using immunofluorescence staining for BrdU. Results The 10B from BSH was washed away rapidly in all these tissues and the retention of 10B from BSH-CD and GPU-201 was similar except in blood where the 10B concentration from GPU-201 was higher for longer. GPU-201 showed a significantly stronger radio-sensitizing effect under neutron beam irradiation on both total and Q cell populations than any other 10B-carrier. Conclusion A novel 10B-carrier conjugated with an integrin-binding RGD peptide (GPU-201) that sensitized tumor cells more markedly than conventional 10B-carriers may be a promising candidate for use in BNCT. However, its toxicity needs to be tested further. PMID:29147290