Traffic Simulations on Parallel Computers Using Domain Decomposition Techniques

DOT National Transportation Integrated Search

1995-01-01

Large scale simulations of Intelligent Transportation Systems (ITS) can only be acheived by using the computing resources offered by parallel computing architectures. Domain decomposition techniques are proposed which allow the performance of traffic...

Parallel Clustering Algorithm for Large-Scale Biological Data Sets

PubMed Central

Wang, Minchao; Zhang, Wu; Ding, Wang; Dai, Dongbo; Zhang, Huiran; Xie, Hao; Chen, Luonan; Guo, Yike; Xie, Jiang

2014-01-01

Backgrounds Recent explosion of biological data brings a great challenge for the traditional clustering algorithms. With increasing scale of data sets, much larger memory and longer runtime are required for the cluster identification problems. The affinity propagation algorithm outperforms many other classical clustering algorithms and is widely applied into the biological researches. However, the time and space complexity become a great bottleneck when handling the large-scale data sets. Moreover, the similarity matrix, whose constructing procedure takes long runtime, is required before running the affinity propagation algorithm, since the algorithm clusters data sets based on the similarities between data pairs. Methods Two types of parallel architectures are proposed in this paper to accelerate the similarity matrix constructing procedure and the affinity propagation algorithm. The memory-shared architecture is used to construct the similarity matrix, and the distributed system is taken for the affinity propagation algorithm, because of its large memory size and great computing capacity. An appropriate way of data partition and reduction is designed in our method, in order to minimize the global communication cost among processes. Result A speedup of 100 is gained with 128 cores. The runtime is reduced from serval hours to a few seconds, which indicates that parallel algorithm is capable of handling large-scale data sets effectively. The parallel affinity propagation also achieves a good performance when clustering large-scale gene data (microarray) and detecting families in large protein superfamilies. PMID:24705246

Accelerating Large Scale Image Analyses on Parallel, CPU-GPU Equipped Systems

PubMed Central

Teodoro, George; Kurc, Tahsin M.; Pan, Tony; Cooper, Lee A.D.; Kong, Jun; Widener, Patrick; Saltz, Joel H.

2014-01-01

The past decade has witnessed a major paradigm shift in high performance computing with the introduction of accelerators as general purpose processors. These computing devices make available very high parallel computing power at low cost and power consumption, transforming current high performance platforms into heterogeneous CPU-GPU equipped systems. Although the theoretical performance achieved by these hybrid systems is impressive, taking practical advantage of this computing power remains a very challenging problem. Most applications are still deployed to either GPU or CPU, leaving the other resource under- or un-utilized. In this paper, we propose, implement, and evaluate a performance aware scheduling technique along with optimizations to make efficient collaborative use of CPUs and GPUs on a parallel system. In the context of feature computations in large scale image analysis applications, our evaluations show that intelligently co-scheduling CPUs and GPUs can significantly improve performance over GPU-only or multi-core CPU-only approaches. PMID:25419545

Parallel Simulation of Unsteady Turbulent Flames

NASA Technical Reports Server (NTRS)

Menon, Suresh

1996-01-01

Time-accurate simulation of turbulent flames in high Reynolds number flows is a challenging task since both fluid dynamics and combustion must be modeled accurately. To numerically simulate this phenomenon, very large computer resources (both time and memory) are required. Although current vector supercomputers are capable of providing adequate resources for simulations of this nature, the high cost and their limited availability, makes practical use of such machines less than satisfactory. At the same time, the explicit time integration algorithms used in unsteady flow simulations often possess a very high degree of parallelism, making them very amenable to efficient implementation on large-scale parallel computers. Under these circumstances, distributed memory parallel computers offer an excellent near-term solution for greatly increased computational speed and memory, at a cost that may render the unsteady simulations of the type discussed above more feasible and affordable.This paper discusses the study of unsteady turbulent flames using a simulation algorithm that is capable of retaining high parallel efficiency on distributed memory parallel architectures. Numerical studies are carried out using large-eddy simulation (LES). In LES, the scales larger than the grid are computed using a time- and space-accurate scheme, while the unresolved small scales are modeled using eddy viscosity based subgrid models. This is acceptable for the moment/energy closure since the small scales primarily provide a dissipative mechanism for the energy transferred from the large scales. However, for combustion to occur, the species must first undergo mixing at the small scales and then come into molecular contact. Therefore, global models cannot be used. Recently, a new model for turbulent combustion was developed, in which the combustion is modeled, within the subgrid (small-scales) using a methodology that simulates the mixing and the molecular transport and the chemical kinetics within each LES grid cell. Finite-rate kinetics can be included without any closure and this approach actually provides a means to predict the turbulent rates and the turbulent flame speed. The subgrid combustion model requires resolution of the local time scales associated with small-scale mixing, molecular diffusion and chemical kinetics and, therefore, within each grid cell, a significant amount of computations must be carried out before the large-scale (LES resolved) effects are incorporated. Therefore, this approach is uniquely suited for parallel processing and has been implemented on various systems such as: Intel Paragon, IBM SP-2, Cray T3D and SGI Power Challenge (PC) using the system independent Message Passing Interface (MPI) compiler. In this paper, timing data on these machines is reported along with some characteristic results.

Parameter estimation in large-scale systems biology models: a parallel and self-adaptive cooperative strategy.

PubMed

Penas, David R; González, Patricia; Egea, Jose A; Doallo, Ramón; Banga, Julio R

2017-01-21

The development of large-scale kinetic models is one of the current key issues in computational systems biology and bioinformatics. Here we consider the problem of parameter estimation in nonlinear dynamic models. Global optimization methods can be used to solve this type of problems but the associated computational cost is very large. Moreover, many of these methods need the tuning of a number of adjustable search parameters, requiring a number of initial exploratory runs and therefore further increasing the computation times. Here we present a novel parallel method, self-adaptive cooperative enhanced scatter search (saCeSS), to accelerate the solution of this class of problems. The method is based on the scatter search optimization metaheuristic and incorporates several key new mechanisms: (i) asynchronous cooperation between parallel processes, (ii) coarse and fine-grained parallelism, and (iii) self-tuning strategies. The performance and robustness of saCeSS is illustrated by solving a set of challenging parameter estimation problems, including medium and large-scale kinetic models of the bacterium E. coli, bakerés yeast S. cerevisiae, the vinegar fly D. melanogaster, Chinese Hamster Ovary cells, and a generic signal transduction network. The results consistently show that saCeSS is a robust and efficient method, allowing very significant reduction of computation times with respect to several previous state of the art methods (from days to minutes, in several cases) even when only a small number of processors is used. The new parallel cooperative method presented here allows the solution of medium and large scale parameter estimation problems in reasonable computation times and with small hardware requirements. Further, the method includes self-tuning mechanisms which facilitate its use by non-experts. We believe that this new method can play a key role in the development of large-scale and even whole-cell dynamic models.

Experiences with hypercube operating system instrumentation

NASA Technical Reports Server (NTRS)

Reed, Daniel A.; Rudolph, David C.

1989-01-01

The difficulties in conceptualizing the interactions among a large number of processors make it difficult both to identify the sources of inefficiencies and to determine how a parallel program could be made more efficient. This paper describes an instrumentation system that can trace the execution of distributed memory parallel programs by recording the occurrence of parallel program events. The resulting event traces can be used to compile summary statistics that provide a global view of program performance. In addition, visualization tools permit the graphic display of event traces. Visual presentation of performance data is particularly useful, indeed, necessary for large-scale parallel computers; the enormous volume of performance data mandates visual display.

A hybrid parallel framework for the cellular Potts model simulations

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

Jiang, Yi; He, Kejing; Dong, Shoubin

2009-01-01

The Cellular Potts Model (CPM) has been widely used for biological simulations. However, most current implementations are either sequential or approximated, which can't be used for large scale complex 3D simulation. In this paper we present a hybrid parallel framework for CPM simulations. The time-consuming POE solving, cell division, and cell reaction operation are distributed to clusters using the Message Passing Interface (MPI). The Monte Carlo lattice update is parallelized on shared-memory SMP system using OpenMP. Because the Monte Carlo lattice update is much faster than the POE solving and SMP systems are more and more common, this hybrid approachmore » achieves good performance and high accuracy at the same time. Based on the parallel Cellular Potts Model, we studied the avascular tumor growth using a multiscale model. The application and performance analysis show that the hybrid parallel framework is quite efficient. The hybrid parallel CPM can be used for the large scale simulation ({approx}10{sup 8} sites) of complex collective behavior of numerous cells ({approx}10{sup 6}).« less

A transient FETI methodology for large-scale parallel implicit computations in structural mechanics

NASA Technical Reports Server (NTRS)

Farhat, Charbel; Crivelli, Luis; Roux, Francois-Xavier

1992-01-01

Explicit codes are often used to simulate the nonlinear dynamics of large-scale structural systems, even for low frequency response, because the storage and CPU requirements entailed by the repeated factorizations traditionally found in implicit codes rapidly overwhelm the available computing resources. With the advent of parallel processing, this trend is accelerating because explicit schemes are also easier to parallelize than implicit ones. However, the time step restriction imposed by the Courant stability condition on all explicit schemes cannot yet -- and perhaps will never -- be offset by the speed of parallel hardware. Therefore, it is essential to develop efficient and robust alternatives to direct methods that are also amenable to massively parallel processing because implicit codes using unconditionally stable time-integration algorithms are computationally more efficient when simulating low-frequency dynamics. Here we present a domain decomposition method for implicit schemes that requires significantly less storage than factorization algorithms, that is several times faster than other popular direct and iterative methods, that can be easily implemented on both shared and local memory parallel processors, and that is both computationally and communication-wise efficient. The proposed transient domain decomposition method is an extension of the method of Finite Element Tearing and Interconnecting (FETI) developed by Farhat and Roux for the solution of static problems. Serial and parallel performance results on the CRAY Y-MP/8 and the iPSC-860/128 systems are reported and analyzed for realistic structural dynamics problems. These results establish the superiority of the FETI method over both the serial/parallel conjugate gradient algorithm with diagonal scaling and the serial/parallel direct method, and contrast the computational power of the iPSC-860/128 parallel processor with that of the CRAY Y-MP/8 system.

A topology visualization early warning distribution algorithm for large-scale network security incidents.

PubMed

He, Hui; Fan, Guotao; Ye, Jianwei; Zhang, Weizhe

2013-01-01

It is of great significance to research the early warning system for large-scale network security incidents. It can improve the network system's emergency response capabilities, alleviate the cyber attacks' damage, and strengthen the system's counterattack ability. A comprehensive early warning system is presented in this paper, which combines active measurement and anomaly detection. The key visualization algorithm and technology of the system are mainly discussed. The large-scale network system's plane visualization is realized based on the divide and conquer thought. First, the topology of the large-scale network is divided into some small-scale networks by the MLkP/CR algorithm. Second, the sub graph plane visualization algorithm is applied to each small-scale network. Finally, the small-scale networks' topologies are combined into a topology based on the automatic distribution algorithm of force analysis. As the algorithm transforms the large-scale network topology plane visualization problem into a series of small-scale network topology plane visualization and distribution problems, it has higher parallelism and is able to handle the display of ultra-large-scale network topology.

A distributed parallel storage architecture and its potential application within EOSDIS

NASA Technical Reports Server (NTRS)

Johnston, William E.; Tierney, Brian; Feuquay, Jay; Butzer, Tony

1994-01-01

We describe the architecture, implementation, use of a scalable, high performance, distributed-parallel data storage system developed in the ARPA funded MAGIC gigabit testbed. A collection of wide area distributed disk servers operate in parallel to provide logical block level access to large data sets. Operated primarily as a network-based cache, the architecture supports cooperation among independently owned resources to provide fast, large-scale, on-demand storage to support data handling, simulation, and computation.

Portable parallel stochastic optimization for the design of aeropropulsion components

NASA Technical Reports Server (NTRS)

Sues, Robert H.; Rhodes, G. S.

1994-01-01

This report presents the results of Phase 1 research to develop a methodology for performing large-scale Multi-disciplinary Stochastic Optimization (MSO) for the design of aerospace systems ranging from aeropropulsion components to complete aircraft configurations. The current research recognizes that such design optimization problems are computationally expensive, and require the use of either massively parallel or multiple-processor computers. The methodology also recognizes that many operational and performance parameters are uncertain, and that uncertainty must be considered explicitly to achieve optimum performance and cost. The objective of this Phase 1 research was to initialize the development of an MSO methodology that is portable to a wide variety of hardware platforms, while achieving efficient, large-scale parallelism when multiple processors are available. The first effort in the project was a literature review of available computer hardware, as well as review of portable, parallel programming environments. The first effort was to implement the MSO methodology for a problem using the portable parallel programming language, Parallel Virtual Machine (PVM). The third and final effort was to demonstrate the example on a variety of computers, including a distributed-memory multiprocessor, a distributed-memory network of workstations, and a single-processor workstation. Results indicate the MSO methodology can be well-applied towards large-scale aerospace design problems. Nearly perfect linear speedup was demonstrated for computation of optimization sensitivity coefficients on both a 128-node distributed-memory multiprocessor (the Intel iPSC/860) and a network of workstations (speedups of almost 19 times achieved for 20 workstations). Very high parallel efficiencies (75 percent for 31 processors and 60 percent for 50 processors) were also achieved for computation of aerodynamic influence coefficients on the Intel. Finally, the multi-level parallelization strategy that will be needed for large-scale MSO problems was demonstrated to be highly efficient. The same parallel code instructions were used on both platforms, demonstrating portability. There are many applications for which MSO can be applied, including NASA's High-Speed-Civil Transport, and advanced propulsion systems. The use of MSO will reduce design and development time and testing costs dramatically.

An Implicit Solver on A Parallel Block-Structured Adaptive Mesh Grid for FLASH

NASA Astrophysics Data System (ADS)

Lee, D.; Gopal, S.; Mohapatra, P.

2012-07-01

We introduce a fully implicit solver for FLASH based on a Jacobian-Free Newton-Krylov (JFNK) approach with an appropriate preconditioner. The main goal of developing this JFNK-type implicit solver is to provide efficient high-order numerical algorithms and methodology for simulating stiff systems of differential equations on large-scale parallel computer architectures. A large number of natural problems in nonlinear physics involve a wide range of spatial and time scales of interest. A system that encompasses such a wide magnitude of scales is described as "stiff." A stiff system can arise in many different fields of physics, including fluid dynamics/aerodynamics, laboratory/space plasma physics, low Mach number flows, reactive flows, radiation hydrodynamics, and geophysical flows. One of the big challenges in solving such a stiff system using current-day computational resources lies in resolving time and length scales varying by several orders of magnitude. We introduce FLASH's preliminary implementation of a time-accurate JFNK-based implicit solver in the framework of FLASH's unsplit hydro solver.

Displacement and deformation measurement for large structures by camera network

NASA Astrophysics Data System (ADS)

Shang, Yang; Yu, Qifeng; Yang, Zhen; Xu, Zhiqiang; Zhang, Xiaohu

2014-03-01

A displacement and deformation measurement method for large structures by a series-parallel connection camera network is presented. By taking the dynamic monitoring of a large-scale crane in lifting operation as an example, a series-parallel connection camera network is designed, and the displacement and deformation measurement method by using this series-parallel connection camera network is studied. The movement range of the crane body is small, and that of the crane arm is large. The displacement of the crane body, the displacement of the crane arm relative to the body and the deformation of the arm are measured. Compared with a pure series or parallel connection camera network, the designed series-parallel connection camera network can be used to measure not only the movement and displacement of a large structure but also the relative movement and deformation of some interesting parts of the large structure by a relatively simple optical measurement system.

Advances in Parallelization for Large Scale Oct-Tree Mesh Generation

NASA Technical Reports Server (NTRS)

O'Connell, Matthew; Karman, Steve L.

2015-01-01

Despite great advancements in the parallelization of numerical simulation codes over the last 20 years, it is still common to perform grid generation in serial. Generating large scale grids in serial often requires using special "grid generation" compute machines that can have more than ten times the memory of average machines. While some parallel mesh generation techniques have been proposed, generating very large meshes for LES or aeroacoustic simulations is still a challenging problem. An automated method for the parallel generation of very large scale off-body hierarchical meshes is presented here. This work enables large scale parallel generation of off-body meshes by using a novel combination of parallel grid generation techniques and a hybrid "top down" and "bottom up" oct-tree method. Meshes are generated using hardware commonly found in parallel compute clusters. The capability to generate very large meshes is demonstrated by the generation of off-body meshes surrounding complex aerospace geometries. Results are shown including a one billion cell mesh generated around a Predator Unmanned Aerial Vehicle geometry, which was generated on 64 processors in under 45 minutes.

DGDFT: A massively parallel method for large scale density functional theory calculations.

PubMed

Hu, Wei; Lin, Lin; Yang, Chao

2015-09-28

We describe a massively parallel implementation of the recently developed discontinuous Galerkin density functional theory (DGDFT) method, for efficient large-scale Kohn-Sham DFT based electronic structure calculations. The DGDFT method uses adaptive local basis (ALB) functions generated on-the-fly during the self-consistent field iteration to represent the solution to the Kohn-Sham equations. The use of the ALB set provides a systematic way to improve the accuracy of the approximation. By using the pole expansion and selected inversion technique to compute electron density, energy, and atomic forces, we can make the computational complexity of DGDFT scale at most quadratically with respect to the number of electrons for both insulating and metallic systems. We show that for the two-dimensional (2D) phosphorene systems studied here, using 37 basis functions per atom allows us to reach an accuracy level of 1.3 × 10(-4) Hartree/atom in terms of the error of energy and 6.2 × 10(-4) Hartree/bohr in terms of the error of atomic force, respectively. DGDFT can achieve 80% parallel efficiency on 128,000 high performance computing cores when it is used to study the electronic structure of 2D phosphorene systems with 3500-14 000 atoms. This high parallel efficiency results from a two-level parallelization scheme that we will describe in detail.

Parallel and serial computing tools for testing single-locus and epistatic SNP effects of quantitative traits in genome-wide association studies

PubMed Central

Ma, Li; Runesha, H Birali; Dvorkin, Daniel; Garbe, John R; Da, Yang

2008-01-01

Background Genome-wide association studies (GWAS) using single nucleotide polymorphism (SNP) markers provide opportunities to detect epistatic SNPs associated with quantitative traits and to detect the exact mode of an epistasis effect. Computational difficulty is the main bottleneck for epistasis testing in large scale GWAS. Results The EPISNPmpi and EPISNP computer programs were developed for testing single-locus and epistatic SNP effects on quantitative traits in GWAS, including tests of three single-locus effects for each SNP (SNP genotypic effect, additive and dominance effects) and five epistasis effects for each pair of SNPs (two-locus interaction, additive × additive, additive × dominance, dominance × additive, and dominance × dominance) based on the extended Kempthorne model. EPISNPmpi is the parallel computing program for epistasis testing in large scale GWAS and achieved excellent scalability for large scale analysis and portability for various parallel computing platforms. EPISNP is the serial computing program based on the EPISNPmpi code for epistasis testing in small scale GWAS using commonly available operating systems and computer hardware. Three serial computing utility programs were developed for graphical viewing of test results and epistasis networks, and for estimating CPU time and disk space requirements. Conclusion The EPISNPmpi parallel computing program provides an effective computing tool for epistasis testing in large scale GWAS, and the epiSNP serial computing programs are convenient tools for epistasis analysis in small scale GWAS using commonly available computer hardware. PMID:18644146

Characterizing parallel file-access patterns on a large-scale multiprocessor

NASA Technical Reports Server (NTRS)

Purakayastha, A.; Ellis, Carla; Kotz, David; Nieuwejaar, Nils; Best, Michael L.

1995-01-01

High-performance parallel file systems are needed to satisfy tremendous I/O requirements of parallel scientific applications. The design of such high-performance parallel file systems depends on a comprehensive understanding of the expected workload, but so far there have been very few usage studies of multiprocessor file systems. This paper is part of the CHARISMA project, which intends to fill this void by measuring real file-system workloads on various production parallel machines. In particular, we present results from the CM-5 at the National Center for Supercomputing Applications. Our results are unique because we collect information about nearly every individual I/O request from the mix of jobs running on the machine. Analysis of the traces leads to various recommendations for parallel file-system design.

Porting LAMMPS to GPUs.

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

Brown, William Michael; Plimpton, Steven James; Wang, Peng

2010-03-01

LAMMPS is a classical molecular dynamics code, and an acronym for Large-scale Atomic/Molecular Massively Parallel Simulator. LAMMPS has potentials for soft materials (biomolecules, polymers) and solid-state materials (metals, semiconductors) and coarse-grained or mesoscopic systems. It can be used to model atoms or, more generically, as a parallel particle simulator at the atomic, meso, or continuum scale. LAMMPS runs on single processors or in parallel using message-passing techniques and a spatial-decomposition of the simulation domain. The code is designed to be easy to modify or extend with new functionality.

Resource Management for Distributed Parallel Systems

NASA Technical Reports Server (NTRS)

Neuman, B. Clifford; Rao, Santosh

1993-01-01

Multiprocessor systems should exist in the the larger context of distributed systems, allowing multiprocessor resources to be shared by those that need them. Unfortunately, typical multiprocessor resource management techniques do not scale to large networks. The Prospero Resource Manager (PRM) is a scalable resource allocation system that supports the allocation of processing resources in large networks and multiprocessor systems. To manage resources in such distributed parallel systems, PRM employs three types of managers: system managers, job managers, and node managers. There exist multiple independent instances of each type of manager, reducing bottlenecks. The complexity of each manager is further reduced because each is designed to utilize information at an appropriate level of abstraction.

A study of the parallel algorithm for large-scale DC simulation of nonlinear systems

NASA Astrophysics Data System (ADS)

Cortés Udave, Diego Ernesto; Ogrodzki, Jan; Gutiérrez de Anda, Miguel Angel

Newton-Raphson DC analysis of large-scale nonlinear circuits may be an extremely time consuming process even if sparse matrix techniques and bypassing of nonlinear models calculation are used. A slight decrease in the time required for this task may be enabled on multi-core, multithread computers if the calculation of the mathematical models for the nonlinear elements as well as the stamp management of the sparse matrix entries are managed through concurrent processes. This numerical complexity can be further reduced via the circuit decomposition and parallel solution of blocks taking as a departure point the BBD matrix structure. This block-parallel approach may give a considerable profit though it is strongly dependent on the system topology and, of course, on the processor type. This contribution presents the easy-parallelizable decomposition-based algorithm for DC simulation and provides a detailed study of its effectiveness.

Research on precision grinding technology of large scale and ultra thin optics

NASA Astrophysics Data System (ADS)

Zhou, Lian; Wei, Qiancai; Li, Jie; Chen, Xianhua; Zhang, Qinghua

2018-03-01

The flatness and parallelism error of large scale and ultra thin optics have an important influence on the subsequent polishing efficiency and accuracy. In order to realize the high precision grinding of those ductile elements, the low deformation vacuum chuck was designed first, which was used for clamping the optics with high supporting rigidity in the full aperture. Then the optics was planar grinded under vacuum adsorption. After machining, the vacuum system was turned off. The form error of optics was on-machine measured using displacement sensor after elastic restitution. The flatness would be convergenced with high accuracy by compensation machining, whose trajectories were integrated with the measurement result. For purpose of getting high parallelism, the optics was turned over and compensation grinded using the form error of vacuum chuck. Finally, the grinding experiment of large scale and ultra thin fused silica optics with aperture of 430mm×430mm×10mm was performed. The best P-V flatness of optics was below 3 μm, and parallelism was below 3 ″. This machining technique has applied in batch grinding of large scale and ultra thin optics.

Parallel Computation of the Regional Ocean Modeling System (ROMS)

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

Wang, P; Song, Y T; Chao, Y

2005-04-05

The Regional Ocean Modeling System (ROMS) is a regional ocean general circulation modeling system solving the free surface, hydrostatic, primitive equations over varying topography. It is free software distributed world-wide for studying both complex coastal ocean problems and the basin-to-global scale ocean circulation. The original ROMS code could only be run on shared-memory systems. With the increasing need to simulate larger model domains with finer resolutions and on a variety of computer platforms, there is a need in the ocean-modeling community to have a ROMS code that can be run on any parallel computer ranging from 10 to hundreds ofmore » processors. Recently, we have explored parallelization for ROMS using the MPI programming model. In this paper, an efficient parallelization strategy for such a large-scale scientific software package, based on an existing shared-memory computing model, is presented. In addition, scientific applications and data-performance issues on a couple of SGI systems, including Columbia, the world's third-fastest supercomputer, are discussed.« less

Multi-thread parallel algorithm for reconstructing 3D large-scale porous structures

NASA Astrophysics Data System (ADS)

Ju, Yang; Huang, Yaohui; Zheng, Jiangtao; Qian, Xu; Xie, Heping; Zhao, Xi

2017-04-01

Geomaterials inherently contain many discontinuous, multi-scale, geometrically irregular pores, forming a complex porous structure that governs their mechanical and transport properties. The development of an efficient reconstruction method for representing porous structures can significantly contribute toward providing a better understanding of the governing effects of porous structures on the properties of porous materials. In order to improve the efficiency of reconstructing large-scale porous structures, a multi-thread parallel scheme was incorporated into the simulated annealing reconstruction method. In the method, four correlation functions, which include the two-point probability function, the linear-path functions for the pore phase and the solid phase, and the fractal system function for the solid phase, were employed for better reproduction of the complex well-connected porous structures. In addition, a random sphere packing method and a self-developed pre-conditioning method were incorporated to cast the initial reconstructed model and select independent interchanging pairs for parallel multi-thread calculation, respectively. The accuracy of the proposed algorithm was evaluated by examining the similarity between the reconstructed structure and a prototype in terms of their geometrical, topological, and mechanical properties. Comparisons of the reconstruction efficiency of porous models with various scales indicated that the parallel multi-thread scheme significantly shortened the execution time for reconstruction of a large-scale well-connected porous model compared to a sequential single-thread procedure.

Implementation of highly parallel and large scale GW calculations within the OpenAtom software

NASA Astrophysics Data System (ADS)

Ismail-Beigi, Sohrab

The need to describe electronic excitations with better accuracy than provided by band structures produced by Density Functional Theory (DFT) has been a long-term enterprise for the computational condensed matter and materials theory communities. In some cases, appropriate theoretical frameworks have existed for some time but have been difficult to apply widely due to computational cost. For example, the GW approximation incorporates a great deal of important non-local and dynamical electronic interaction effects but has been too computationally expensive for routine use in large materials simulations. OpenAtom is an open source massively parallel ab initiodensity functional software package based on plane waves and pseudopotentials (http://charm.cs.uiuc.edu/OpenAtom/) that takes advantage of the Charm + + parallel framework. At present, it is developed via a three-way collaboration, funded by an NSF SI2-SSI grant (ACI-1339804), between Yale (Ismail-Beigi), IBM T. J. Watson (Glenn Martyna) and the University of Illinois at Urbana Champaign (Laxmikant Kale). We will describe the project and our current approach towards implementing large scale GW calculations with OpenAtom. Potential applications of large scale parallel GW software for problems involving electronic excitations in semiconductor and/or metal oxide systems will be also be pointed out.

Hybrid Parallelism for Volume Rendering on Large-, Multi-, and Many-Core Systems

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

Howison, Mark; Bethel, E. Wes; Childs, Hank

2012-01-01

With the computing industry trending towards multi- and many-core processors, we study how a standard visualization algorithm, ray-casting volume rendering, can benefit from a hybrid parallelism approach. Hybrid parallelism provides the best of both worlds: using distributed-memory parallelism across a large numbers of nodes increases available FLOPs and memory, while exploiting shared-memory parallelism among the cores within each node ensures that each node performs its portion of the larger calculation as efficiently as possible. We demonstrate results from weak and strong scaling studies, at levels of concurrency ranging up to 216,000, and with datasets as large as 12.2 trillion cells.more » The greatest benefit from hybrid parallelism lies in the communication portion of the algorithm, the dominant cost at higher levels of concurrency. We show that reducing the number of participants with a hybrid approach significantly improves performance.« less

Argonne Simulation Framework for Intelligent Transportation Systems

DOT National Transportation Integrated Search

1996-01-01

A simulation framework has been developed which defines a high-level architecture for a large-scale, comprehensive, scalable simulation of an Intelligent Transportation System (ITS). The simulator is designed to run on parallel computers and distribu...

Profiling and Improving I/O Performance of a Large-Scale Climate Scientific Application

NASA Technical Reports Server (NTRS)

Liu, Zhuo; Wang, Bin; Wang, Teng; Tian, Yuan; Xu, Cong; Wang, Yandong; Yu, Weikuan; Cruz, Carlos A.; Zhou, Shujia; Clune, Tom;

A Multi-Time Scale Morphable Software Milieu for Polymorphous Computing Architectures (PCA) - Composable, Scalable Systems

DTIC Science & Technology

2004-10-01

MONITORING AGENCY NAME(S) AND ADDRESS(ES) Defense Advanced Research Projects Agency AFRL/IFTC 3701 North Fairfax Drive...Scalable Parallel Libraries for Large-Scale Concurrent Applications," Technical Report UCRL -JC-109251, Lawrence Livermore National Laboratory

The NAS parallel benchmarks

NASA Technical Reports Server (NTRS)

Bailey, David (Editor); Barton, John (Editor); Lasinski, Thomas (Editor); Simon, Horst (Editor)

1993-01-01

A new set of benchmarks was developed for the performance evaluation of highly parallel supercomputers. These benchmarks consist of a set of kernels, the 'Parallel Kernels,' and a simulated application benchmark. Together they mimic the computation and data movement characteristics of large scale computational fluid dynamics (CFD) applications. The principal distinguishing feature of these benchmarks is their 'pencil and paper' specification - all details of these benchmarks are specified only algorithmically. In this way many of the difficulties associated with conventional benchmarking approaches on highly parallel systems are avoided.

Development and Applications of a Modular Parallel Process for Large Scale Fluid/Structures Problems

NASA Technical Reports Server (NTRS)

Guruswamy, Guru P.; Kwak, Dochan (Technical Monitor)

2002-01-01

A modular process that can efficiently solve large scale multidisciplinary problems using massively parallel supercomputers is presented. The process integrates disciplines with diverse physical characteristics by retaining the efficiency of individual disciplines. Computational domain independence of individual disciplines is maintained using a meta programming approach. The process integrates disciplines without affecting the combined performance. Results are demonstrated for large scale aerospace problems on several supercomputers. The super scalability and portability of the approach is demonstrated on several parallel computers.

Development and Applications of a Modular Parallel Process for Large Scale Fluid/Structures Problems

NASA Technical Reports Server (NTRS)

Guruswamy, Guru P.; Byun, Chansup; Kwak, Dochan (Technical Monitor)

2001-01-01

A modular process that can efficiently solve large scale multidisciplinary problems using massively parallel super computers is presented. The process integrates disciplines with diverse physical characteristics by retaining the efficiency of individual disciplines. Computational domain independence of individual disciplines is maintained using a meta programming approach. The process integrates disciplines without affecting the combined performance. Results are demonstrated for large scale aerospace problems on several supercomputers. The super scalability and portability of the approach is demonstrated on several parallel computers.

Using Agent Base Models to Optimize Large Scale Network for Large System Inventories

NASA Technical Reports Server (NTRS)

Shameldin, Ramez Ahmed; Bowling, Shannon R.

2010-01-01

The aim of this paper is to use Agent Base Models (ABM) to optimize large scale network handling capabilities for large system inventories and to implement strategies for the purpose of reducing capital expenses. The models used in this paper either use computational algorithms or procedure implementations developed by Matlab to simulate agent based models in a principal programming language and mathematical theory using clusters, these clusters work as a high performance computational performance to run the program in parallel computational. In both cases, a model is defined as compilation of a set of structures and processes assumed to underlie the behavior of a network system.

Large Scale GW Calculations on the Cori System

NASA Astrophysics Data System (ADS)

Deslippe, Jack; Del Ben, Mauro; da Jornada, Felipe; Canning, Andrew; Louie, Steven

The NERSC Cori system, powered by 9000+ Intel Xeon-Phi processors, represents one of the largest HPC systems for open-science in the United States and the world. We discuss the optimization of the GW methodology for this system, including both node level and system-scale optimizations. We highlight multiple large scale (thousands of atoms) case studies and discuss both absolute application performance and comparison to calculations on more traditional HPC architectures. We find that the GW method is particularly well suited for many-core architectures due to the ability to exploit a large amount of parallelism across many layers of the system. This work was supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division, as part of the Computational Materials Sciences Program.

Parallel Mutual Information Based Construction of Genome-Scale Networks on the Intel® Xeon Phi™ Coprocessor.

PubMed

Misra, Sanchit; Pamnany, Kiran; Aluru, Srinivas

2015-01-01

Construction of whole-genome networks from large-scale gene expression data is an important problem in systems biology. While several techniques have been developed, most cannot handle network reconstruction at the whole-genome scale, and the few that can, require large clusters. In this paper, we present a solution on the Intel Xeon Phi coprocessor, taking advantage of its multi-level parallelism including many x86-based cores, multiple threads per core, and vector processing units. We also present a solution on the Intel® Xeon® processor. Our solution is based on TINGe, a fast parallel network reconstruction technique that uses mutual information and permutation testing for assessing statistical significance. We demonstrate the first ever inference of a plant whole genome regulatory network on a single chip by constructing a 15,575 gene network of the plant Arabidopsis thaliana from 3,137 microarray experiments in only 22 minutes. In addition, our optimization for parallelizing mutual information computation on the Intel Xeon Phi coprocessor holds out lessons that are applicable to other domains.

Parallel Visualization Co-Processing of Overnight CFD Propulsion Applications

NASA Technical Reports Server (NTRS)

Edwards, David E.; Haimes, Robert

1999-01-01

An interactive visualization system pV3 is being developed for the investigation of advanced computational methodologies employing visualization and parallel processing for the extraction of information contained in large-scale transient engineering simulations. Visual techniques for extracting information from the data in terms of cutting planes, iso-surfaces, particle tracing and vector fields are included in this system. This paper discusses improvements to the pV3 system developed under NASA's Affordable High Performance Computing project.

Implementation of Shifted Periodic Boundary Conditions in the Large-Scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) Software

DTIC Science & Technology

2015-08-01

Atomic/Molecular Massively Parallel Simulator ( LAMMPS ) Software by N Scott Weingarten and James P Larentzos Approved for...Massively Parallel Simulator ( LAMMPS ) Software by N Scott Weingarten Weapons and Materials Research Directorate, ARL James P Larentzos Engility...Shifted Periodic Boundary Conditions in the Large-Scale Atomic/Molecular Massively Parallel Simulator ( LAMMPS ) Software 5a. CONTRACT NUMBER 5b

PLASMA TURBULENCE AND KINETIC INSTABILITIES AT ION SCALES IN THE EXPANDING SOLAR WIND

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

Hellinger, Petr; Trávnícek, Pavel M.; Matteini, Lorenzo

The relationship between a decaying strong turbulence and kinetic instabilities in a slowly expanding plasma is investigated using two-dimensional (2D) hybrid expanding box simulations. We impose an initial ambient magnetic field perpendicular to the simulation box, and we start with a spectrum of large-scale, linearly polarized, random-phase Alfvénic fluctuations that have energy equipartition between kinetic and magnetic fluctuations and vanishing correlation between the two fields. A turbulent cascade rapidly develops; magnetic field fluctuations exhibit a power-law spectrum at large scales and a steeper spectrum at ion scales. The turbulent cascade leads to an overall anisotropic proton heating, protons are heatedmore » in the perpendicular direction, and, initially, also in the parallel direction. The imposed expansion leads to generation of a large parallel proton temperature anisotropy which is at later stages partly reduced by turbulence. The turbulent heating is not sufficient to overcome the expansion-driven perpendicular cooling and the system eventually drives the oblique firehose instability in a form of localized nonlinear wave packets which efficiently reduce the parallel temperature anisotropy. This work demonstrates that kinetic instabilities may coexist with strong plasma turbulence even in a constrained 2D regime.« less

Exploiting multi-scale parallelism for large scale numerical modelling of laser wakefield accelerators

NASA Astrophysics Data System (ADS)

Fonseca, R. A.; Vieira, J.; Fiuza, F.; Davidson, A.; Tsung, F. S.; Mori, W. B.; Silva, L. O.

2013-12-01

A new generation of laser wakefield accelerators (LWFA), supported by the extreme accelerating fields generated in the interaction of PW-Class lasers and underdense targets, promises the production of high quality electron beams in short distances for multiple applications. Achieving this goal will rely heavily on numerical modelling to further understand the underlying physics and identify optimal regimes, but large scale modelling of these scenarios is computationally heavy and requires the efficient use of state-of-the-art petascale supercomputing systems. We discuss the main difficulties involved in running these simulations and the new developments implemented in the OSIRIS framework to address these issues, ranging from multi-dimensional dynamic load balancing and hybrid distributed/shared memory parallelism to the vectorization of the PIC algorithm. We present the results of the OASCR Joule Metric program on the issue of large scale modelling of LWFA, demonstrating speedups of over 1 order of magnitude on the same hardware. Finally, scalability to over ˜106 cores and sustained performance over ˜2 P Flops is demonstrated, opening the way for large scale modelling of LWFA scenarios.

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

Gamblin, T; de Supinski, B R; Schulz, M

Good load balance is crucial on very large parallel systems, but the most sophisticated algorithms introduce dynamic imbalances through adaptation in domain decomposition or use of adaptive solvers. To observe and diagnose imbalance, developers need system-wide, temporally-ordered measurements from full-scale runs. This potentially requires data collection from multiple code regions on all processors over the entire execution. Doing this instrumentation naively can, in combination with the application itself, exceed available I/O bandwidth and storage capacity, and can induce severe behavioral perturbations. We present and evaluate a novel technique for scalable, low-error load balance measurement. This uses a parallel wavelet transformmore » and other parallel encoding methods. We show that our technique collects and reconstructs system-wide measurements with low error. Compression time scales sublinearly with system size and data volume is several orders of magnitude smaller than the raw data. The overhead is low enough for online use in a production environment.« less

Processing large remote sensing image data sets on Beowulf clusters

USGS Publications Warehouse

Steinwand, Daniel R.; Maddox, Brian; Beckmann, Tim; Schmidt, Gail

2003-01-01

High-performance computing is often concerned with the speed at which floating- point calculations can be performed. The architectures of many parallel computers and/or their network topologies are based on these investigations. Often, benchmarks resulting from these investigations are compiled with little regard to how a large dataset would move about in these systems. This part of the Beowulf study addresses that concern by looking at specific applications software and system-level modifications. Applications include an implementation of a smoothing filter for time-series data, a parallel implementation of the decision tree algorithm used in the Landcover Characterization project, a parallel Kriging algorithm used to fit point data collected in the field on invasive species to a regular grid, and modifications to the Beowulf project's resampling algorithm to handle larger, higher resolution datasets at a national scale. Systems-level investigations include a feasibility study on Flat Neighborhood Networks and modifications of that concept with Parallel File Systems.

Astrophysical N-body Simulations Using Hierarchical Tree Data Structures

NASA Astrophysics Data System (ADS)

Warren, M. S.; Salmon, J. K.

The authors report on recent large astrophysical N-body simulations executed on the Intel Touchstone Delta system. They review the astrophysical motivation and the numerical techniques and discuss steps taken to parallelize these simulations. The methods scale as O(N log N), for large values of N, and also scale linearly with the number of processors. The performance sustained for a duration of 67 h, was between 5.1 and 5.4 Gflop/s on a 512-processor system.

A parallel orbital-updating based plane-wave basis method for electronic structure calculations

NASA Astrophysics Data System (ADS)

Pan, Yan; Dai, Xiaoying; de Gironcoli, Stefano; Gong, Xin-Gao; Rignanese, Gian-Marco; Zhou, Aihui

2017-11-01

Motivated by the recently proposed parallel orbital-updating approach in real space method [1], we propose a parallel orbital-updating based plane-wave basis method for electronic structure calculations, for solving the corresponding eigenvalue problems. In addition, we propose two new modified parallel orbital-updating methods. Compared to the traditional plane-wave methods, our methods allow for two-level parallelization, which is particularly interesting for large scale parallelization. Numerical experiments show that these new methods are more reliable and efficient for large scale calculations on modern supercomputers.

A unifying framework for systems modeling, control systems design, and system operation

NASA Technical Reports Server (NTRS)

Dvorak, Daniel L.; Indictor, Mark B.; Ingham, Michel D.; Rasmussen, Robert D.; Stringfellow, Margaret V.

2005-01-01

Current engineering practice in the analysis and design of large-scale multi-disciplinary control systems is typified by some form of decomposition- whether functional or physical or discipline-based-that enables multiple teams to work in parallel and in relative isolation. Too often, the resulting system after integration is an awkward marriage of different control and data mechanisms with poor end-to-end accountability. System of systems engineering, which faces this problem on a large scale, cries out for a unifying framework to guide analysis, design, and operation. This paper describes such a framework based on a state-, model-, and goal-based architecture for semi-autonomous control systems that guides analysis and modeling, shapes control system software design, and directly specifies operational intent. This paper illustrates the key concepts in the context of a large-scale, concurrent, globally distributed system of systems: NASA's proposed Array-based Deep Space Network.

Neuromorphic Hardware Architecture Using the Neural Engineering Framework for Pattern Recognition.

PubMed

Wang, Runchun; Thakur, Chetan Singh; Cohen, Gregory; Hamilton, Tara Julia; Tapson, Jonathan; van Schaik, Andre

2017-06-01

We present a hardware architecture that uses the neural engineering framework (NEF) to implement large-scale neural networks on field programmable gate arrays (FPGAs) for performing massively parallel real-time pattern recognition. NEF is a framework that is capable of synthesising large-scale cognitive systems from subnetworks and we have previously presented an FPGA implementation of the NEF that successfully performs nonlinear mathematical computations. That work was developed based on a compact digital neural core, which consists of 64 neurons that are instantiated by a single physical neuron using a time-multiplexing approach. We have now scaled this approach up to build a pattern recognition system by combining identical neural cores together. As a proof of concept, we have developed a handwritten digit recognition system using the MNIST database and achieved a recognition rate of 96.55%. The system is implemented on a state-of-the-art FPGA and can process 5.12 million digits per second. The architecture and hardware optimisations presented offer high-speed and resource-efficient means for performing high-speed, neuromorphic, and massively parallel pattern recognition and classification tasks.

The NAS parallel benchmarks

NASA Technical Reports Server (NTRS)

Bailey, D. H.; Barszcz, E.; Barton, J. T.; Carter, R. L.; Lasinski, T. A.; Browning, D. S.; Dagum, L.; Fatoohi, R. A.; Frederickson, P. O.; Schreiber, R. S.

1991-01-01

A new set of benchmarks has been developed for the performance evaluation of highly parallel supercomputers in the framework of the NASA Ames Numerical Aerodynamic Simulation (NAS) Program. These consist of five 'parallel kernel' benchmarks and three 'simulated application' benchmarks. Together they mimic the computation and data movement characteristics of large-scale computational fluid dynamics applications. The principal distinguishing feature of these benchmarks is their 'pencil and paper' specification-all details of these benchmarks are specified only algorithmically. In this way many of the difficulties associated with conventional benchmarking approaches on highly parallel systems are avoided.

Regional-scale calculation of the LS factor using parallel processing

NASA Astrophysics Data System (ADS)

Liu, Kai; Tang, Guoan; Jiang, Ling; Zhu, A.-Xing; Yang, Jianyi; Song, Xiaodong

2015-05-01

With the increase of data resolution and the increasing application of USLE over large areas, the existing serial implementation of algorithms for computing the LS factor is becoming a bottleneck. In this paper, a parallel processing model based on message passing interface (MPI) is presented for the calculation of the LS factor, so that massive datasets at a regional scale can be processed efficiently. The parallel model contains algorithms for calculating flow direction, flow accumulation, drainage network, slope, slope length and the LS factor. According to the existence of data dependence, the algorithms are divided into local algorithms and global algorithms. Parallel strategy are designed according to the algorithm characters including the decomposition method for maintaining the integrity of the results, optimized workflow for reducing the time taken for exporting the unnecessary intermediate data and a buffer-communication-computation strategy for improving the communication efficiency. Experiments on a multi-node system show that the proposed parallel model allows efficient calculation of the LS factor at a regional scale with a massive dataset.

Portable parallel portfolio optimization in the Aurora Financial Management System

NASA Astrophysics Data System (ADS)

Laure, Erwin; Moritsch, Hans

2001-07-01

Financial planning problems are formulated as large scale, stochastic, multiperiod, tree structured optimization problems. An efficient technique for solving this kind of problems is the nested Benders decomposition method. In this paper we present a parallel, portable, asynchronous implementation of this technique. To achieve our portability goals we elected the programming language Java for our implementation and used a high level Java based framework, called OpusJava, for expressing the parallelism potential as well as synchronization constraints. Our implementation is embedded within a modular decision support tool for portfolio and asset liability management, the Aurora Financial Management System.

MLP: A Parallel Programming Alternative to MPI for New Shared Memory Parallel Systems

NASA Technical Reports Server (NTRS)

Taft, James R.

1999-01-01

Recent developments at the NASA AMES Research Center's NAS Division have demonstrated that the new generation of NUMA based Symmetric Multi-Processing systems (SMPs), such as the Silicon Graphics Origin 2000, can successfully execute legacy vector oriented CFD production codes at sustained rates far exceeding processing rates possible on dedicated 16 CPU Cray C90 systems. This high level of performance is achieved via shared memory based Multi-Level Parallelism (MLP). This programming approach, developed at NAS and outlined below, is distinct from the message passing paradigm of MPI. It offers parallelism at both the fine and coarse grained level, with communication latencies that are approximately 50-100 times lower than typical MPI implementations on the same platform. Such latency reductions offer the promise of performance scaling to very large CPU counts. The method draws on, but is also distinct from, the newly defined OpenMP specification, which uses compiler directives to support a limited subset of multi-level parallel operations. The NAS MLP method is general, and applicable to a large class of NASA CFD codes.

Parallel computing for probabilistic fatigue analysis

NASA Technical Reports Server (NTRS)

Sues, Robert H.; Lua, Yuan J.; Smith, Mark D.

1993-01-01

This paper presents the results of Phase I research to investigate the most effective parallel processing software strategies and hardware configurations for probabilistic structural analysis. We investigate the efficiency of both shared and distributed-memory architectures via a probabilistic fatigue life analysis problem. We also present a parallel programming approach, the virtual shared-memory paradigm, that is applicable across both types of hardware. Using this approach, problems can be solved on a variety of parallel configurations, including networks of single or multiprocessor workstations. We conclude that it is possible to effectively parallelize probabilistic fatigue analysis codes; however, special strategies will be needed to achieve large-scale parallelism to keep large number of processors busy and to treat problems with the large memory requirements encountered in practice. We also conclude that distributed-memory architecture is preferable to shared-memory for achieving large scale parallelism; however, in the future, the currently emerging hybrid-memory architectures will likely be optimal.

Symposium on Parallel Computational Methods for Large-scale Structural Analysis and Design, 2nd, Norfolk, VA, US

NASA Technical Reports Server (NTRS)

Storaasli, Olaf O. (Editor); Housner, Jerrold M. (Editor)

1993-01-01

Computing speed is leaping forward by several orders of magnitude each decade. Engineers and scientists gathered at a NASA Langley symposium to discuss these exciting trends as they apply to parallel computational methods for large-scale structural analysis and design. Among the topics discussed were: large-scale static analysis; dynamic, transient, and thermal analysis; domain decomposition (substructuring); and nonlinear and numerical methods.

Simulating neural systems with Xyce.

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

Schiek, Richard Louis; Thornquist, Heidi K.; Mei, Ting

2012-12-01

Sandias parallel circuit simulator, Xyce, can address large scale neuron simulations in a new way extending the range within which one can perform high-fidelity, multi-compartment neuron simulations. This report documents the implementation of neuron devices in Xyce, their use in simulation and analysis of neuron systems.

Extreme-Scale Bayesian Inference for Uncertainty Quantification of Complex Simulations

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

Biros, George

Uncertainty quantification (UQ)—that is, quantifying uncertainties in complex mathematical models and their large-scale computational implementations—is widely viewed as one of the outstanding challenges facing the field of CS&E over the coming decade. The EUREKA project set to address the most difficult class of UQ problems: those for which both the underlying PDE model as well as the uncertain parameters are of extreme scale. In the project we worked on these extreme-scale challenges in the following four areas: 1. Scalable parallel algorithms for sampling and characterizing the posterior distribution that exploit the structure of the underlying PDEs and parameter-to-observable map. Thesemore » include structure-exploiting versions of the randomized maximum likelihood method, which aims to overcome the intractability of employing conventional MCMC methods for solving extreme-scale Bayesian inversion problems by appealing to and adapting ideas from large-scale PDE-constrained optimization, which have been very successful at exploring high-dimensional spaces. 2. Scalable parallel algorithms for construction of prior and likelihood functions based on learning methods and non-parametric density estimation. Constructing problem-specific priors remains a critical challenge in Bayesian inference, and more so in high dimensions. Another challenge is construction of likelihood functions that capture unmodeled couplings between observations and parameters. We will create parallel algorithms for non-parametric density estimation using high dimensional N-body methods and combine them with supervised learning techniques for the construction of priors and likelihood functions. 3. Bayesian inadequacy models, which augment physics models with stochastic models that represent their imperfections. The success of the Bayesian inference framework depends on the ability to represent the uncertainty due to imperfections of the mathematical model of the phenomena of interest. This is a central challenge in UQ, especially for large-scale models. We propose to develop the mathematical tools to address these challenges in the context of extreme-scale problems. 4. Parallel scalable algorithms for Bayesian optimal experimental design (OED). Bayesian inversion yields quantified uncertainties in the model parameters, which can be propagated forward through the model to yield uncertainty in outputs of interest. This opens the way for designing new experiments to reduce the uncertainties in the model parameters and model predictions. Such experimental design problems have been intractable for large-scale problems using conventional methods; we will create OED algorithms that exploit the structure of the PDE model and the parameter-to-output map to overcome these challenges. Parallel algorithms for these four problems were created, analyzed, prototyped, implemented, tuned, and scaled up for leading-edge supercomputers, including UT-Austin’s own 10 petaflops Stampede system, ANL’s Mira system, and ORNL’s Titan system. While our focus is on fundamental mathematical/computational methods and algorithms, we will assess our methods on model problems derived from several DOE mission applications, including multiscale mechanics and ice sheet dynamics.« less

Parallel block schemes for large scale least squares computations

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

Golub, G.H.; Plemmons, R.J.; Sameh, A.

1986-04-01

Large scale least squares computations arise in a variety of scientific and engineering problems, including geodetic adjustments and surveys, medical image analysis, molecular structures, partial differential equations and substructuring methods in structural engineering. In each of these problems, matrices often arise which possess a block structure which reflects the local connection nature of the underlying physical problem. For example, such super-large nonlinear least squares computations arise in geodesy. Here the coordinates of positions are calculated by iteratively solving overdetermined systems of nonlinear equations by the Gauss-Newton method. The US National Geodetic Survey will complete this year (1986) the readjustment ofmore » the North American Datum, a problem which involves over 540 thousand unknowns and over 6.5 million observations (equations). The observation matrix for these least squares computations has a block angular form with 161 diagnonal blocks, each containing 3 to 4 thousand unknowns. In this paper parallel schemes are suggested for the orthogonal factorization of matrices in block angular form and for the associated backsubstitution phase of the least squares computations. In addition, a parallel scheme for the calculation of certain elements of the covariance matrix for such problems is described. It is shown that these algorithms are ideally suited for multiprocessors with three levels of parallelism such as the Cedar system at the University of Illinois. 20 refs., 7 figs.« less

Parallel Optimization of Polynomials for Large-scale Problems in Stability and Control

NASA Astrophysics Data System (ADS)

Kamyar, Reza

In this thesis, we focus on some of the NP-hard problems in control theory. Thanks to the converse Lyapunov theory, these problems can often be modeled as optimization over polynomials. To avoid the problem of intractability, we establish a trade off between accuracy and complexity. In particular, we develop a sequence of tractable optimization problems --- in the form of Linear Programs (LPs) and/or Semi-Definite Programs (SDPs) --- whose solutions converge to the exact solution of the NP-hard problem. However, the computational and memory complexity of these LPs and SDPs grow exponentially with the progress of the sequence - meaning that improving the accuracy of the solutions requires solving SDPs with tens of thousands of decision variables and constraints. Setting up and solving such problems is a significant challenge. The existing optimization algorithms and software are only designed to use desktop computers or small cluster computers --- machines which do not have sufficient memory for solving such large SDPs. Moreover, the speed-up of these algorithms does not scale beyond dozens of processors. This in fact is the reason we seek parallel algorithms for setting-up and solving large SDPs on large cluster- and/or super-computers. We propose parallel algorithms for stability analysis of two classes of systems: 1) Linear systems with a large number of uncertain parameters; 2) Nonlinear systems defined by polynomial vector fields. First, we develop a distributed parallel algorithm which applies Polya's and/or Handelman's theorems to some variants of parameter-dependent Lyapunov inequalities with parameters defined over the standard simplex. The result is a sequence of SDPs which possess a block-diagonal structure. We then develop a parallel SDP solver which exploits this structure in order to map the computation, memory and communication to a distributed parallel environment. Numerical tests on a supercomputer demonstrate the ability of the algorithm to efficiently utilize hundreds and potentially thousands of processors, and analyze systems with 100+ dimensional state-space. Furthermore, we extend our algorithms to analyze robust stability over more complicated geometries such as hypercubes and arbitrary convex polytopes. Our algorithms can be readily extended to address a wide variety of problems in control such as Hinfinity synthesis for systems with parametric uncertainty and computing control Lyapunov functions.

Geocomputation over Hybrid Computer Architecture and Systems: Prior Works and On-going Initiatives at UARK

NASA Astrophysics Data System (ADS)

Shi, X.

2015-12-01

As NSF indicated - "Theory and experimentation have for centuries been regarded as two fundamental pillars of science. It is now widely recognized that computational and data-enabled science forms a critical third pillar." Geocomputation is the third pillar of GIScience and geosciences. With the exponential growth of geodata, the challenge of scalable and high performance computing for big data analytics become urgent because many research activities are constrained by the inability of software or tool that even could not complete the computation process. Heterogeneous geodata integration and analytics obviously magnify the complexity and operational time frame. Many large-scale geospatial problems may be not processable at all if the computer system does not have sufficient memory or computational power. Emerging computer architectures, such as Intel's Many Integrated Core (MIC) Architecture and Graphics Processing Unit (GPU), and advanced computing technologies provide promising solutions to employ massive parallelism and hardware resources to achieve scalability and high performance for data intensive computing over large spatiotemporal and social media data. Exploring novel algorithms and deploying the solutions in massively parallel computing environment to achieve the capability for scalable data processing and analytics over large-scale, complex, and heterogeneous geodata with consistent quality and high-performance has been the central theme of our research team in the Department of Geosciences at the University of Arkansas (UARK). New multi-core architectures combined with application accelerators hold the promise to achieve scalability and high performance by exploiting task and data levels of parallelism that are not supported by the conventional computing systems. Such a parallel or distributed computing environment is particularly suitable for large-scale geocomputation over big data as proved by our prior works, while the potential of such advanced infrastructure remains unexplored in this domain. Within this presentation, our prior and on-going initiatives will be summarized to exemplify how we exploit multicore CPUs, GPUs, and MICs, and clusters of CPUs, GPUs and MICs, to accelerate geocomputation in different applications.

Linear-scaling density-functional simulations of charged point defects in Al2O3 using hierarchical sparse matrix algebra.

PubMed

Hine, N D M; Haynes, P D; Mostofi, A A; Payne, M C

2010-09-21

We present calculations of formation energies of defects in an ionic solid (Al(2)O(3)) extrapolated to the dilute limit, corresponding to a simulation cell of infinite size. The large-scale calculations required for this extrapolation are enabled by developments in the approach to parallel sparse matrix algebra operations, which are central to linear-scaling density-functional theory calculations. The computational cost of manipulating sparse matrices, whose sizes are determined by the large number of basis functions present, is greatly improved with this new approach. We present details of the sparse algebra scheme implemented in the ONETEP code using hierarchical sparsity patterns, and demonstrate its use in calculations on a wide range of systems, involving thousands of atoms on hundreds to thousands of parallel processes.

Parallel Dynamics Simulation Using a Krylov-Schwarz Linear Solution Scheme

DOE PAGES

Abhyankar, Shrirang; Constantinescu, Emil M.; Smith, Barry F.; ...

2016-11-07

Fast dynamics simulation of large-scale power systems is a computational challenge because of the need to solve a large set of stiff, nonlinear differential-algebraic equations at every time step. The main bottleneck in dynamic simulations is the solution of a linear system during each nonlinear iteration of Newton’s method. In this paper, we present a parallel Krylov- Schwarz linear solution scheme that uses the Krylov subspacebased iterative linear solver GMRES with an overlapping restricted additive Schwarz preconditioner. As a result, performance tests of the proposed Krylov-Schwarz scheme for several large test cases ranging from 2,000 to 20,000 buses, including amore » real utility network, show good scalability on different computing architectures.« less

Parallel Dynamics Simulation Using a Krylov-Schwarz Linear Solution Scheme

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

Abhyankar, Shrirang; Constantinescu, Emil M.; Smith, Barry F.

Fast dynamics simulation of large-scale power systems is a computational challenge because of the need to solve a large set of stiff, nonlinear differential-algebraic equations at every time step. The main bottleneck in dynamic simulations is the solution of a linear system during each nonlinear iteration of Newton’s method. In this paper, we present a parallel Krylov- Schwarz linear solution scheme that uses the Krylov subspacebased iterative linear solver GMRES with an overlapping restricted additive Schwarz preconditioner. As a result, performance tests of the proposed Krylov-Schwarz scheme for several large test cases ranging from 2,000 to 20,000 buses, including amore » real utility network, show good scalability on different computing architectures.« less

A Stream Tilling Approach to Surface Area Estimation for Large Scale Spatial Data in a Shared Memory System

NASA Astrophysics Data System (ADS)

Liu, Jiping; Kang, Xiaochen; Dong, Chun; Xu, Shenghua

2017-12-01

Surface area estimation is a widely used tool for resource evaluation in the physical world. When processing large scale spatial data, the input/output (I/O) can easily become the bottleneck in parallelizing the algorithm due to the limited physical memory resources and the very slow disk transfer rate. In this paper, we proposed a stream tilling approach to surface area estimation that first decomposed a spatial data set into tiles with topological expansions. With these tiles, the one-to-one mapping relationship between the input and the computing process was broken. Then, we realized a streaming framework towards the scheduling of the I/O processes and computing units. Herein, each computing unit encapsulated a same copy of the estimation algorithm, and multiple asynchronous computing units could work individually in parallel. Finally, the performed experiment demonstrated that our stream tilling estimation can efficiently alleviate the heavy pressures from the I/O-bound work, and the measured speedup after being optimized have greatly outperformed the directly parallel versions in shared memory systems with multi-core processors.

Demonstration of Hadoop-GIS: A Spatial Data Warehousing System Over MapReduce.

PubMed

Aji, Ablimit; Sun, Xiling; Vo, Hoang; Liu, Qioaling; Lee, Rubao; Zhang, Xiaodong; Saltz, Joel; Wang, Fusheng

2013-11-01

The proliferation of GPS-enabled devices, and the rapid improvement of scientific instruments have resulted in massive amounts of spatial data in the last decade. Support of high performance spatial queries on large volumes data has become increasingly important in numerous fields, which requires a scalable and efficient spatial data warehousing solution as existing approaches exhibit scalability limitations and efficiency bottlenecks for large scale spatial applications. In this demonstration, we present Hadoop-GIS - a scalable and high performance spatial query system over MapReduce. Hadoop-GIS provides an efficient spatial query engine to process spatial queries, data and space based partitioning, and query pipelines that parallelize queries implicitly on MapReduce. Hadoop-GIS also provides an expressive, SQL-like spatial query language for workload specification. We will demonstrate how spatial queries are expressed in spatially extended SQL queries, and submitted through a command line/web interface for execution. Parallel to our system demonstration, we explain the system architecture and details on how queries are translated to MapReduce operators, optimized, and executed on Hadoop. In addition, we will showcase how the system can be used to support two representative real world use cases: large scale pathology analytical imaging, and geo-spatial data warehousing.

Synchronization Of Parallel Discrete Event Simulations

NASA Technical Reports Server (NTRS)

Steinman, Jeffrey S.

1992-01-01

Adaptive, parallel, discrete-event-simulation-synchronization algorithm, Breathing Time Buckets, developed in Synchronous Parallel Environment for Emulation and Discrete Event Simulation (SPEEDES) operating system. Algorithm allows parallel simulations to process events optimistically in fluctuating time cycles that naturally adapt while simulation in progress. Combines best of optimistic and conservative synchronization strategies while avoiding major disadvantages. Algorithm processes events optimistically in time cycles adapting while simulation in progress. Well suited for modeling communication networks, for large-scale war games, for simulated flights of aircraft, for simulations of computer equipment, for mathematical modeling, for interactive engineering simulations, and for depictions of flows of information.

Optimistic barrier synchronization

NASA Technical Reports Server (NTRS)

Nicol, David M.

1992-01-01

Barrier synchronization is fundamental operation in parallel computation. In many contexts, at the point a processor enters a barrier it knows that it has already processed all the work required of it prior to synchronization. The alternative case, when a processor cannot enter a barrier with the assurance that it has already performed all the necessary pre-synchronization computation, is treated. The problem arises when the number of pre-sychronization messages to be received by a processor is unkown, for example, in a parallel discrete simulation or any other computation that is largely driven by an unpredictable exchange of messages. We describe an optimistic O(log sup 2 P) barrier algorithm for such problems, study its performance on a large-scale parallel system, and consider extensions to general associative reductions as well as associative parallel prefix computations.

A parallel data management system for large-scale NASA datasets

NASA Technical Reports Server (NTRS)

Srivastava, Jaideep

1993-01-01

The past decade has experienced a phenomenal growth in the amount of data and resultant information generated by NASA's operations and research projects. A key application is the reprocessing problem which has been identified to require data management capabilities beyond those available today (PRAT93). The Intelligent Information Fusion (IIF) system (ROEL91) is an ongoing NASA project which has similar requirements. Deriving our understanding of NASA's future data management needs based on the above, this paper describes an approach to using parallel computer systems (processor and I/O architectures) to develop an efficient parallel database management system to address the needs. Specifically, we propose to investigate issues in low-level record organizations and management, complex query processing, and query compilation and scheduling.

Towards Scalable Deep Learning via I/O Analysis and Optimization

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

Pumma, Sarunya; Si, Min; Feng, Wu-Chun

Deep learning systems have been growing in prominence as a way to automatically characterize objects, trends, and anomalies. Given the importance of deep learning systems, researchers have been investigating techniques to optimize such systems. An area of particular interest has been using large supercomputing systems to quickly generate effective deep learning networks: a phase often referred to as “training” of the deep learning neural network. As we scale existing deep learning frameworks—such as Caffe—on these large supercomputing systems, we notice that the parallelism can help improve the computation tremendously, leaving data I/O as the major bottleneck limiting the overall systemmore » scalability. In this paper, we first present a detailed analysis of the performance bottlenecks of Caffe on large supercomputing systems. Our analysis shows that the I/O subsystem of Caffe—LMDB—relies on memory-mapped I/O to access its database, which can be highly inefficient on large-scale systems because of its interaction with the process scheduling system and the network-based parallel filesystem. Based on this analysis, we then present LMDBIO, our optimized I/O plugin for Caffe that takes into account the data access pattern of Caffe in order to vastly improve I/O performance. Our experimental results show that LMDBIO can improve the overall execution time of Caffe by nearly 20-fold in some cases.« less

Scaling NASA Applications to 1024 CPUs on Origin 3K

NASA Technical Reports Server (NTRS)

Taft, Jim

2002-01-01

The long and highly successful joint SGI-NASA research effort in ever larger SSI systems was to a large degree the result of the successful development of the MLP scalable parallel programming paradigm developed at ARC: 1) MLP scaling in real production codes justified ever larger systems at NAS; 2) MLP scaling on 256p Origin 2000 gave SGl impetus to productize 256p; 3) MLP scaling on 512 gave SGI courage to build 1024p O3K; and 4) History of MLP success resulted in IBM Star Cluster based MLP effort.

CERN data services for LHC computing

NASA Astrophysics Data System (ADS)

Espinal, X.; Bocchi, E.; Chan, B.; Fiorot, A.; Iven, J.; Lo Presti, G.; Lopez, J.; Gonzalez, H.; Lamanna, M.; Mascetti, L.; Moscicki, J.; Pace, A.; Peters, A.; Ponce, S.; Rousseau, H.; van der Ster, D.

2017-10-01

Dependability, resilience, adaptability and efficiency. Growing requirements require tailoring storage services and novel solutions. Unprecedented volumes of data coming from the broad number of experiments at CERN need to be quickly available in a highly scalable way for large-scale processing and data distribution while in parallel they are routed to tape for long-term archival. These activities are critical for the success of HEP experiments. Nowadays we operate at high incoming throughput (14GB/s during 2015 LHC Pb-Pb run and 11PB in July 2016) and with concurrent complex production work-loads. In parallel our systems provide the platform for the continuous user and experiment driven work-loads for large-scale data analysis, including end-user access and sharing. The storage services at CERN cover the needs of our community: EOS and CASTOR as a large-scale storage; CERNBox for end-user access and sharing; Ceph as data back-end for the CERN OpenStack infrastructure, NFS services and S3 functionality; AFS for legacy distributed-file-system services. In this paper we will summarise the experience in supporting LHC experiments and the transition of our infrastructure from static monolithic systems to flexible components providing a more coherent environment with pluggable protocols, tuneable QoS, sharing capabilities and fine grained ACLs management while continuing to guarantee dependable and robust services.

Secure web-based invocation of large-scale plasma simulation codes

NASA Astrophysics Data System (ADS)

Dimitrov, D. A.; Busby, R.; Exby, J.; Bruhwiler, D. L.; Cary, J. R.

2004-12-01

We present our design and initial implementation of a web-based system for running, both in parallel and serial, Particle-In-Cell (PIC) codes for plasma simulations with automatic post processing and generation of visual diagnostics.

Scaling Semantic Graph Databases in Size and Performance

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

Morari, Alessandro; Castellana, Vito G.; Villa, Oreste

In this paper we present SGEM, a full software system for accelerating large-scale semantic graph databases on commodity clusters. Unlike current approaches, SGEM addresses semantic graph databases by only employing graph methods at all the levels of the stack. On one hand, this allows exploiting the space efficiency of graph data structures and the inherent parallelism of graph algorithms. These features adapt well to the increasing system memory and core counts of modern commodity clusters. On the other hand, however, these systems are optimized for regular computation and batched data transfers, while graph methods usually are irregular and generate fine-grainedmore » data accesses with poor spatial and temporal locality. Our framework comprises a SPARQL to data parallel C compiler, a library of parallel graph methods and a custom, multithreaded runtime system. We introduce our stack, motivate its advantages with respect to other solutions and show how we solved the challenges posed by irregular behaviors. We present the result of our software stack on the Berlin SPARQL benchmarks with datasets up to 10 billion triples (a triple corresponds to a graph edge), demonstrating scaling in dataset size and in performance as more nodes are added to the cluster.« less

Rethinking key–value store for parallel I/O optimization

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

Kougkas, Anthony; Eslami, Hassan; Sun, Xian-He

2015-01-26

Key-value stores are being widely used as the storage system for large-scale internet services and cloud storage systems. However, they are rarely used in HPC systems, where parallel file systems are the dominant storage solution. In this study, we examine the architecture differences and performance characteristics of parallel file systems and key-value stores. We propose using key-value stores to optimize overall Input/Output (I/O) performance, especially for workloads that parallel file systems cannot handle well, such as the cases with intense data synchronization or heavy metadata operations. We conducted experiments with several synthetic benchmarks, an I/O benchmark, and a real application.more » We modeled the performance of these two systems using collected data from our experiments, and we provide a predictive method to identify which system offers better I/O performance given a specific workload. The results show that we can optimize the I/O performance in HPC systems by utilizing key-value stores.« less

Development and Evaluation of a Parallel Reaction Monitoring Strategy for Large-Scale Targeted Metabolomics Quantification.

PubMed

Zhou, Juntuo; Liu, Huiying; Liu, Yang; Liu, Jia; Zhao, Xuyang; Yin, Yuxin

2016-04-19

Recent advances in mass spectrometers which have yielded higher resolution and faster scanning speeds have expanded their application in metabolomics of diverse diseases. Using a quadrupole-Orbitrap LC-MS system, we developed an efficient large-scale quantitative method targeting 237 metabolites involved in various metabolic pathways using scheduled, parallel reaction monitoring (PRM). We assessed the dynamic range, linearity, reproducibility, and system suitability of the PRM assay by measuring concentration curves, biological samples, and clinical serum samples. The quantification performances of PRM and MS1-based assays in Q-Exactive were compared, and the MRM assay in QTRAP 6500 was also compared. The PRM assay monitoring 237 polar metabolites showed greater reproducibility and quantitative accuracy than MS1-based quantification and also showed greater flexibility in postacquisition assay refinement than the MRM assay in QTRAP 6500. We present a workflow for convenient PRM data processing using Skyline software which is free of charge. In this study we have established a reliable PRM methodology on a quadrupole-Orbitrap platform for evaluation of large-scale targeted metabolomics, which provides a new choice for basic and clinical metabolomics study.

Vectorial finite elements for solving the radiative transfer equation

NASA Astrophysics Data System (ADS)

Badri, M. A.; Jolivet, P.; Rousseau, B.; Le Corre, S.; Digonnet, H.; Favennec, Y.

2018-06-01

The discrete ordinate method coupled with the finite element method is often used for the spatio-angular discretization of the radiative transfer equation. In this paper we attempt to improve upon such a discretization technique. Instead of using standard finite elements, we reformulate the radiative transfer equation using vectorial finite elements. In comparison to standard finite elements, this reformulation yields faster timings for the linear system assemblies, as well as for the solution phase when using scattering media. The proposed vectorial finite element discretization for solving the radiative transfer equation is cross-validated against a benchmark problem available in literature. In addition, we have used the method of manufactured solutions to verify the order of accuracy for our discretization technique within different absorbing, scattering, and emitting media. For solving large problems of radiation on parallel computers, the vectorial finite element method is parallelized using domain decomposition. The proposed domain decomposition method scales on large number of processes, and its performance is unaffected by the changes in optical thickness of the medium. Our parallel solver is used to solve a large scale radiative transfer problem of the Kelvin-cell radiation.

Analysis and optimization of gyrokinetic toroidal simulations on homogenous and heterogenous platforms

DOE PAGES

Ibrahim, Khaled Z.; Madduri, Kamesh; Williams, Samuel; ...

2013-07-18

The Gyrokinetic Toroidal Code (GTC) uses the particle-in-cell method to efficiently simulate plasma microturbulence. This paper presents novel analysis and optimization techniques to enhance the performance of GTC on large-scale machines. We introduce cell access analysis to better manage locality vs. synchronization tradeoffs on CPU and GPU-based architectures. Finally, our optimized hybrid parallel implementation of GTC uses MPI, OpenMP, and NVIDIA CUDA, achieves up to a 2× speedup over the reference Fortran version on multiple parallel systems, and scales efficiently to tens of thousands of cores.

Overview of the NCC

NASA Technical Reports Server (NTRS)

Liu, Nan-Suey

2001-01-01

A multi-disciplinary design/analysis tool for combustion systems is critical for optimizing the low-emission, high-performance combustor design process. Based on discussions between then NASA Lewis Research Center and the jet engine companies, an industry-government team was formed in early 1995 to develop the National Combustion Code (NCC), which is an integrated system of computer codes for the design and analysis of combustion systems. NCC has advanced features that address the need to meet designer's requirements such as "assured accuracy", "fast turnaround", and "acceptable cost". The NCC development team is comprised of Allison Engine Company (Allison), CFD Research Corporation (CFDRC), GE Aircraft Engines (GEAE), NASA Glenn Research Center (LeRC), and Pratt & Whitney (P&W). The "unstructured mesh" capability and "parallel computing" are fundamental features of NCC from its inception. The NCC system is composed of a set of "elements" which includes grid generator, main flow solver, turbulence module, turbulence and chemistry interaction module, chemistry module, spray module, radiation heat transfer module, data visualization module, and a post-processor for evaluating engine performance parameters. Each element may have contributions from several team members. Such a multi-source multi-element system needs to be integrated in a way that facilitates inter-module data communication, flexibility in module selection, and ease of integration. The development of the NCC beta version was essentially completed in June 1998. Technical details of the NCC elements are given in the Reference List. Elements such as the baseline flow solver, turbulence module, and the chemistry module, have been extensively validated; and their parallel performance on large-scale parallel systems has been evaluated and optimized. However the scalar PDF module and the Spray module, as well as their coupling with the baseline flow solver, were developed in a small-scale distributed computing environment. As a result, the validation of the NCC beta version as a whole was quite limited. Current effort has been focused on the validation of the integrated code and the evaluation/optimization of its overall performance on large-scale parallel systems.

Parallelization of the FLAPW method and comparison with the PPW method

NASA Astrophysics Data System (ADS)

Canning, Andrew; Mannstadt, Wolfgang; Freeman, Arthur

2000-03-01

The FLAPW (full-potential linearized-augmented plane-wave) method is one of the most accurate first-principles methods for determining electronic and magnetic properties of crystals and surfaces. In the past the FLAPW method has been limited to systems of about a hundred atoms due to the lack of an efficient parallel implementation to exploit the power and memory of parallel computers. In this work we present an efficient parallelization of the method by division among the processors of the plane-wave components for each state. The code is also optimized for RISC (reduced instruction set computer) architectures, such as those found on most parallel computers, making full use of BLAS (basic linear algebra subprograms) wherever possible. Scaling results are presented for systems of up to 686 silicon atoms and 343 palladium atoms per unit cell running on up to 512 processors on a Cray T3E parallel supercomputer. Some results will also be presented on a comparison of the plane-wave pseudopotential method and the FLAPW method on large systems.

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

Rizzi, Silvio; Hereld, Mark; Insley, Joseph

In this work we perform in-situ visualization of molecular dynamics simulations, which can help scientists to visualize simulation output on-the-fly, without incurring storage overheads. We present a case study to couple LAMMPS, the large-scale molecular dynamics simulation code with vl3, our parallel framework for large-scale visualization and analysis. Our motivation is to identify effective approaches for covisualization and exploration of large-scale atomistic simulations at interactive frame rates.We propose a system of coupled libraries and describe its architecture, with an implementation that runs on GPU-based clusters. We present the results of strong and weak scalability experiments, as well as future researchmore » avenues based on our results.« less

The Scalable Checkpoint/Restart Library

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

Moody, A.

The Scalable Checkpoint/Restart (SCR) library provides an interface that codes may use to worite our and read in application-level checkpoints in a scalable fashion. In the current implementation, checkpoint files are cached in local storage (hard disk or RAM disk) on the compute nodes. This technique provides scalable aggregate bandwidth and uses storage resources that are fully dedicated to the job. This approach addresses the two common drawbacks of checkpointing a large-scale application to a shared parallel file system, namely, limited bandwidth and file system contention. In fact, on current platforms, SCR scales linearly with the number of compute nodes.more » It has been benchmarked as high as 720GB/s on 1094 nodes of Atlas, which is nearly two orders of magnitude faster thanthe parallel file system.« less

On the nature of the NAA diffusion attenuated MR signal in the central nervous system.

PubMed

Kroenke, Christopher D; Ackerman, Joseph J H; Yablonskiy, Dmitriy A

2004-11-01

In the brain, on a macroscopic scale, diffusion of the intraneuronal constituent N-acetyl-L-aspartate (NAA) appears to be isotropic. In contrast, on a microscopic scale, NAA diffusion is likely highly anisotropic, with displacements perpendicular to neuronal fibers being markedly hindered, and parallel displacements less so. In this report we first substantiate that local anisotropy influences NAA diffusion in vivo by observing differing diffusivities parallel and perpendicular to human corpus callosum axonal fibers. We then extend our measurements to large voxels within rat brains. As expected, the macroscopic apparent diffusion coefficient (ADC) of NAA is practically isotropic due to averaging of the numerous and diverse fiber orientations. We demonstrate that the substantially non-monoexponential diffusion-mediated MR signal decay vs. b value can be quantitatively explained by a theoretical model of NAA confined to an ensemble of differently oriented neuronal fibers. On the microscopic scale, NAA diffusion is found to be strongly anisotropic, with displacements occurring almost exclusively parallel to the local fiber axis. This parallel diffusivity, ADCparallel, is 0.36 +/- 0.01 microm2/ms, and ADCperpendicular is essentially zero. From ADCparallel the apparent viscosity of the neuron cytoplasm is estimated to be twice as large as that of a temperature-matched dilute aqueous solution. (c) 2004 Wiley-Liss, Inc.

Three pillars for achieving quantum mechanical molecular dynamics simulations of huge systems: Divide-and-conquer, density-functional tight-binding, and massively parallel computation.

PubMed

Nishizawa, Hiroaki; Nishimura, Yoshifumi; Kobayashi, Masato; Irle, Stephan; Nakai, Hiromi

2016-08-05

The linear-scaling divide-and-conquer (DC) quantum chemical methodology is applied to the density-functional tight-binding (DFTB) theory to develop a massively parallel program that achieves on-the-fly molecular reaction dynamics simulations of huge systems from scratch. The functions to perform large scale geometry optimization and molecular dynamics with DC-DFTB potential energy surface are implemented to the program called DC-DFTB-K. A novel interpolation-based algorithm is developed for parallelizing the determination of the Fermi level in the DC method. The performance of the DC-DFTB-K program is assessed using a laboratory computer and the K computer. Numerical tests show the high efficiency of the DC-DFTB-K program, a single-point energy gradient calculation of a one-million-atom system is completed within 60 s using 7290 nodes of the K computer. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Large-scale optimization-based non-negative computational framework for diffusion equations: Parallel implementation and performance studies

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

Chang, Justin; Karra, Satish; Nakshatrala, Kalyana B.

It is well-known that the standard Galerkin formulation, which is often the formulation of choice under the finite element method for solving self-adjoint diffusion equations, does not meet maximum principles and the non-negative constraint for anisotropic diffusion equations. Recently, optimization-based methodologies that satisfy maximum principles and the non-negative constraint for steady-state and transient diffusion-type equations have been proposed. To date, these methodologies have been tested only on small-scale academic problems. The purpose of this paper is to systematically study the performance of the non-negative methodology in the context of high performance computing (HPC). PETSc and TAO libraries are, respectively, usedmore » for the parallel environment and optimization solvers. For large-scale problems, it is important for computational scientists to understand the computational performance of current algorithms available in these scientific libraries. The numerical experiments are conducted on the state-of-the-art HPC systems, and a single-core performance model is used to better characterize the efficiency of the solvers. Furthermore, our studies indicate that the proposed non-negative computational framework for diffusion-type equations exhibits excellent strong scaling for real-world large-scale problems.« less

Large-scale optimization-based non-negative computational framework for diffusion equations: Parallel implementation and performance studies

DOE PAGES

Chang, Justin; Karra, Satish; Nakshatrala, Kalyana B.

2016-07-26

It is well-known that the standard Galerkin formulation, which is often the formulation of choice under the finite element method for solving self-adjoint diffusion equations, does not meet maximum principles and the non-negative constraint for anisotropic diffusion equations. Recently, optimization-based methodologies that satisfy maximum principles and the non-negative constraint for steady-state and transient diffusion-type equations have been proposed. To date, these methodologies have been tested only on small-scale academic problems. The purpose of this paper is to systematically study the performance of the non-negative methodology in the context of high performance computing (HPC). PETSc and TAO libraries are, respectively, usedmore » for the parallel environment and optimization solvers. For large-scale problems, it is important for computational scientists to understand the computational performance of current algorithms available in these scientific libraries. The numerical experiments are conducted on the state-of-the-art HPC systems, and a single-core performance model is used to better characterize the efficiency of the solvers. Furthermore, our studies indicate that the proposed non-negative computational framework for diffusion-type equations exhibits excellent strong scaling for real-world large-scale problems.« less

Multidisciplinary Optimization Methods for Aircraft Preliminary Design

NASA Technical Reports Server (NTRS)

Kroo, Ilan; Altus, Steve; Braun, Robert; Gage, Peter; Sobieski, Ian

1994-01-01

This paper describes a research program aimed at improved methods for multidisciplinary design and optimization of large-scale aeronautical systems. The research involves new approaches to system decomposition, interdisciplinary communication, and methods of exploiting coarse-grained parallelism for analysis and optimization. A new architecture, that involves a tight coupling between optimization and analysis, is intended to improve efficiency while simplifying the structure of multidisciplinary, computation-intensive design problems involving many analysis disciplines and perhaps hundreds of design variables. Work in two areas is described here: system decomposition using compatibility constraints to simplify the analysis structure and take advantage of coarse-grained parallelism; and collaborative optimization, a decomposition of the optimization process to permit parallel design and to simplify interdisciplinary communication requirements.

GLAD: a system for developing and deploying large-scale bioinformatics grid.

PubMed

Teo, Yong-Meng; Wang, Xianbing; Ng, Yew-Kwong

2005-03-01

Grid computing is used to solve large-scale bioinformatics problems with gigabytes database by distributing the computation across multiple platforms. Until now in developing bioinformatics grid applications, it is extremely tedious to design and implement the component algorithms and parallelization techniques for different classes of problems, and to access remotely located sequence database files of varying formats across the grid. In this study, we propose a grid programming toolkit, GLAD (Grid Life sciences Applications Developer), which facilitates the development and deployment of bioinformatics applications on a grid. GLAD has been developed using ALiCE (Adaptive scaLable Internet-based Computing Engine), a Java-based grid middleware, which exploits the task-based parallelism. Two bioinformatics benchmark applications, such as distributed sequence comparison and distributed progressive multiple sequence alignment, have been developed using GLAD.

The force on the flex: Global parallelism and portability

NASA Technical Reports Server (NTRS)

Jordan, H. F.

1986-01-01

A parallel programming methodology, called the force, supports the construction of programs to be executed in parallel by an unspecified, but potentially large, number of processes. The methodology was originally developed on a pipelined, shared memory multiprocessor, the Denelcor HEP, and embodies the primitive operations of the force in a set of macros which expand into multiprocessor Fortran code. A small set of primitives is sufficient to write large parallel programs, and the system has been used to produce 10,000 line programs in computational fluid dynamics. The level of complexity of the force primitives is intermediate. It is high enough to mask detailed architectural differences between multiprocessors but low enough to give the user control over performance. The system is being ported to a medium scale multiprocessor, the Flex/32, which is a 20 processor system with a mixture of shared and local memory. Memory organization and the type of processor synchronization supported by the hardware on the two machines lead to some differences in efficient implementations of the force primitives, but the user interface remains the same. An initial implementation was done by retargeting the macros to Flexible Computer Corporation's ConCurrent C language. Subsequently, the macros were caused to directly produce the system calls which form the basis for ConCurrent C. The implementation of the Fortran based system is in step with Flexible Computer Corporations's implementation of a Fortran system in the parallel environment.

Simulation framework for intelligent transportation systems

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

Ewing, T.; Doss, E.; Hanebutte, U.

1996-10-01

A simulation framework has been developed for a large-scale, comprehensive, scaleable simulation of an Intelligent Transportation System (ITS). The simulator is designed for running on parallel computers and distributed (networked) computer systems, but can run on standalone workstations for smaller simulations. The simulator currently models instrumented smart vehicles with in-vehicle navigation units capable of optimal route planning and Traffic Management Centers (TMC). The TMC has probe vehicle tracking capabilities (display position and attributes of instrumented vehicles), and can provide two-way interaction with traffic to provide advisories and link times. Both the in-vehicle navigation module and the TMC feature detailed graphicalmore » user interfaces to support human-factors studies. Realistic modeling of variations of the posted driving speed are based on human factors studies that take into consideration weather, road conditions, driver personality and behavior, and vehicle type. The prototype has been developed on a distributed system of networked UNIX computers but is designed to run on parallel computers, such as ANL`s IBM SP-2, for large-scale problems. A novel feature of the approach is that vehicles are represented by autonomous computer processes which exchange messages with other processes. The vehicles have a behavior model which governs route selection and driving behavior, and can react to external traffic events much like real vehicles. With this approach, the simulation is scaleable to take advantage of emerging massively parallel processor (MPP) systems.« less

Combining points and lines in rectifying satellite images

NASA Astrophysics Data System (ADS)

Elaksher, Ahmed F.

2017-09-01

The quick advance in remote sensing technologies established the potential to gather accurate and reliable information about the Earth surface using high resolution satellite images. Remote sensing satellite images of less than one-meter pixel size are currently used in large-scale mapping. Rigorous photogrammetric equations are usually used to describe the relationship between the image coordinates and ground coordinates. These equations require the knowledge of the exterior and interior orientation parameters of the image that might not be available. On the other hand, the parallel projection transformation could be used to represent the mathematical relationship between the image-space and objectspace coordinate systems and provides the required accuracy for large-scale mapping using fewer ground control features. This article investigates the differences between point-based and line-based parallel projection transformation models in rectifying satellite images with different resolutions. The point-based parallel projection transformation model and its extended form are presented and the corresponding line-based forms are developed. Results showed that the RMS computed using the point- or line-based transformation models are equivalent and satisfy the requirement for large-scale mapping. The differences between the transformation parameters computed using the point- and line-based transformation models are insignificant. The results showed high correlation between the differences in the ground elevation and the RMS.

Decomposition method for fast computation of gigapixel-sized Fresnel holograms on a graphics processing unit cluster.

PubMed

Jackin, Boaz Jessie; Watanabe, Shinpei; Ootsu, Kanemitsu; Ohkawa, Takeshi; Yokota, Takashi; Hayasaki, Yoshio; Yatagai, Toyohiko; Baba, Takanobu

2018-04-20

A parallel computation method for large-size Fresnel computer-generated hologram (CGH) is reported. The method was introduced by us in an earlier report as a technique for calculating Fourier CGH from 2D object data. In this paper we extend the method to compute Fresnel CGH from 3D object data. The scale of the computation problem is also expanded to 2 gigapixels, making it closer to real application requirements. The significant feature of the reported method is its ability to avoid communication overhead and thereby fully utilize the computing power of parallel devices. The method exhibits three layers of parallelism that favor small to large scale parallel computing machines. Simulation and optical experiments were conducted to demonstrate the workability and to evaluate the efficiency of the proposed technique. A two-times improvement in computation speed has been achieved compared to the conventional method, on a 16-node cluster (one GPU per node) utilizing only one layer of parallelism. A 20-times improvement in computation speed has been estimated utilizing two layers of parallelism on a very large-scale parallel machine with 16 nodes, where each node has 16 GPUs.

Linux OS Jitter Measurements at Large Node Counts using a BlueGene/L

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

Jones, Terry R; Tauferner, Mr. Andrew; Inglett, Mr. Todd

2010-01-01

We present experimental results for a coordinated scheduling implementation of the Linux operating system. Results were collected on an IBM Blue Gene/L machine at scales up to 16K nodes. Our results indicate coordinated scheduling was able to provide a dramatic improvement in scaling performance for two applications characterized as bulk synchronous parallel programs.

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.

Demonstration of Hadoop-GIS: A Spatial Data Warehousing System Over MapReduce

PubMed Central

Aji, Ablimit; Sun, Xiling; Vo, Hoang; Liu, Qioaling; Lee, Rubao; Zhang, Xiaodong; Saltz, Joel; Wang, Fusheng

2016-01-01

The proliferation of GPS-enabled devices, and the rapid improvement of scientific instruments have resulted in massive amounts of spatial data in the last decade. Support of high performance spatial queries on large volumes data has become increasingly important in numerous fields, which requires a scalable and efficient spatial data warehousing solution as existing approaches exhibit scalability limitations and efficiency bottlenecks for large scale spatial applications. In this demonstration, we present Hadoop-GIS – a scalable and high performance spatial query system over MapReduce. Hadoop-GIS provides an efficient spatial query engine to process spatial queries, data and space based partitioning, and query pipelines that parallelize queries implicitly on MapReduce. Hadoop-GIS also provides an expressive, SQL-like spatial query language for workload specification. We will demonstrate how spatial queries are expressed in spatially extended SQL queries, and submitted through a command line/web interface for execution. Parallel to our system demonstration, we explain the system architecture and details on how queries are translated to MapReduce operators, optimized, and executed on Hadoop. In addition, we will showcase how the system can be used to support two representative real world use cases: large scale pathology analytical imaging, and geo-spatial data warehousing. PMID:27617325

Software Engineering Support of the Third Round of Scientific Grand Challenge Investigations: Earth System Modeling Software Framework Survey

NASA Technical Reports Server (NTRS)

Talbot, Bryan; Zhou, Shu-Jia; Higgins, Glenn; Zukor, Dorothy (Technical Monitor)

2002-01-01

One of the most significant challenges in large-scale climate modeling, as well as in high-performance computing in other scientific fields, is that of effectively integrating many software models from multiple contributors. A software framework facilitates the integration task, both in the development and runtime stages of the simulation. Effective software frameworks reduce the programming burden for the investigators, freeing them to focus more on the science and less on the parallel communication implementation. while maintaining high performance across numerous supercomputer and workstation architectures. This document surveys numerous software frameworks for potential use in Earth science modeling. Several frameworks are evaluated in depth, including Parallel Object-Oriented Methods and Applications (POOMA), Cactus (from (he relativistic physics community), Overture, Goddard Earth Modeling System (GEMS), the National Center for Atmospheric Research Flux Coupler, and UCLA/UCB Distributed Data Broker (DDB). Frameworks evaluated in less detail include ROOT, Parallel Application Workspace (PAWS), and Advanced Large-Scale Integrated Computational Environment (ALICE). A host of other frameworks and related tools are referenced in this context. The frameworks are evaluated individually and also compared with each other.

Pushing configuration-interaction to the limit: Towards massively parallel MCSCF calculations

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

Vogiatzis, Konstantinos D.; Ma, Dongxia; Olsen, Jeppe

A new large-scale parallel multiconfigurational self-consistent field (MCSCF) implementation in the open-source NWChem computational chemistry code is presented. The generalized active space approach is used to partition large configuration interaction (CI) vectors and generate a sufficient number of batches that can be distributed to the available cores. Massively parallel CI calculations with large active spaces can be performed. The new parallel MCSCF implementation is tested for the chromium trimer and for an active space of 20 electrons in 20 orbitals, which can now routinely be performed. Unprecedented CI calculations with an active space of 22 electrons in 22 orbitals formore » the pentacene systems were performed and a single CI iteration calculation with an active space of 24 electrons in 24 orbitals for the chromium tetramer was possible. In conclusion, the chromium tetramer corresponds to a CI expansion of one trillion Slater determinants (914 058 513 424) and is the largest conventional CI calculation attempted up to date.« less

Pushing configuration-interaction to the limit: Towards massively parallel MCSCF calculations

DOE PAGES

Vogiatzis, Konstantinos D.; Ma, Dongxia; Olsen, Jeppe; ...

2017-11-14

A new large-scale parallel multiconfigurational self-consistent field (MCSCF) implementation in the open-source NWChem computational chemistry code is presented. The generalized active space approach is used to partition large configuration interaction (CI) vectors and generate a sufficient number of batches that can be distributed to the available cores. Massively parallel CI calculations with large active spaces can be performed. The new parallel MCSCF implementation is tested for the chromium trimer and for an active space of 20 electrons in 20 orbitals, which can now routinely be performed. Unprecedented CI calculations with an active space of 22 electrons in 22 orbitals formore » the pentacene systems were performed and a single CI iteration calculation with an active space of 24 electrons in 24 orbitals for the chromium tetramer was possible. In conclusion, the chromium tetramer corresponds to a CI expansion of one trillion Slater determinants (914 058 513 424) and is the largest conventional CI calculation attempted up to date.« less

Pushing configuration-interaction to the limit: Towards massively parallel MCSCF calculations

NASA Astrophysics Data System (ADS)

Vogiatzis, Konstantinos D.; Ma, Dongxia; Olsen, Jeppe; Gagliardi, Laura; de Jong, Wibe A.

2017-11-01

A new large-scale parallel multiconfigurational self-consistent field (MCSCF) implementation in the open-source NWChem computational chemistry code is presented. The generalized active space approach is used to partition large configuration interaction (CI) vectors and generate a sufficient number of batches that can be distributed to the available cores. Massively parallel CI calculations with large active spaces can be performed. The new parallel MCSCF implementation is tested for the chromium trimer and for an active space of 20 electrons in 20 orbitals, which can now routinely be performed. Unprecedented CI calculations with an active space of 22 electrons in 22 orbitals for the pentacene systems were performed and a single CI iteration calculation with an active space of 24 electrons in 24 orbitals for the chromium tetramer was possible. The chromium tetramer corresponds to a CI expansion of one trillion Slater determinants (914 058 513 424) and is the largest conventional CI calculation attempted up to date.

Applications of Parallel Process HiMAP for Large Scale Multidisciplinary Problems

NASA Technical Reports Server (NTRS)

Guruswamy, Guru P.; Potsdam, Mark; Rodriguez, David; Kwak, Dochay (Technical Monitor)

2000-01-01

HiMAP is a three level parallel middleware that can be interfaced to a large scale global design environment for code independent, multidisciplinary analysis using high fidelity equations. Aerospace technology needs are rapidly changing. Computational tools compatible with the requirements of national programs such as space transportation are needed. Conventional computation tools are inadequate for modern aerospace design needs. Advanced, modular computational tools are needed, such as those that incorporate the technology of massively parallel processors (MPP).

A cloud-based framework for large-scale traditional Chinese medical record retrieval.

PubMed

Liu, Lijun; Liu, Li; Fu, Xiaodong; Huang, Qingsong; Zhang, Xianwen; Zhang, Yin

2018-01-01

Electronic medical records are increasingly common in medical practice. The secondary use of medical records has become increasingly important. It relies on the ability to retrieve the complete information about desired patient populations. How to effectively and accurately retrieve relevant medical records from large- scale medical big data is becoming a big challenge. Therefore, we propose an efficient and robust framework based on cloud for large-scale Traditional Chinese Medical Records (TCMRs) retrieval. We propose a parallel index building method and build a distributed search cluster, the former is used to improve the performance of index building, and the latter is used to provide high concurrent online TCMRs retrieval. Then, a real-time multi-indexing model is proposed to ensure the latest relevant TCMRs are indexed and retrieved in real-time, and a semantics-based query expansion method and a multi- factor ranking model are proposed to improve retrieval quality. Third, we implement a template-based visualization method for displaying medical reports. The proposed parallel indexing method and distributed search cluster can improve the performance of index building and provide high concurrent online TCMRs retrieval. The multi-indexing model can ensure the latest relevant TCMRs are indexed and retrieved in real-time. The semantics expansion method and the multi-factor ranking model can enhance retrieval quality. The template-based visualization method can enhance the availability and universality, where the medical reports are displayed via friendly web interface. In conclusion, compared with the current medical record retrieval systems, our system provides some advantages that are useful in improving the secondary use of large-scale traditional Chinese medical records in cloud environment. The proposed system is more easily integrated with existing clinical systems and be used in various scenarios. Copyright © 2017. Published by Elsevier Inc.

MOOSE: A PARALLEL COMPUTATIONAL FRAMEWORK FOR COUPLED SYSTEMS OF NONLINEAR EQUATIONS.

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

G. Hansen; C. Newman; D. Gaston

Systems of coupled, nonlinear partial di?erential equations often arise in sim- ulation of nuclear processes. MOOSE: Multiphysics Ob ject Oriented Simulation Environment, a parallel computational framework targeted at solving these systems is presented. As opposed to traditional data / ?ow oriented com- putational frameworks, MOOSE is instead founded on mathematics based on Jacobian-free Newton Krylov (JFNK). Utilizing the mathematical structure present in JFNK, physics are modularized into “Kernels” allowing for rapid production of new simulation tools. In addition, systems are solved fully cou- pled and fully implicit employing physics based preconditioning allowing for a large amount of ?exibility even withmore » large variance in time scales. Background on the mathematics, an inspection of the structure of MOOSE and several rep- resentative solutions from applications built on the framework are presented.« less

Graph-based linear scaling electronic structure theory.

PubMed

Niklasson, Anders M N; Mniszewski, Susan M; Negre, Christian F A; Cawkwell, Marc J; Swart, Pieter J; Mohd-Yusof, Jamal; Germann, Timothy C; Wall, Michael E; Bock, Nicolas; Rubensson, Emanuel H; Djidjev, Hristo

2016-06-21

We show how graph theory can be combined with quantum theory to calculate the electronic structure of large complex systems. The graph formalism is general and applicable to a broad range of electronic structure methods and materials, including challenging systems such as biomolecules. The methodology combines well-controlled accuracy, low computational cost, and natural low-communication parallelism. This combination addresses substantial shortcomings of linear scaling electronic structure theory, in particular with respect to quantum-based molecular dynamics simulations.

Graph-based linear scaling electronic structure theory

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

Niklasson, Anders M. N., E-mail: amn@lanl.gov; Negre, Christian F. A.; Cawkwell, Marc J.

2016-06-21

We show how graph theory can be combined with quantum theory to calculate the electronic structure of large complex systems. The graph formalism is general and applicable to a broad range of electronic structure methods and materials, including challenging systems such as biomolecules. The methodology combines well-controlled accuracy, low computational cost, and natural low-communication parallelism. This combination addresses substantial shortcomings of linear scaling electronic structure theory, in particular with respect to quantum-based molecular dynamics simulations.

Scalable domain decomposition solvers for stochastic PDEs in high performance computing

DOE PAGES

Desai, Ajit; Khalil, Mohammad; Pettit, Chris; ...

2017-09-21

Stochastic spectral finite element models of practical engineering systems may involve solutions of linear systems or linearized systems for non-linear problems with billions of unknowns. For stochastic modeling, it is therefore essential to design robust, parallel and scalable algorithms that can efficiently utilize high-performance computing to tackle such large-scale systems. Domain decomposition based iterative solvers can handle such systems. And though these algorithms exhibit excellent scalabilities, significant algorithmic and implementational challenges exist to extend them to solve extreme-scale stochastic systems using emerging computing platforms. Intrusive polynomial chaos expansion based domain decomposition algorithms are extended here to concurrently handle high resolutionmore » in both spatial and stochastic domains using an in-house implementation. Sparse iterative solvers with efficient preconditioners are employed to solve the resulting global and subdomain level local systems through multi-level iterative solvers. We also use parallel sparse matrix–vector operations to reduce the floating-point operations and memory requirements. Numerical and parallel scalabilities of these algorithms are presented for the diffusion equation having spatially varying diffusion coefficient modeled by a non-Gaussian stochastic process. Scalability of the solvers with respect to the number of random variables is also investigated.« less

Scalable domain decomposition solvers for stochastic PDEs in high performance computing

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

Desai, Ajit; Khalil, Mohammad; Pettit, Chris

Stochastic spectral finite element models of practical engineering systems may involve solutions of linear systems or linearized systems for non-linear problems with billions of unknowns. For stochastic modeling, it is therefore essential to design robust, parallel and scalable algorithms that can efficiently utilize high-performance computing to tackle such large-scale systems. Domain decomposition based iterative solvers can handle such systems. And though these algorithms exhibit excellent scalabilities, significant algorithmic and implementational challenges exist to extend them to solve extreme-scale stochastic systems using emerging computing platforms. Intrusive polynomial chaos expansion based domain decomposition algorithms are extended here to concurrently handle high resolutionmore » in both spatial and stochastic domains using an in-house implementation. Sparse iterative solvers with efficient preconditioners are employed to solve the resulting global and subdomain level local systems through multi-level iterative solvers. We also use parallel sparse matrix–vector operations to reduce the floating-point operations and memory requirements. Numerical and parallel scalabilities of these algorithms are presented for the diffusion equation having spatially varying diffusion coefficient modeled by a non-Gaussian stochastic process. Scalability of the solvers with respect to the number of random variables is also investigated.« less

Characterizing parallel file-access patterns on a large-scale multiprocessor

NASA Technical Reports Server (NTRS)

Purakayastha, Apratim; Ellis, Carla Schlatter; Kotz, David; Nieuwejaar, Nils; Best, Michael

1994-01-01

Rapid increases in the computational speeds of multiprocessors have not been matched by corresponding performance enhancements in the I/O subsystem. To satisfy the large and growing I/O requirements of some parallel scientific applications, we need parallel file systems that can provide high-bandwidth and high-volume data transfer between the I/O subsystem and thousands of processors. Design of such high-performance parallel file systems depends on a thorough grasp of the expected workload. So far there have been no comprehensive usage studies of multiprocessor file systems. Our CHARISMA project intends to fill this void. The first results from our study involve an iPSC/860 at NASA Ames. This paper presents results from a different platform, the CM-5 at the National Center for Supercomputing Applications. The CHARISMA studies are unique because we collect information about every individual read and write request and about the entire mix of applications running on the machines. The results of our trace analysis lead to recommendations for parallel file system design. First the file system should support efficient concurrent access to many files, and I/O requests from many jobs under varying load conditions. Second, it must efficiently manage large files kept open for long periods. Third, it should expect to see small requests predominantly sequential access patterns, application-wide synchronous access, no concurrent file-sharing between jobs appreciable byte and block sharing between processes within jobs, and strong interprocess locality. Finally, the trace data suggest that node-level write caches and collective I/O request interfaces may be useful in certain environments.

Improving parallel I/O autotuning with performance modeling

DOE PAGES

Behzad, Babak; Byna, Surendra; Wild, Stefan M.; ...

2014-01-01

Various layers of the parallel I/O subsystem offer tunable parameters for improving I/O performance on large-scale computers. However, searching through a large parameter space is challenging. We are working towards an autotuning framework for determining the parallel I/O parameters that can achieve good I/O performance for different data write patterns. In this paper, we characterize parallel I/O and discuss the development of predictive models for use in effectively reducing the parameter space. Furthermore, applying our technique on tuning an I/O kernel derived from a large-scale simulation code shows that the search time can be reduced from 12 hours to 2more » hours, while achieving 54X I/O performance speedup.« less

Efficient partitioning and assignment on programs for multiprocessor execution

NASA Technical Reports Server (NTRS)

Standley, Hilda M.

1993-01-01

The general problem studied is that of segmenting or partitioning programs for distribution across a multiprocessor system. Efficient partitioning and the assignment of program elements are of great importance since the time consumed in this overhead activity may easily dominate the computation, effectively eliminating any gains made by the use of the parallelism. In this study, the partitioning of sequentially structured programs (written in FORTRAN) is evaluated. Heuristics, developed for similar applications are examined. Finally, a model for queueing networks with finite queues is developed which may be used to analyze multiprocessor system architectures with a shared memory approach to the problem of partitioning. The properties of sequentially written programs form obstacles to large scale (at the procedure or subroutine level) parallelization. Data dependencies of even the minutest nature, reflecting the sequential development of the program, severely limit parallelism. The design of heuristic algorithms is tied to the experience gained in the parallel splitting. Parallelism obtained through the physical separation of data has seen some success, especially at the data element level. Data parallelism on a grander scale requires models that accurately reflect the effects of blocking caused by finite queues. A model for the approximation of the performance of finite queueing networks is developed. This model makes use of the decomposition approach combined with the efficiency of product form solutions.

Scalable Triadic Analysis of Large-Scale Graphs: Multi-Core vs. Multi-Processor vs. Multi-Threaded Shared Memory Architectures

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

Chin, George; Marquez, Andres; Choudhury, Sutanay

2012-09-01

Triadic analysis encompasses a useful set of graph mining methods that is centered on the concept of a triad, which is a subgraph of three nodes and the configuration of directed edges across the nodes. Such methods are often applied in the social sciences as well as many other diverse fields. Triadic methods commonly operate on a triad census that counts the number of triads of every possible edge configuration in a graph. Like other graph algorithms, triadic census algorithms do not scale well when graphs reach tens of millions to billions of nodes. To enable the triadic analysis ofmore » large-scale graphs, we developed and optimized a triad census algorithm to efficiently execute on shared memory architectures. We will retrace the development and evolution of a parallel triad census algorithm. Over the course of several versions, we continually adapted the code’s data structures and program logic to expose more opportunities to exploit parallelism on shared memory that would translate into improved computational performance. We will recall the critical steps and modifications that occurred during code development and optimization. Furthermore, we will compare the performances of triad census algorithm versions on three specific systems: Cray XMT, HP Superdome, and AMD multi-core NUMA machine. These three systems have shared memory architectures but with markedly different hardware capabilities to manage parallelism.« less

Compact holographic optical neural network system for real-time pattern recognition

NASA Astrophysics Data System (ADS)

Lu, Taiwei; Mintzer, David T.; Kostrzewski, Andrew A.; Lin, Freddie S.

1996-08-01

One of the important characteristics of artificial neural networks is their capability for massive interconnection and parallel processing. Recently, specialized electronic neural network processors and VLSI neural chips have been introduced in the commercial market. The number of parallel channels they can handle is limited because of the limited parallel interconnections that can be implemented with 1D electronic wires. High-resolution pattern recognition problems can require a large number of neurons for parallel processing of an image. This paper describes a holographic optical neural network (HONN) that is based on high- resolution volume holographic materials and is capable of performing massive 3D parallel interconnection of tens of thousands of neurons. A HONN with more than 16,000 neurons packaged in an attache case has been developed. Rotation- shift-scale-invariant pattern recognition operations have been demonstrated with this system. System parameters such as the signal-to-noise ratio, dynamic range, and processing speed are discussed.

Formation of Electrostatic Potential Drops in the Auroral Zone

NASA Technical Reports Server (NTRS)

Schriver, D.; Ashour-Abdalla, M.; Richard, R. L.

2001-01-01

In order to examine the self-consistent formation of large-scale quasi-static parallel electric fields in the auroral zone on a micro/meso scale, a particle in cell simulation has been developed. The code resolves electron Debye length scales so that electron micro-processes are included and a variable grid scheme is used such that the overall length scale of the simulation is of the order of an Earth radii along the magnetic field. The simulation is electrostatic and includes the magnetic mirror force, as well as two types of plasmas, a cold dense ionospheric plasma and a warm tenuous magnetospheric plasma. In order to study the formation of parallel electric fields in the auroral zone, different magnetospheric ion and electron inflow boundary conditions are used to drive the system. It has been found that for conditions in the primary (upward) current region an upward directed quasi-static electric field can form across the system due to magnetic mirroring of the magnetospheric ions and electrons at different altitudes. For conditions in the return (downward) current region it is shown that a quasi-static parallel electric field in the opposite sense of that in the primary current region is formed, i.e., the parallel electric field is directed earthward. The conditions for how these different electric fields can be formed are discussed using satellite observations and numerical simulations.

A Metascalable Computing Framework for Large Spatiotemporal-Scale Atomistic Simulations

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

Nomura, K; Seymour, R; Wang, W

2009-02-17

A metascalable (or 'design once, scale on new architectures') parallel computing framework has been developed for large spatiotemporal-scale atomistic simulations of materials based on spatiotemporal data locality principles, which is expected to scale on emerging multipetaflops architectures. The framework consists of: (1) an embedded divide-and-conquer (EDC) algorithmic framework based on spatial locality to design linear-scaling algorithms for high complexity problems; (2) a space-time-ensemble parallel (STEP) approach based on temporal locality to predict long-time dynamics, while introducing multiple parallelization axes; and (3) a tunable hierarchical cellular decomposition (HCD) parallelization framework to map these O(N) algorithms onto a multicore cluster based onmore » hybrid implementation combining message passing and critical section-free multithreading. The EDC-STEP-HCD framework exposes maximal concurrency and data locality, thereby achieving: (1) inter-node parallel efficiency well over 0.95 for 218 billion-atom molecular-dynamics and 1.68 trillion electronic-degrees-of-freedom quantum-mechanical simulations on 212,992 IBM BlueGene/L processors (superscalability); (2) high intra-node, multithreading parallel efficiency (nanoscalability); and (3) nearly perfect time/ensemble parallel efficiency (eon-scalability). The spatiotemporal scale covered by MD simulation on a sustained petaflops computer per day (i.e. petaflops {center_dot} day of computing) is estimated as NT = 2.14 (e.g. N = 2.14 million atoms for T = 1 microseconds).« less

A scalable parallel black oil simulator on distributed memory parallel computers

NASA Astrophysics Data System (ADS)

Wang, Kun; Liu, Hui; Chen, Zhangxin

2015-11-01

This paper presents our work on developing a parallel black oil simulator for distributed memory computers based on our in-house parallel platform. The parallel simulator is designed to overcome the performance issues of common simulators that are implemented for personal computers and workstations. The finite difference method is applied to discretize the black oil model. In addition, some advanced techniques are employed to strengthen the robustness and parallel scalability of the simulator, including an inexact Newton method, matrix decoupling methods, and algebraic multigrid methods. A new multi-stage preconditioner is proposed to accelerate the solution of linear systems from the Newton methods. Numerical experiments show that our simulator is scalable and efficient, and is capable of simulating extremely large-scale black oil problems with tens of millions of grid blocks using thousands of MPI processes on parallel computers.

An efficient implementation of 3D high-resolution imaging for large-scale seismic data with GPU/CPU heterogeneous parallel computing

NASA Astrophysics Data System (ADS)

Xu, Jincheng; Liu, Wei; Wang, Jin; Liu, Linong; Zhang, Jianfeng

2018-02-01

De-absorption pre-stack time migration (QPSTM) compensates for the absorption and dispersion of seismic waves by introducing an effective Q parameter, thereby making it an effective tool for 3D, high-resolution imaging of seismic data. Although the optimal aperture obtained via stationary-phase migration reduces the computational cost of 3D QPSTM and yields 3D stationary-phase QPSTM, the associated computational efficiency is still the main problem in the processing of 3D, high-resolution images for real large-scale seismic data. In the current paper, we proposed a division method for large-scale, 3D seismic data to optimize the performance of stationary-phase QPSTM on clusters of graphics processing units (GPU). Then, we designed an imaging point parallel strategy to achieve an optimal parallel computing performance. Afterward, we adopted an asynchronous double buffering scheme for multi-stream to perform the GPU/CPU parallel computing. Moreover, several key optimization strategies of computation and storage based on the compute unified device architecture (CUDA) were adopted to accelerate the 3D stationary-phase QPSTM algorithm. Compared with the initial GPU code, the implementation of the key optimization steps, including thread optimization, shared memory optimization, register optimization and special function units (SFU), greatly improved the efficiency. A numerical example employing real large-scale, 3D seismic data showed that our scheme is nearly 80 times faster than the CPU-QPSTM algorithm. Our GPU/CPU heterogeneous parallel computing framework significant reduces the computational cost and facilitates 3D high-resolution imaging for large-scale seismic data.

High Performance Input/Output for Parallel Computer Systems

NASA Technical Reports Server (NTRS)

Ligon, W. B.

1996-01-01

The goal of our project is to study the I/O characteristics of parallel applications used in Earth Science data processing systems such as Regional Data Centers (RDCs) or EOSDIS. Our approach is to study the runtime behavior of typical programs and the effect of key parameters of the I/O subsystem both under simulation and with direct experimentation on parallel systems. Our three year activity has focused on two items: developing a test bed that facilitates experimentation with parallel I/O, and studying representative programs from the Earth science data processing application domain. The Parallel Virtual File System (PVFS) has been developed for use on a number of platforms including the Tiger Parallel Architecture Workbench (TPAW) simulator, The Intel Paragon, a cluster of DEC Alpha workstations, and the Beowulf system (at CESDIS). PVFS provides considerable flexibility in configuring I/O in a UNIX- like environment. Access to key performance parameters facilitates experimentation. We have studied several key applications fiom levels 1,2 and 3 of the typical RDC processing scenario including instrument calibration and navigation, image classification, and numerical modeling codes. We have also considered large-scale scientific database codes used to organize image data.

Large-Scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) Simulations of the Molecular Crystal alphaRDX

DTIC Science & Technology

2013-08-01

potential for HMX / RDX (3, 9). ...................................................................................8 1 1. Purpose This work...6 dispersion and electrostatic interactions. Constants for the SB potential are given in table 1. 8 Table 1. SB potential for HMX / RDX (3, 9...modeling dislocations in the energetic molecular crystal RDX using the Large-Scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) molecular

Implementing a Parallel Image Edge Detection Algorithm Based on the Otsu-Canny Operator on the Hadoop Platform.

PubMed

Cao, Jianfang; Chen, Lichao; Wang, Min; Tian, Yun

2018-01-01

The Canny operator is widely used to detect edges in images. However, as the size of the image dataset increases, the edge detection performance of the Canny operator decreases and its runtime becomes excessive. To improve the runtime and edge detection performance of the Canny operator, in this paper, we propose a parallel design and implementation for an Otsu-optimized Canny operator using a MapReduce parallel programming model that runs on the Hadoop platform. The Otsu algorithm is used to optimize the Canny operator's dual threshold and improve the edge detection performance, while the MapReduce parallel programming model facilitates parallel processing for the Canny operator to solve the processing speed and communication cost problems that occur when the Canny edge detection algorithm is applied to big data. For the experiments, we constructed datasets of different scales from the Pascal VOC2012 image database. The proposed parallel Otsu-Canny edge detection algorithm performs better than other traditional edge detection algorithms. The parallel approach reduced the running time by approximately 67.2% on a Hadoop cluster architecture consisting of 5 nodes with a dataset of 60,000 images. Overall, our approach system speeds up the system by approximately 3.4 times when processing large-scale datasets, which demonstrates the obvious superiority of our method. The proposed algorithm in this study demonstrates both better edge detection performance and improved time performance.

Grid-Enabled Quantitative Analysis of Breast Cancer

DTIC Science & Technology

2010-10-01

large-scale, multi-modality computerized image analysis . The central hypothesis of this research is that large-scale image analysis for breast cancer...research, we designed a pilot study utilizing large scale parallel Grid computing harnessing nationwide infrastructure for medical image analysis . Also

A microkernel design for component-based parallel numerical software systems.

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

Balay, S.

1999-01-13

What is the minimal software infrastructure and what type of conventions are needed to simplify development of sophisticated parallel numerical application codes using a variety of software components that are not necessarily available as source code? We propose an opaque object-based model where the objects are dynamically loadable from the file system or network. The microkernel required to manage such a system needs to include, at most: (1) a few basic services, namely--a mechanism for loading objects at run time via dynamic link libraries, and consistent schemes for error handling and memory management; and (2) selected methods that all objectsmore » share, to deal with object life (destruction, reference counting, relationships), and object observation (viewing, profiling, tracing). We are experimenting with these ideas in the context of extensible numerical software within the ALICE (Advanced Large-scale Integrated Computational Environment) project, where we are building the microkernel to manage the interoperability among various tools for large-scale scientific simulations. This paper presents some preliminary observations and conclusions from our work with microkernel design.« less

NWChem: A comprehensive and scalable open-source solution for large scale molecular simulations

NASA Astrophysics Data System (ADS)

Valiev, M.; Bylaska, E. J.; Govind, N.; Kowalski, K.; Straatsma, T. P.; Van Dam, H. J. J.; Wang, D.; Nieplocha, J.; Apra, E.; Windus, T. L.; de Jong, W. A.

2010-09-01

The latest release of NWChem delivers an open-source computational chemistry package with extensive capabilities for large scale simulations of chemical and biological systems. Utilizing a common computational framework, diverse theoretical descriptions can be used to provide the best solution for a given scientific problem. Scalable parallel implementations and modular software design enable efficient utilization of current computational architectures. This paper provides an overview of NWChem focusing primarily on the core theoretical modules provided by the code and their parallel performance. Program summaryProgram title: NWChem Catalogue identifier: AEGI_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEGI_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Open Source Educational Community License No. of lines in distributed program, including test data, etc.: 11 709 543 No. of bytes in distributed program, including test data, etc.: 680 696 106 Distribution format: tar.gz Programming language: Fortran 77, C Computer: all Linux based workstations and parallel supercomputers, Windows and Apple machines Operating system: Linux, OS X, Windows Has the code been vectorised or parallelized?: Code is parallelized Classification: 2.1, 2.2, 3, 7.3, 7.7, 16.1, 16.2, 16.3, 16.10, 16.13 Nature of problem: Large-scale atomistic simulations of chemical and biological systems require efficient and reliable methods for ground and excited solutions of many-electron Hamiltonian, analysis of the potential energy surface, and dynamics. Solution method: Ground and excited solutions of many-electron Hamiltonian are obtained utilizing density-functional theory, many-body perturbation approach, and coupled cluster expansion. These solutions or a combination thereof with classical descriptions are then used to analyze potential energy surface and perform dynamical simulations. Additional comments: Full documentation is provided in the distribution file. This includes an INSTALL file giving details of how to build the package. A set of test runs is provided in the examples directory. The distribution file for this program is over 90 Mbytes and therefore is not delivered directly when download or Email is requested. Instead a html file giving details of how the program can be obtained is sent. Running time: Running time depends on the size of the chemical system, complexity of the method, number of cpu's and the computational task. It ranges from several seconds for serial DFT energy calculations on a few atoms to several hours for parallel coupled cluster energy calculations on tens of atoms or ab-initio molecular dynamics simulation on hundreds of atoms.

Parallel Multivariate Spatio-Temporal Clustering of Large Ecological Datasets on Hybrid Supercomputers

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

Sreepathi, Sarat; Kumar, Jitendra; Mills, Richard T.

A proliferation of data from vast networks of remote sensing platforms (satellites, unmanned aircraft systems (UAS), airborne etc.), observational facilities (meteorological, eddy covariance etc.), state-of-the-art sensors, and simulation models offer unprecedented opportunities for scientific discovery. Unsupervised classification is a widely applied data mining approach to derive insights from such data. However, classification of very large data sets is a complex computational problem that requires efficient numerical algorithms and implementations on high performance computing (HPC) platforms. Additionally, increasing power, space, cooling and efficiency requirements has led to the deployment of hybrid supercomputing platforms with complex architectures and memory hierarchies like themore » Titan system at Oak Ridge National Laboratory. The advent of such accelerated computing architectures offers new challenges and opportunities for big data analytics in general and specifically, large scale cluster analysis in our case. Although there is an existing body of work on parallel cluster analysis, those approaches do not fully meet the needs imposed by the nature and size of our large data sets. Moreover, they had scaling limitations and were mostly limited to traditional distributed memory computing platforms. We present a parallel Multivariate Spatio-Temporal Clustering (MSTC) technique based on k-means cluster analysis that can target hybrid supercomputers like Titan. We developed a hybrid MPI, CUDA and OpenACC implementation that can utilize both CPU and GPU resources on computational nodes. We describe performance results on Titan that demonstrate the scalability and efficacy of our approach in processing large ecological data sets.« less

Solutions of large-scale electromagnetics problems involving dielectric objects with the parallel multilevel fast multipole algorithm.

PubMed

Ergül, Özgür

2011-11-01

Fast and accurate solutions of large-scale electromagnetics problems involving homogeneous dielectric objects are considered. Problems are formulated with the electric and magnetic current combined-field integral equation and discretized with the Rao-Wilton-Glisson functions. Solutions are performed iteratively by using the multilevel fast multipole algorithm (MLFMA). For the solution of large-scale problems discretized with millions of unknowns, MLFMA is parallelized on distributed-memory architectures using a rigorous technique, namely, the hierarchical partitioning strategy. Efficiency and accuracy of the developed implementation are demonstrated on very large problems involving as many as 100 million unknowns.

Cellular automata with object-oriented features for parallel molecular network modeling.

PubMed

Zhu, Hao; Wu, Yinghui; Huang, Sui; Sun, Yan; Dhar, Pawan

2005-06-01

Cellular automata are an important modeling paradigm for studying the dynamics of large, parallel systems composed of multiple, interacting components. However, to model biological systems, cellular automata need to be extended beyond the large-scale parallelism and intensive communication in order to capture two fundamental properties characteristic of complex biological systems: hierarchy and heterogeneity. This paper proposes extensions to a cellular automata language, Cellang, to meet this purpose. The extended language, with object-oriented features, can be used to describe the structure and activity of parallel molecular networks within cells. Capabilities of this new programming language include object structure to define molecular programs within a cell, floating-point data type and mathematical functions to perform quantitative computation, message passing capability to describe molecular interactions, as well as new operators, statements, and built-in functions. We discuss relevant programming issues of these features, including the object-oriented description of molecular interactions with molecule encapsulation, message passing, and the description of heterogeneity and anisotropy at the cell and molecule levels. By enabling the integration of modeling at the molecular level with system behavior at cell, tissue, organ, or even organism levels, the program will help improve our understanding of how complex and dynamic biological activities are generated and controlled by parallel functioning of molecular networks. Index Terms-Cellular automata, modeling, molecular network, object-oriented.

Reduced-Order Structure-Preserving Model for Parallel-Connected Three-Phase Grid-Tied Inverters: Preprint

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

Johnson, Brian B; Purba, Victor; Jafarpour, Saber

Given that next-generation infrastructures will contain large numbers of grid-connected inverters and these interfaces will be satisfying a growing fraction of system load, it is imperative to analyze the impacts of power electronics on such systems. However, since each inverter model has a relatively large number of dynamic states, it would be impractical to execute complex system models where the full dynamics of each inverter are retained. To address this challenge, we derive a reduced-order structure-preserving model for parallel-connected grid-tied three-phase inverters. Here, each inverter in the system is assumed to have a full-bridge topology, LCL filter at the pointmore » of common coupling, and the control architecture for each inverter includes a current controller, a power controller, and a phase-locked loop for grid synchronization. We outline a structure-preserving reduced-order inverter model for the setting where the parallel inverters are each designed such that the filter components and controller gains scale linearly with the power rating. By structure preserving, we mean that the reduced-order three-phase inverter model is also composed of an LCL filter, a power controller, current controller, and PLL. That is, we show that the system of parallel inverters can be modeled exactly as one aggregated inverter unit and this equivalent model has the same number of dynamical states as an individual inverter in the paralleled system. Numerical simulations validate the reduced-order models.« less

Lessons Learned in Deploying the World s Largest Scale Lustre File System

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

Dillow, David A; Fuller, Douglas; Wang, Feiyi

2010-01-01

The Spider system at the Oak Ridge National Laboratory's Leadership Computing Facility (OLCF) is the world's largest scale Lustre parallel file system. Envisioned as a shared parallel file system capable of delivering both the bandwidth and capacity requirements of the OLCF's diverse computational environment, the project had a number of ambitious goals. To support the workloads of the OLCF's diverse computational platforms, the aggregate performance and storage capacity of Spider exceed that of our previously deployed systems by a factor of 6x - 240 GB/sec, and 17x - 10 Petabytes, respectively. Furthermore, Spider supports over 26,000 clients concurrently accessing themore » file system, which exceeds our previously deployed systems by nearly 4x. In addition to these scalability challenges, moving to a center-wide shared file system required dramatically improved resiliency and fault-tolerance mechanisms. This paper details our efforts in designing, deploying, and operating Spider. Through a phased approach of research and development, prototyping, deployment, and transition to operations, this work has resulted in a number of insights into large-scale parallel file system architectures, from both the design and the operational perspectives. We present in this paper our solutions to issues such as network congestion, performance baselining and evaluation, file system journaling overheads, and high availability in a system with tens of thousands of components. We also discuss areas of continued challenges, such as stressed metadata performance and the need for file system quality of service alongside with our efforts to address them. Finally, operational aspects of managing a system of this scale are discussed along with real-world data and observations.« less

GROMACS 4:  Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation.

PubMed

Hess, Berk; Kutzner, Carsten; van der Spoel, David; Lindahl, Erik

2008-03-01

Molecular simulation is an extremely useful, but computationally very expensive tool for studies of chemical and biomolecular systems. Here, we present a new implementation of our molecular simulation toolkit GROMACS which now both achieves extremely high performance on single processors from algorithmic optimizations and hand-coded routines and simultaneously scales very well on parallel machines. The code encompasses a minimal-communication domain decomposition algorithm, full dynamic load balancing, a state-of-the-art parallel constraint solver, and efficient virtual site algorithms that allow removal of hydrogen atom degrees of freedom to enable integration time steps up to 5 fs for atomistic simulations also in parallel. To improve the scaling properties of the common particle mesh Ewald electrostatics algorithms, we have in addition used a Multiple-Program, Multiple-Data approach, with separate node domains responsible for direct and reciprocal space interactions. Not only does this combination of algorithms enable extremely long simulations of large systems but also it provides that simulation performance on quite modest numbers of standard cluster nodes.

Extreme Performance Scalable Operating Systems Final Progress Report (July 1, 2008 - October 31, 2011)

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

Malony, Allen D; Shende, Sameer

This is the final progress report for the FastOS (Phase 2) (FastOS-2) project with Argonne National Laboratory and the University of Oregon (UO). The project started at UO on July 1, 2008 and ran until April 30, 2010, at which time a six-month no-cost extension began. The FastOS-2 work at UO delivered excellent results in all research work areas: * scalable parallel monitoring * kernel-level performance measurement * parallel I/0 system measurement * large-scale and hybrid application performance measurement * onlne scalable performance data reduction and analysis * binary instrumentation

Load Balancing Scientific Applications

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

Pearce, Olga Tkachyshyn

2014-12-01

The largest supercomputers have millions of independent processors, and concurrency levels are rapidly increasing. For ideal efficiency, developers of the simulations that run on these machines must ensure that computational work is evenly balanced among processors. Assigning work evenly is challenging because many large modern parallel codes simulate behavior of physical systems that evolve over time, and their workloads change over time. Furthermore, the cost of imbalanced load increases with scale because most large-scale scientific simulations today use a Single Program Multiple Data (SPMD) parallel programming model, and an increasing number of processors will wait for the slowest one atmore » the synchronization points. To address load imbalance, many large-scale parallel applications use dynamic load balance algorithms to redistribute work evenly. The research objective of this dissertation is to develop methods to decide when and how to load balance the application, and to balance it effectively and affordably. We measure and evaluate the computational load of the application, and develop strategies to decide when and how to correct the imbalance. Depending on the simulation, a fast, local load balance algorithm may be suitable, or a more sophisticated and expensive algorithm may be required. We developed a model for comparison of load balance algorithms for a specific state of the simulation that enables the selection of a balancing algorithm that will minimize overall runtime.« less

Performance of fully-coupled algebraic multigrid preconditioners for large-scale VMS resistive MHD

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

Lin, P. T.; Shadid, J. N.; Hu, J. J.

Here, we explore the current performance and scaling of a fully-implicit stabilized unstructured finite element (FE) variational multiscale (VMS) capability for large-scale simulations of 3D incompressible resistive magnetohydrodynamics (MHD). The large-scale linear systems that are generated by a Newton nonlinear solver approach are iteratively solved by preconditioned Krylov subspace methods. The efficiency of this approach is critically dependent on the scalability and performance of the algebraic multigrid preconditioner. Our study considers the performance of the numerical methods as recently implemented in the second-generation Trilinos implementation that is 64-bit compliant and is not limited by the 32-bit global identifiers of themore » original Epetra-based Trilinos. The study presents representative results for a Poisson problem on 1.6 million cores of an IBM Blue Gene/Q platform to demonstrate very large-scale parallel execution. Additionally, results for a more challenging steady-state MHD generator and a transient solution of a benchmark MHD turbulence calculation for the full resistive MHD system are also presented. These results are obtained on up to 131,000 cores of a Cray XC40 and one million cores of a BG/Q system.« less

Performance of fully-coupled algebraic multigrid preconditioners for large-scale VMS resistive MHD

DOE PAGES

Lin, P. T.; Shadid, J. N.; Hu, J. J.; ...

2017-11-06

Here, we explore the current performance and scaling of a fully-implicit stabilized unstructured finite element (FE) variational multiscale (VMS) capability for large-scale simulations of 3D incompressible resistive magnetohydrodynamics (MHD). The large-scale linear systems that are generated by a Newton nonlinear solver approach are iteratively solved by preconditioned Krylov subspace methods. The efficiency of this approach is critically dependent on the scalability and performance of the algebraic multigrid preconditioner. Our study considers the performance of the numerical methods as recently implemented in the second-generation Trilinos implementation that is 64-bit compliant and is not limited by the 32-bit global identifiers of themore » original Epetra-based Trilinos. The study presents representative results for a Poisson problem on 1.6 million cores of an IBM Blue Gene/Q platform to demonstrate very large-scale parallel execution. Additionally, results for a more challenging steady-state MHD generator and a transient solution of a benchmark MHD turbulence calculation for the full resistive MHD system are also presented. These results are obtained on up to 131,000 cores of a Cray XC40 and one million cores of a BG/Q system.« less

Computer Science Techniques Applied to Parallel Atomistic Simulation

NASA Astrophysics Data System (ADS)

Nakano, Aiichiro

1998-03-01

Recent developments in parallel processing technology and multiresolution numerical algorithms have established large-scale molecular dynamics (MD) simulations as a new research mode for studying materials phenomena such as fracture. However, this requires large system sizes and long simulated times. We have developed: i) Space-time multiresolution schemes; ii) fuzzy-clustering approach to hierarchical dynamics; iii) wavelet-based adaptive curvilinear-coordinate load balancing; iv) multilevel preconditioned conjugate gradient method; and v) spacefilling-curve-based data compression for parallel I/O. Using these techniques, million-atom parallel MD simulations are performed for the oxidation dynamics of nanocrystalline Al. The simulations take into account the effect of dynamic charge transfer between Al and O using the electronegativity equalization scheme. The resulting long-range Coulomb interaction is calculated efficiently with the fast multipole method. Results for temperature and charge distributions, residual stresses, bond lengths and bond angles, and diffusivities of Al and O will be presented. The oxidation of nanocrystalline Al is elucidated through immersive visualization in virtual environments. A unique dual-degree education program at Louisiana State University will also be discussed in which students can obtain a Ph.D. in Physics & Astronomy and a M.S. from the Department of Computer Science in five years. This program fosters interdisciplinary research activities for interfacing High Performance Computing and Communications with large-scale atomistic simulations of advanced materials. This work was supported by NSF (CAREER Program), ARO, PRF, and Louisiana LEQSF.

GENESIS: a hybrid-parallel and multi-scale molecular dynamics simulator with enhanced sampling algorithms for biomolecular and cellular simulations.

PubMed

Jung, Jaewoon; Mori, Takaharu; Kobayashi, Chigusa; Matsunaga, Yasuhiro; Yoda, Takao; Feig, Michael; Sugita, Yuji

2015-07-01

GENESIS (Generalized-Ensemble Simulation System) is a new software package for molecular dynamics (MD) simulations of macromolecules. It has two MD simulators, called ATDYN and SPDYN. ATDYN is parallelized based on an atomic decomposition algorithm for the simulations of all-atom force-field models as well as coarse-grained Go-like models. SPDYN is highly parallelized based on a domain decomposition scheme, allowing large-scale MD simulations on supercomputers. Hybrid schemes combining OpenMP and MPI are used in both simulators to target modern multicore computer architectures. Key advantages of GENESIS are (1) the highly parallel performance of SPDYN for very large biological systems consisting of more than one million atoms and (2) the availability of various REMD algorithms (T-REMD, REUS, multi-dimensional REMD for both all-atom and Go-like models under the NVT, NPT, NPAT, and NPγT ensembles). The former is achieved by a combination of the midpoint cell method and the efficient three-dimensional Fast Fourier Transform algorithm, where the domain decomposition space is shared in real-space and reciprocal-space calculations. Other features in SPDYN, such as avoiding concurrent memory access, reducing communication times, and usage of parallel input/output files, also contribute to the performance. We show the REMD simulation results of a mixed (POPC/DMPC) lipid bilayer as a real application using GENESIS. GENESIS is released as free software under the GPLv2 licence and can be easily modified for the development of new algorithms and molecular models. WIREs Comput Mol Sci 2015, 5:310-323. doi: 10.1002/wcms.1220.

Reducing computational costs in large scale 3D EIT by using a sparse Jacobian matrix with block-wise CGLS reconstruction.

PubMed

Yang, C L; Wei, H Y; Adler, A; Soleimani, M

2013-06-01

Electrical impedance tomography (EIT) is a fast and cost-effective technique to provide a tomographic conductivity image of a subject from boundary current-voltage data. This paper proposes a time and memory efficient method for solving a large scale 3D EIT inverse problem using a parallel conjugate gradient (CG) algorithm. The 3D EIT system with a large number of measurement data can produce a large size of Jacobian matrix; this could cause difficulties in computer storage and the inversion process. One of challenges in 3D EIT is to decrease the reconstruction time and memory usage, at the same time retaining the image quality. Firstly, a sparse matrix reduction technique is proposed using thresholding to set very small values of the Jacobian matrix to zero. By adjusting the Jacobian matrix into a sparse format, the element with zeros would be eliminated, which results in a saving of memory requirement. Secondly, a block-wise CG method for parallel reconstruction has been developed. The proposed method has been tested using simulated data as well as experimental test samples. Sparse Jacobian with a block-wise CG enables the large scale EIT problem to be solved efficiently. Image quality measures are presented to quantify the effect of sparse matrix reduction in reconstruction results.

Toward server-side, high performance climate change data analytics in the Earth System Grid Federation (ESGF) eco-system

NASA Astrophysics Data System (ADS)

Fiore, Sandro; Williams, Dean; Aloisio, Giovanni

2016-04-01

In many scientific domains such as climate, data is often n-dimensional and requires tools that support specialized data types and primitives to be properly stored, accessed, analysed and visualized. Moreover, new challenges arise in large-scale scenarios and eco-systems where petabytes (PB) of data can be available and data can be distributed and/or replicated (e.g., the Earth System Grid Federation (ESGF) serving the Coupled Model Intercomparison Project, Phase 5 (CMIP5) experiment, providing access to 2.5PB of data for the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5). Most of the tools currently available for scientific data analysis in the climate domain fail at large scale since they: (1) are desktop based and need the data locally; (2) are sequential, so do not benefit from available multicore/parallel machines; (3) do not provide declarative languages to express scientific data analysis tasks; (4) are domain-specific, which ties their adoption to a specific domain; and (5) do not provide a workflow support, to enable the definition of complex "experiments". The Ophidia project aims at facing most of the challenges highlighted above by providing a big data analytics framework for eScience. Ophidia provides declarative, server-side, and parallel data analysis, jointly with an internal storage model able to efficiently deal with multidimensional data and a hierarchical data organization to manage large data volumes ("datacubes"). The project relies on a strong background of high performance database management and OLAP systems to manage large scientific data sets. It also provides a native workflow management support, to define processing chains and workflows with tens to hundreds of data analytics operators to build real scientific use cases. With regard to interoperability aspects, the talk will present the contribution provided both to the RDA Working Group on Array Databases, and the Earth System Grid Federation (ESGF) Compute Working Team. Also highlighted will be the results of large scale climate model intercomparison data analysis experiments, for example: (1) defined in the context of the EU H2020 INDIGO-DataCloud project; (2) implemented in a real geographically distributed environment involving CMCC (Italy) and LLNL (US) sites; (3) exploiting Ophidia as server-side, parallel analytics engine; and (4) applied on real CMIP5 data sets available through ESGF.

RGCA: A Reliable GPU Cluster Architecture for Large-Scale Internet of Things Computing Based on Effective Performance-Energy Optimization

PubMed Central

Chen, Qingkui; Zhao, Deyu; Wang, Jingjuan

2017-01-01

This paper aims to develop a low-cost, high-performance and high-reliability computing system to process large-scale data using common data mining algorithms in the Internet of Things (IoT) computing environment. Considering the characteristics of IoT data processing, similar to mainstream high performance computing, we use a GPU (Graphics Processing Unit) cluster to achieve better IoT services. Firstly, we present an energy consumption calculation method (ECCM) based on WSNs. Then, using the CUDA (Compute Unified Device Architecture) Programming model, we propose a Two-level Parallel Optimization Model (TLPOM) which exploits reasonable resource planning and common compiler optimization techniques to obtain the best blocks and threads configuration considering the resource constraints of each node. The key to this part is dynamic coupling Thread-Level Parallelism (TLP) and Instruction-Level Parallelism (ILP) to improve the performance of the algorithms without additional energy consumption. Finally, combining the ECCM and the TLPOM, we use the Reliable GPU Cluster Architecture (RGCA) to obtain a high-reliability computing system considering the nodes’ diversity, algorithm characteristics, etc. The results show that the performance of the algorithms significantly increased by 34.1%, 33.96% and 24.07% for Fermi, Kepler and Maxwell on average with TLPOM and the RGCA ensures that our IoT computing system provides low-cost and high-reliability services. PMID:28777325

RGCA: A Reliable GPU Cluster Architecture for Large-Scale Internet of Things Computing Based on Effective Performance-Energy Optimization.

PubMed

Fang, Yuling; Chen, Qingkui; Xiong, Neal N; Zhao, Deyu; Wang, Jingjuan

2017-08-04

This paper aims to develop a low-cost, high-performance and high-reliability computing system to process large-scale data using common data mining algorithms in the Internet of Things (IoT) computing environment. Considering the characteristics of IoT data processing, similar to mainstream high performance computing, we use a GPU (Graphics Processing Unit) cluster to achieve better IoT services. Firstly, we present an energy consumption calculation method (ECCM) based on WSNs. Then, using the CUDA (Compute Unified Device Architecture) Programming model, we propose a Two-level Parallel Optimization Model (TLPOM) which exploits reasonable resource planning and common compiler optimization techniques to obtain the best blocks and threads configuration considering the resource constraints of each node. The key to this part is dynamic coupling Thread-Level Parallelism (TLP) and Instruction-Level Parallelism (ILP) to improve the performance of the algorithms without additional energy consumption. Finally, combining the ECCM and the TLPOM, we use the Reliable GPU Cluster Architecture (RGCA) to obtain a high-reliability computing system considering the nodes' diversity, algorithm characteristics, etc. The results show that the performance of the algorithms significantly increased by 34.1%, 33.96% and 24.07% for Fermi, Kepler and Maxwell on average with TLPOM and the RGCA ensures that our IoT computing system provides low-cost and high-reliability services.

Quantum error correction in crossbar architectures

NASA Astrophysics Data System (ADS)

Helsen, Jonas; Steudtner, Mark; Veldhorst, Menno; Wehner, Stephanie

2018-07-01

A central challenge for the scaling of quantum computing systems is the need to control all qubits in the system without a large overhead. A solution for this problem in classical computing comes in the form of so-called crossbar architectures. Recently we made a proposal for a large-scale quantum processor (Li et al arXiv:1711.03807 (2017)) to be implemented in silicon quantum dots. This system features a crossbar control architecture which limits parallel single-qubit control, but allows the scheme to overcome control scaling issues that form a major hurdle to large-scale quantum computing systems. In this work, we develop a language that makes it possible to easily map quantum circuits to crossbar systems, taking into account their architecture and control limitations. Using this language we show how to map well known quantum error correction codes such as the planar surface and color codes in this limited control setting with only a small overhead in time. We analyze the logical error behavior of this surface code mapping for estimated experimental parameters of the crossbar system and conclude that logical error suppression to a level useful for real quantum computation is feasible.

Shared versus distributed memory multiprocessors

NASA Technical Reports Server (NTRS)

Jordan, Harry F.

1991-01-01

The question of whether multiprocessors should have shared or distributed memory has attracted a great deal of attention. Some researchers argue strongly for building distributed memory machines, while others argue just as strongly for programming shared memory multiprocessors. A great deal of research is underway on both types of parallel systems. Special emphasis is placed on systems with a very large number of processors for computation intensive tasks and considers research and implementation trends. It appears that the two types of systems will likely converge to a common form for large scale multiprocessors.

Spatial attention determines the nature of nonverbal number representation.

PubMed

Hyde, Daniel C; Wood, Justin N

2011-09-01

Coordinated studies of adults, infants, and nonhuman animals provide evidence for two systems of nonverbal number representation: a "parallel individuation" system that represents individual items and a "numerical magnitude" system that represents the approximate cardinal value of a group. However, there is considerable debate about the nature and functions of these systems, due largely to the fact that some studies show a dissociation between small (1-3) and large (>3) number representation, whereas others do not. Using event-related potentials, we show that it is possible to determine which system will represent the numerical value of a small number set (1-3 items) by manipulating spatial attention. Specifically, when attention can select individual objects, an early brain response (N1) scales with the cardinal value of the display, the signature of parallel individuation. In contrast, when attention cannot select individual objects or is occupied by another task, a later brain response (P2p) scales with ratio, the signature of the approximate numerical magnitude system. These results provide neural evidence that small numbers can be represented as approximate numerical magnitudes. Further, they empirically demonstrate the importance of early attentional processes to number representation by showing that the way in which attention disperses across a scene determines which numerical system will deploy in a given context.

Accurate and Efficient Parallel Implementation of an Effective Linear-Scaling Direct Random Phase Approximation Method.

PubMed

Graf, Daniel; Beuerle, Matthias; Schurkus, Henry F; Luenser, Arne; Savasci, Gökcen; Ochsenfeld, Christian

2018-05-08

An efficient algorithm for calculating the random phase approximation (RPA) correlation energy is presented that is as accurate as the canonical molecular orbital resolution-of-the-identity RPA (RI-RPA) with the important advantage of an effective linear-scaling behavior (instead of quartic) for large systems due to a formulation in the local atomic orbital space. The high accuracy is achieved by utilizing optimized minimax integration schemes and the local Coulomb metric attenuated by the complementary error function for the RI approximation. The memory bottleneck of former atomic orbital (AO)-RI-RPA implementations ( Schurkus, H. F.; Ochsenfeld, C. J. Chem. Phys. 2016 , 144 , 031101 and Luenser, A.; Schurkus, H. F.; Ochsenfeld, C. J. Chem. Theory Comput. 2017 , 13 , 1647 - 1655 ) is addressed by precontraction of the large 3-center integral matrix with the Cholesky factors of the ground state density reducing the memory requirements of that matrix by a factor of [Formula: see text]. Furthermore, we present a parallel implementation of our method, which not only leads to faster RPA correlation energy calculations but also to a scalable decrease in memory requirements, opening the door for investigations of large molecules even on small- to medium-sized computing clusters. Although it is known that AO methods are highly efficient for extended systems, where sparsity allows for reaching the linear-scaling regime, we show that our work also extends the applicability when considering highly delocalized systems for which no linear scaling can be achieved. As an example, the interlayer distance of two covalent organic framework pore fragments (comprising 384 atoms in total) is analyzed.

Exploring the Ability of a Coarse-grained Potential to Describe the Stress-strain Response of Glassy Polystyrene

DTIC Science & Technology

2012-10-01

using the open-source code Large-scale Atomic/Molecular Massively Parallel Simulator ( LAMMPS ) (http://lammps.sandia.gov) (23). The commercial...parameters are proprietary and cannot be ported to the LAMMPS 4 simulation code. In our molecular dynamics simulations at the atomistic resolution, we...IBI iterative Boltzmann inversion LAMMPS Large-scale Atomic/Molecular Massively Parallel Simulator MAPS Materials Processes and Simulations MS

Big data driven cycle time parallel prediction for production planning in wafer manufacturing

NASA Astrophysics Data System (ADS)

Wang, Junliang; Yang, Jungang; Zhang, Jie; Wang, Xiaoxi; Zhang, Wenjun Chris

2018-07-01

Cycle time forecasting (CTF) is one of the most crucial issues for production planning to keep high delivery reliability in semiconductor wafer fabrication systems (SWFS). This paper proposes a novel data-intensive cycle time (CT) prediction system with parallel computing to rapidly forecast the CT of wafer lots with large datasets. First, a density peak based radial basis function network (DP-RBFN) is designed to forecast the CT with the diverse and agglomerative CT data. Second, the network learning method based on a clustering technique is proposed to determine the density peak. Third, a parallel computing approach for network training is proposed in order to speed up the training process with large scaled CT data. Finally, an experiment with respect to SWFS is presented, which demonstrates that the proposed CTF system can not only speed up the training process of the model but also outperform the radial basis function network, the back-propagation-network and multivariate regression methodology based CTF methods in terms of the mean absolute deviation and standard deviation.

HAlign-II: efficient ultra-large multiple sequence alignment and phylogenetic tree reconstruction with distributed and parallel computing.

PubMed

Wan, Shixiang; Zou, Quan

2017-01-01

Multiple sequence alignment (MSA) plays a key role in biological sequence analyses, especially in phylogenetic tree construction. Extreme increase in next-generation sequencing results in shortage of efficient ultra-large biological sequence alignment approaches for coping with different sequence types. Distributed and parallel computing represents a crucial technique for accelerating ultra-large (e.g. files more than 1 GB) sequence analyses. Based on HAlign and Spark distributed computing system, we implement a highly cost-efficient and time-efficient HAlign-II tool to address ultra-large multiple biological sequence alignment and phylogenetic tree construction. The experiments in the DNA and protein large scale data sets, which are more than 1GB files, showed that HAlign II could save time and space. It outperformed the current software tools. HAlign-II can efficiently carry out MSA and construct phylogenetic trees with ultra-large numbers of biological sequences. HAlign-II shows extremely high memory efficiency and scales well with increases in computing resource. THAlign-II provides a user-friendly web server based on our distributed computing infrastructure. HAlign-II with open-source codes and datasets was established at http://lab.malab.cn/soft/halign.

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

Chen, Chao; Pouransari, Hadi; Rajamanickam, Sivasankaran

We present a parallel hierarchical solver for general sparse linear systems on distributed-memory machines. For large-scale problems, this fully algebraic algorithm is faster and more memory-efficient than sparse direct solvers because it exploits the low-rank structure of fill-in blocks. Depending on the accuracy of low-rank approximations, the hierarchical solver can be used either as a direct solver or as a preconditioner. The parallel algorithm is based on data decomposition and requires only local communication for updating boundary data on every processor. Moreover, the computation-to-communication ratio of the parallel algorithm is approximately the volume-to-surface-area ratio of the subdomain owned by everymore » processor. We also provide various numerical results to demonstrate the versatility and scalability of the parallel algorithm.« less

NASA Exhibits

NASA Technical Reports Server (NTRS)

Deardorff, Glenn; Djomehri, M. Jahed; Freeman, Ken; Gambrel, Dave; Green, Bryan; Henze, Chris; Hinke, Thomas; Hood, Robert; Kiris, Cetin; Moran, Patrick;

Implementing a Parallel Image Edge Detection Algorithm Based on the Otsu-Canny Operator on the Hadoop Platform

PubMed Central

Wang, Min; Tian, Yun

2018-01-01

The Canny operator is widely used to detect edges in images. However, as the size of the image dataset increases, the edge detection performance of the Canny operator decreases and its runtime becomes excessive. To improve the runtime and edge detection performance of the Canny operator, in this paper, we propose a parallel design and implementation for an Otsu-optimized Canny operator using a MapReduce parallel programming model that runs on the Hadoop platform. The Otsu algorithm is used to optimize the Canny operator's dual threshold and improve the edge detection performance, while the MapReduce parallel programming model facilitates parallel processing for the Canny operator to solve the processing speed and communication cost problems that occur when the Canny edge detection algorithm is applied to big data. For the experiments, we constructed datasets of different scales from the Pascal VOC2012 image database. The proposed parallel Otsu-Canny edge detection algorithm performs better than other traditional edge detection algorithms. The parallel approach reduced the running time by approximately 67.2% on a Hadoop cluster architecture consisting of 5 nodes with a dataset of 60,000 images. Overall, our approach system speeds up the system by approximately 3.4 times when processing large-scale datasets, which demonstrates the obvious superiority of our method. The proposed algorithm in this study demonstrates both better edge detection performance and improved time performance. PMID:29861711

Reduced-Order Structure-Preserving Model for Parallel-Connected Three-Phase Grid-Tied Inverters

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

Johnson, Brian B; Purba, Victor; Jafarpour, Saber

Next-generation power networks will contain large numbers of grid-connected inverters satisfying a significant fraction of system load. Since each inverter model has a relatively large number of dynamic states, it is impractical to analyze complex system models where the full dynamics of each inverter are retained. To address this challenge, we derive a reduced-order structure-preserving model for parallel-connected grid-tied three-phase inverters. Here, each inverter in the system is assumed to have a full-bridge topology, LCL filter at the point of common coupling, and the control architecture for each inverter includes a current controller, a power controller, and a phase-locked loopmore » for grid synchronization. We outline a structure-preserving reduced-order inverter model with lumped parameters for the setting where the parallel inverters are each designed such that the filter components and controller gains scale linearly with the power rating. By structure preserving, we mean that the reduced-order three-phase inverter model is also composed of an LCL filter, a power controller, current controller, and PLL. We show that the system of parallel inverters can be modeled exactly as one aggregated inverter unit and this equivalent model has the same number of dynamical states as any individual inverter in the system. Numerical simulations validate the reduced-order model.« less

LAMMPS strong scaling performance optimization on Blue Gene/Q

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

Coffman, Paul; Jiang, Wei; Romero, Nichols A.

2014-11-12

LAMMPS "Large-scale Atomic/Molecular Massively Parallel Simulator" is an open-source molecular dynamics package from Sandia National Laboratories. Significant performance improvements in strong-scaling and time-to-solution for this application on IBM's Blue Gene/Q have been achieved through computational optimizations of the OpenMP versions of the short-range Lennard-Jones term of the CHARMM force field and the long-range Coulombic interaction implemented with the PPPM (particle-particle-particle mesh) algorithm, enhanced by runtime parameter settings controlling thread utilization. Additionally, MPI communication performance improvements were made to the PPPM calculation by re-engineering the parallel 3D FFT to use MPICH collectives instead of point-to-point. Performance testing was done using anmore » 8.4-million atom simulation scaling up to 16 racks on the Mira system at Argonne Leadership Computing Facility (ALCF). Speedups resulting from this effort were in some cases over 2x.« less

Large-Scale Modeling of Epileptic Seizures: Scaling Properties of Two Parallel Neuronal Network Simulation Algorithms

DOE PAGES

Pesce, Lorenzo L.; Lee, Hyong C.; Hereld, Mark; ...

2013-01-01

Our limited understanding of the relationship between the behavior of individual neurons and large neuronal networks is an important limitation in current epilepsy research and may be one of the main causes of our inadequate ability to treat it. Addressing this problem directly via experiments is impossibly complex; thus, we have been developing and studying medium-large-scale simulations of detailed neuronal networks to guide us. Flexibility in the connection schemas and a complete description of the cortical tissue seem necessary for this purpose. In this paper we examine some of the basic issues encountered in these multiscale simulations. We have determinedmore » the detailed behavior of two such simulators on parallel computer systems. The observed memory and computation-time scaling behavior for a distributed memory implementation were very good over the range studied, both in terms of network sizes (2,000 to 400,000 neurons) and processor pool sizes (1 to 256 processors). Our simulations required between a few megabytes and about 150 gigabytes of RAM and lasted between a few minutes and about a week, well within the capability of most multinode clusters. Therefore, simulations of epileptic seizures on networks with millions of cells should be feasible on current supercomputers.« less

Parallel computing works

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

Not Available

An account of the Caltech Concurrent Computation Program (C{sup 3}P), a five year project that focused on answering the question: Can parallel computers be used to do large-scale scientific computations '' As the title indicates, the question is answered in the affirmative, by implementing numerous scientific applications on real parallel computers and doing computations that produced new scientific results. In the process of doing so, C{sup 3}P helped design and build several new computers, designed and implemented basic system software, developed algorithms for frequently used mathematical computations on massively parallel machines, devised performance models and measured the performance of manymore » computers, and created a high performance computing facility based exclusively on parallel computers. While the initial focus of C{sup 3}P was the hypercube architecture developed by C. Seitz, many of the methods developed and lessons learned have been applied successfully on other massively parallel architectures.« less

A Parallel, Multi-Scale Watershed-Hydrologic-Inundation Model with Adaptively Switching Mesh for Capturing Flooding and Lake Dynamics

NASA Astrophysics Data System (ADS)

Ji, X.; Shen, C.

2017-12-01

Flood inundation presents substantial societal hazards and also changes biogeochemistry for systems like the Amazon. It is often expensive to simulate high-resolution flood inundation and propagation in a long-term watershed-scale model. Due to the Courant-Friedrichs-Lewy (CFL) restriction, high resolution and large local flow velocity both demand prohibitively small time steps even for parallel codes. Here we develop a parallel surface-subsurface process-based model enhanced by multi-resolution meshes that are adaptively switched on or off. The high-resolution overland flow meshes are enabled only when the flood wave invades to floodplains. This model applies semi-implicit, semi-Lagrangian (SISL) scheme in solving dynamic wave equations, and with the assistant of the multi-mesh method, it also adaptively chooses the dynamic wave equation only in the area of deep inundation. Therefore, the model achieves a balance between accuracy and computational cost.

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

Parallel/distributed direct method for solving linear systems

NASA Technical Reports Server (NTRS)

Lin, Avi

1990-01-01

A new family of parallel schemes for directly solving linear systems is presented and analyzed. It is shown that these schemes exhibit a near optimal performance and enjoy several important features: (1) For large enough linear systems, the design of the appropriate paralleled algorithm is insensitive to the number of processors as its performance grows monotonically with them; (2) It is especially good for large matrices, with dimensions large relative to the number of processors in the system; (3) It can be used in both distributed parallel computing environments and tightly coupled parallel computing systems; and (4) This set of algorithms can be mapped onto any parallel architecture without any major programming difficulties or algorithmical changes.

Lightweight and Statistical Techniques for Petascale Debugging: Correctness on Petascale Systems (CoPS) Preliminry Report

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

de Supinski, B R; Miller, B P; Liblit, B

2011-09-13

Petascale platforms with O(10{sup 5}) and O(10{sup 6}) processing cores are driving advancements in a wide range of scientific disciplines. These large systems create unprecedented application development challenges. Scalable correctness tools are critical to shorten the time-to-solution on these systems. Currently, many DOE application developers use primitive manual debugging based on printf or traditional debuggers such as TotalView or DDT. This paradigm breaks down beyond a few thousand cores, yet bugs often arise above that scale. Programmers must reproduce problems in smaller runs to analyze them with traditional tools, or else perform repeated runs at scale using only primitive techniques.more » Even when traditional tools run at scale, the approach wastes substantial effort and computation cycles. Continued scientific progress demands new paradigms for debugging large-scale applications. The Correctness on Petascale Systems (CoPS) project is developing a revolutionary debugging scheme that will reduce the debugging problem to a scale that human developers can comprehend. The scheme can provide precise diagnoses of the root causes of failure, including suggestions of the location and the type of errors down to the level of code regions or even a single execution point. Our fundamentally new strategy combines and expands three relatively new complementary debugging approaches. The Stack Trace Analysis Tool (STAT), a 2011 R&D 100 Award Winner, identifies behavior equivalence classes in MPI jobs and highlights behavior when elements of the class demonstrate divergent behavior, often the first indicator of an error. The Cooperative Bug Isolation (CBI) project has developed statistical techniques for isolating programming errors in widely deployed code that we will adapt to large-scale parallel applications. Finally, we are developing a new approach to parallelizing expensive correctness analyses, such as analysis of memory usage in the Memgrind tool. In the first two years of the project, we have successfully extended STAT to determine the relative progress of different MPI processes. We have shown that the STAT, which is now included in the debugging tools distributed by Cray with their large-scale systems, substantially reduces the scale at which traditional debugging techniques are applied. We have extended CBI to large-scale systems and developed new compiler based analyses that reduce its instrumentation overhead. Our results demonstrate that CBI can identify the source of errors in large-scale applications. Finally, we have developed MPIecho, a new technique that will reduce the time required to perform key correctness analyses, such as the detection of writes to unallocated memory. Overall, our research results are the foundations for new debugging paradigms that will improve application scientist productivity by reducing the time to determine which package or module contains the root cause of a problem that arises at all scales of our high end systems. While we have made substantial progress in the first two years of CoPS research, significant work remains. While STAT provides scalable debugging assistance for incorrect application runs, we could apply its techniques to assertions in order to observe deviations from expected behavior. Further, we must continue to refine STAT's techniques to represent behavioral equivalence classes efficiently as we expect systems with millions of threads in the next year. We are exploring new CBI techniques that can assess the likelihood that execution deviations from past behavior are the source of erroneous execution. Finally, we must develop usable correctness analyses that apply the MPIecho parallelization strategy in order to locate coding errors. We expect to make substantial progress on these directions in the next year but anticipate that significant work will remain to provide usable, scalable debugging paradigms.« less

Composing Data Parallel Code for a SPARQL Graph Engine

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

Castellana, Vito G.; Tumeo, Antonino; Villa, Oreste

Big data analytics process large amount of data to extract knowledge from them. Semantic databases are big data applications that adopt the Resource Description Framework (RDF) to structure metadata through a graph-based representation. The graph based representation provides several benefits, such as the possibility to perform in memory processing with large amounts of parallelism. SPARQL is a language used to perform queries on RDF-structured data through graph matching. In this paper we present a tool that automatically translates SPARQL queries to parallel graph crawling and graph matching operations. The tool also supports complex SPARQL constructs, which requires more than basicmore » graph matching for their implementation. The tool generates parallel code annotated with OpenMP pragmas for x86 Shared-memory Multiprocessors (SMPs). With respect to commercial database systems such as Virtuoso, our approach reduces memory occupation due to join operations and provides higher performance. We show the scaling of the automatically generated graph-matching code on a 48-core SMP.« less

Large-Scale, Parallel, Multi-Sensor Atmospheric Data Fusion Using Cloud Computing

NASA Astrophysics Data System (ADS)

Wilson, B. D.; Manipon, G.; Hua, H.; Fetzer, E.

2013-05-01

NASA's Earth Observing System (EOS) is an ambitious facility for studying global climate change. The mandate now is to combine measurements from the instruments on the "A-Train" platforms (AIRS, AMSR-E, MODIS, MISR, MLS, and CloudSat) and other Earth probes to enable large-scale studies of climate change over decades. Moving to multi-sensor, long-duration analyses of important climate variables presents serious challenges for large-scale data mining and fusion. For example, one might want to compare temperature and water vapor retrievals from one instrument (AIRS) to another (MODIS), and to a model (ECMWF), stratify the comparisons using a classification of the "cloud scenes" from CloudSat, and repeat the entire analysis over 10 years of data. To efficiently assemble such datasets, we are utilizing Elastic Computing in the Cloud and parallel map/reduce-based algorithms. However, these problems are Data Intensive computing so the data transfer times and storage costs (for caching) are key issues. SciReduce is a Hadoop-like parallel analysis system, programmed in parallel python, that is designed from the ground up for Earth science. SciReduce executes inside VMWare images and scales to any number of nodes in the Cloud. Unlike Hadoop, SciReduce operates on bundles of named numeric arrays, which can be passed in memory or serialized to disk in netCDF4 or HDF5. Figure 1 shows the architecture of the full computational system, with SciReduce at the core. Multi-year datasets are automatically "sharded" by time and space across a cluster of nodes so that years of data (millions of files) can be processed in a massively parallel way. Input variables (arrays) are pulled on-demand into the Cloud using OPeNDAP URLs or other subsetting services, thereby minimizing the size of the cached input and intermediate datasets. We are using SciReduce to automate the production of multiple versions of a ten-year A-Train water vapor climatology under a NASA MEASURES grant. We will present the architecture of SciReduce, describe the achieved "clock time" speedups in fusing datasets on our own nodes and in the Cloud, and discuss the Cloud cost tradeoffs for storage, compute, and data transfer. We will also present a concept/prototype for staging NASA's A-Train Atmospheric datasets (Levels 2 & 3) in the Amazon Cloud so that any number of compute jobs can be executed "near" the multi-sensor data. Given such a system, multi-sensor climate studies over 10-20 years of data could be performed in an efficient way, with the researcher paying only his own Cloud compute bill.; Figure 1 -- Architecture.

Large-Scale, Parallel, Multi-Sensor Atmospheric Data Fusion Using Cloud Computing

NASA Astrophysics Data System (ADS)

Wilson, B. D.; Manipon, G.; Hua, H.; Fetzer, E. J.

2013-12-01

NASA's Earth Observing System (EOS) is an ambitious facility for studying global climate change. The mandate now is to combine measurements from the instruments on the 'A-Train' platforms (AIRS, AMSR-E, MODIS, MISR, MLS, and CloudSat) and other Earth probes to enable large-scale studies of climate change over decades. Moving to multi-sensor, long-duration analyses of important climate variables presents serious challenges for large-scale data mining and fusion. For example, one might want to compare temperature and water vapor retrievals from one instrument (AIRS) to another (MODIS), and to a model (MERRA), stratify the comparisons using a classification of the 'cloud scenes' from CloudSat, and repeat the entire analysis over 10 years of data. To efficiently assemble such datasets, we are utilizing Elastic Computing in the Cloud and parallel map/reduce-based algorithms. However, these problems are Data Intensive computing so the data transfer times and storage costs (for caching) are key issues. SciReduce is a Hadoop-like parallel analysis system, programmed in parallel python, that is designed from the ground up for Earth science. SciReduce executes inside VMWare images and scales to any number of nodes in the Cloud. Unlike Hadoop, SciReduce operates on bundles of named numeric arrays, which can be passed in memory or serialized to disk in netCDF4 or HDF5. Figure 1 shows the architecture of the full computational system, with SciReduce at the core. Multi-year datasets are automatically 'sharded' by time and space across a cluster of nodes so that years of data (millions of files) can be processed in a massively parallel way. Input variables (arrays) are pulled on-demand into the Cloud using OPeNDAP URLs or other subsetting services, thereby minimizing the size of the cached input and intermediate datasets. We are using SciReduce to automate the production of multiple versions of a ten-year A-Train water vapor climatology under a NASA MEASURES grant. We will present the architecture of SciReduce, describe the achieved 'clock time' speedups in fusing datasets on our own compute nodes and in the public Cloud, and discuss the Cloud cost tradeoffs for storage, compute, and data transfer. We will also present a concept/prototype for staging NASA's A-Train Atmospheric datasets (Levels 2 & 3) in the Amazon Cloud so that any number of compute jobs can be executed 'near' the multi-sensor data. Given such a system, multi-sensor climate studies over 10-20 years of data could be performed in an efficient way, with the researcher paying only his own Cloud compute bill. SciReduce Architecture

High performance computing applications in neurobiological research

NASA Technical Reports Server (NTRS)

Ross, Muriel D.; Cheng, Rei; Doshay, David G.; Linton, Samuel W.; Montgomery, Kevin; Parnas, Bruce R.

1994-01-01

The human nervous system is a massively parallel processor of information. The vast numbers of neurons, synapses and circuits is daunting to those seeking to understand the neural basis of consciousness and intellect. Pervading obstacles are lack of knowledge of the detailed, three-dimensional (3-D) organization of even a simple neural system and the paucity of large scale, biologically relevant computer simulations. We use high performance graphics workstations and supercomputers to study the 3-D organization of gravity sensors as a prototype architecture foreshadowing more complex systems. Scaled-down simulations run on a Silicon Graphics workstation and scale-up, three-dimensional versions run on the Cray Y-MP and CM5 supercomputers.

Visual Data-Analytics of Large-Scale Parallel Discrete-Event Simulations

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

Ross, Caitlin; Carothers, Christopher D.; Mubarak, Misbah

Parallel discrete-event simulation (PDES) is an important tool in the codesign of extreme-scale systems because PDES provides a cost-effective way to evaluate designs of highperformance computing systems. Optimistic synchronization algorithms for PDES, such as Time Warp, allow events to be processed without global synchronization among the processing elements. A rollback mechanism is provided when events are processed out of timestamp order. Although optimistic synchronization protocols enable the scalability of large-scale PDES, the performance of the simulations must be tuned to reduce the number of rollbacks and provide an improved simulation runtime. To enable efficient large-scale optimistic simulations, one has tomore » gain insight into the factors that affect the rollback behavior and simulation performance. We developed a tool for ROSS model developers that gives them detailed metrics on the performance of their large-scale optimistic simulations at varying levels of simulation granularity. Model developers can use this information for parameter tuning of optimistic simulations in order to achieve better runtime and fewer rollbacks. In this work, we instrument the ROSS optimistic PDES framework to gather detailed statistics about the simulation engine. We have also developed an interactive visualization interface that uses the data collected by the ROSS instrumentation to understand the underlying behavior of the simulation engine. The interface connects real time to virtual time in the simulation and provides the ability to view simulation data at different granularities. We demonstrate the usefulness of our framework by performing a visual analysis of the dragonfly network topology model provided by the CODES simulation framework built on top of ROSS. The instrumentation needs to minimize overhead in order to accurately collect data about the simulation performance. To ensure that the instrumentation does not introduce unnecessary overhead, we perform a scaling study that compares instrumented ROSS simulations with their noninstrumented counterparts in order to determine the amount of perturbation when running at different simulation scales.« less

Debugging and Analysis of Large-Scale Parallel Programs

DTIC Science & Technology

1989-09-01

Przybylski, T. Riordan , C. Rowen, and D. Van’t Hof, "A CMOS RISC Processor with Integrated System Functions," In Proc. of the 1986 COMPCON. IEEE, March 1986...Sequencers," Communications of the ACM, 22(2):115-123, 1979. 115 [Richardson, 1988] Rick Richardson, "Dhrystone 2.1 Benchmark," Usenet Distribution

A novel milliliter-scale chemostat system for parallel cultivation of microorganisms in stirred-tank bioreactors.

PubMed

Schmideder, Andreas; Severin, Timm Steffen; Cremer, Johannes Heinrich; Weuster-Botz, Dirk

2015-09-20

A pH-controlled parallel stirred-tank bioreactor system was modified for parallel continuous cultivation on a 10 mL-scale by connecting multichannel peristaltic pumps for feeding and medium removal with micro-pipes (250 μm inner diameter). Parallel chemostat processes with Escherichia coli as an example showed high reproducibility with regard to culture volume and flow rates as well as dry cell weight, dissolved oxygen concentration and pH control at steady states (n=8, coefficient of variation <5%). Reliable estimation of kinetic growth parameters of E. coli was easily achieved within one parallel experiment by preselecting ten different steady states. Scalability of milliliter-scale steady state results was demonstrated by chemostat studies with a stirred-tank bioreactor on a liter-scale. Thus, parallel and continuously operated stirred-tank bioreactors on a milliliter-scale facilitate timesaving and cost reducing steady state studies with microorganisms. The applied continuous bioreactor system overcomes the drawbacks of existing miniaturized bioreactors, like poor mass transfer and insufficient process control. Copyright © 2015 Elsevier B.V. All rights reserved.

Final Report, DE-FG01-06ER25718 Domain Decomposition and Parallel Computing

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

Widlund, Olof B.

2015-06-09

The goal of this project is to develop and improve domain decomposition algorithms for a variety of partial differential equations such as those of linear elasticity and electro-magnetics.These iterative methods are designed for massively parallel computing systems and allow the fast solution of the very large systems of algebraic equations that arise in large scale and complicated simulations. A special emphasis is placed on problems arising from Maxwell's equation. The approximate solvers, the preconditioners, are combined with the conjugate gradient method and must always include a solver of a coarse model in order to have a performance which is independentmore » of the number of processors used in the computer simulation. A recent development allows for an adaptive construction of this coarse component of the preconditioner.« less

GENESIS: a hybrid-parallel and multi-scale molecular dynamics simulator with enhanced sampling algorithms for biomolecular and cellular simulations

PubMed Central

Jung, Jaewoon; Mori, Takaharu; Kobayashi, Chigusa; Matsunaga, Yasuhiro; Yoda, Takao; Feig, Michael; Sugita, Yuji

2015-01-01

GENESIS (Generalized-Ensemble Simulation System) is a new software package for molecular dynamics (MD) simulations of macromolecules. It has two MD simulators, called ATDYN and SPDYN. ATDYN is parallelized based on an atomic decomposition algorithm for the simulations of all-atom force-field models as well as coarse-grained Go-like models. SPDYN is highly parallelized based on a domain decomposition scheme, allowing large-scale MD simulations on supercomputers. Hybrid schemes combining OpenMP and MPI are used in both simulators to target modern multicore computer architectures. Key advantages of GENESIS are (1) the highly parallel performance of SPDYN for very large biological systems consisting of more than one million atoms and (2) the availability of various REMD algorithms (T-REMD, REUS, multi-dimensional REMD for both all-atom and Go-like models under the NVT, NPT, NPAT, and NPγT ensembles). The former is achieved by a combination of the midpoint cell method and the efficient three-dimensional Fast Fourier Transform algorithm, where the domain decomposition space is shared in real-space and reciprocal-space calculations. Other features in SPDYN, such as avoiding concurrent memory access, reducing communication times, and usage of parallel input/output files, also contribute to the performance. We show the REMD simulation results of a mixed (POPC/DMPC) lipid bilayer as a real application using GENESIS. GENESIS is released as free software under the GPLv2 licence and can be easily modified for the development of new algorithms and molecular models. WIREs Comput Mol Sci 2015, 5:310–323. doi: 10.1002/wcms.1220 PMID:26753008

A derivation and scalable implementation of the synchronous parallel kinetic Monte Carlo method for simulating long-time dynamics

NASA Astrophysics Data System (ADS)

Byun, Hye Suk; El-Naggar, Mohamed Y.; Kalia, Rajiv K.; Nakano, Aiichiro; Vashishta, Priya

2017-10-01

Kinetic Monte Carlo (KMC) simulations are used to study long-time dynamics of a wide variety of systems. Unfortunately, the conventional KMC algorithm is not scalable to larger systems, since its time scale is inversely proportional to the simulated system size. A promising approach to resolving this issue is the synchronous parallel KMC (SPKMC) algorithm, which makes the time scale size-independent. This paper introduces a formal derivation of the SPKMC algorithm based on local transition-state and time-dependent Hartree approximations, as well as its scalable parallel implementation based on a dual linked-list cell method. The resulting algorithm has achieved a weak-scaling parallel efficiency of 0.935 on 1024 Intel Xeon processors for simulating biological electron transfer dynamics in a 4.2 billion-heme system, as well as decent strong-scaling parallel efficiency. The parallel code has been used to simulate a lattice of cytochrome complexes on a bacterial-membrane nanowire, and it is broadly applicable to other problems such as computational synthesis of new materials.

An Optimization Code for Nonlinear Transient Problems of a Large Scale Multidisciplinary Mathematical Model

NASA Astrophysics Data System (ADS)

Takasaki, Koichi

This paper presents a program for the multidisciplinary optimization and identification problem of the nonlinear model of large aerospace vehicle structures. The program constructs the global matrix of the dynamic system in the time direction by the p-version finite element method (pFEM), and the basic matrix for each pFEM node in the time direction is described by a sparse matrix similarly to the static finite element problem. The algorithm used by the program does not require the Hessian matrix of the objective function and so has low memory requirements. It also has a relatively low computational cost, and is suited to parallel computation. The program was integrated as a solver module of the multidisciplinary analysis system CUMuLOUS (Computational Utility for Multidisciplinary Large scale Optimization of Undense System) which is under development by the Aerospace Research and Development Directorate (ARD) of the Japan Aerospace Exploration Agency (JAXA).

Modeling Large Scale Circuits Using Massively Parallel Descrete-Event Simulation

DTIC Science & Technology

2013-06-01

exascale levels of performance, the smallest elements of a single processor can greatly affect the entire computer system (e.g. its power consumption...grow to exascale levels of performance, the smallest elements of a single processor can greatly affect the entire computer system (e.g. its power...Warp Speed 10.0. 2.0 INTRODUCTION As supercomputer systems approach exascale , the core count will exceed 1024 and number of transistors used in

Massive parallelization of serial inference algorithms for a complex generalized linear model

PubMed Central

Suchard, Marc A.; Simpson, Shawn E.; Zorych, Ivan; Ryan, Patrick; Madigan, David

2014-01-01

Following a series of high-profile drug safety disasters in recent years, many countries are redoubling their efforts to ensure the safety of licensed medical products. Large-scale observational databases such as claims databases or electronic health record systems are attracting particular attention in this regard, but present significant methodological and computational concerns. In this paper we show how high-performance statistical computation, including graphics processing units, relatively inexpensive highly parallel computing devices, can enable complex methods in large databases. We focus on optimization and massive parallelization of cyclic coordinate descent approaches to fit a conditioned generalized linear model involving tens of millions of observations and thousands of predictors in a Bayesian context. We find orders-of-magnitude improvement in overall run-time. Coordinate descent approaches are ubiquitous in high-dimensional statistics and the algorithms we propose open up exciting new methodological possibilities with the potential to significantly improve drug safety. PMID:25328363

Scale dependence of the alignment between strain rate and rotation in turbulent shear flow

NASA Astrophysics Data System (ADS)

Fiscaletti, D.; Elsinga, G. E.; Attili, A.; Bisetti, F.; Buxton, O. R. H.

2016-10-01

The scale dependence of the statistical alignment tendencies of the eigenvectors of the strain-rate tensor ei, with the vorticity vector ω , is examined in the self-preserving region of a planar turbulent mixing layer. Data from a direct numerical simulation are filtered at various length scales and the probability density functions of the magnitude of the alignment cosines between the two unit vectors | ei.ω ̂| are examined. It is observed that the alignment tendencies are insensitive to the concurrent large-scale velocity fluctuations, but are quantitatively affected by the nature of the concurrent large-scale velocity-gradient fluctuations. It is confirmed that the small-scale (local) vorticity vector is preferentially aligned in parallel with the large-scale (background) extensive strain-rate eigenvector e1, in contrast to the global tendency for ω to be aligned in parallel with the intermediate strain-rate eigenvector [Hamlington et al., Phys. Fluids 20, 111703 (2008), 10.1063/1.3021055]. When only data from regions of the flow that exhibit strong swirling are included, the so-called high-enstrophy worms, the alignment tendencies are exaggerated with respect to the global picture. These findings support the notion that the production of enstrophy, responsible for a net cascade of turbulent kinetic energy from large scales to small scales, is driven by vorticity stretching due to the preferential parallel alignment between ω and nonlocal e1 and that the strongly swirling worms are kinematically significant to this process.

Scalable Evaluation of Polarization Energy and Associated Forces in Polarizable Molecular Dynamics: II.Towards Massively Parallel Computations using Smooth Particle Mesh Ewald.

PubMed

Lagardère, Louis; Lipparini, Filippo; Polack, Étienne; Stamm, Benjamin; Cancès, Éric; Schnieders, Michael; Ren, Pengyu; Maday, Yvon; Piquemal, Jean-Philip

2014-02-28

In this paper, we present a scalable and efficient implementation of point dipole-based polarizable force fields for molecular dynamics (MD) simulations with periodic boundary conditions (PBC). The Smooth Particle-Mesh Ewald technique is combined with two optimal iterative strategies, namely, a preconditioned conjugate gradient solver and a Jacobi solver in conjunction with the Direct Inversion in the Iterative Subspace for convergence acceleration, to solve the polarization equations. We show that both solvers exhibit very good parallel performances and overall very competitive timings in an energy-force computation needed to perform a MD step. Various tests on large systems are provided in the context of the polarizable AMOEBA force field as implemented in the newly developed Tinker-HP package which is the first implementation for a polarizable model making large scale experiments for massively parallel PBC point dipole models possible. We show that using a large number of cores offers a significant acceleration of the overall process involving the iterative methods within the context of spme and a noticeable improvement of the memory management giving access to very large systems (hundreds of thousands of atoms) as the algorithm naturally distributes the data on different cores. Coupled with advanced MD techniques, gains ranging from 2 to 3 orders of magnitude in time are now possible compared to non-optimized, sequential implementations giving new directions for polarizable molecular dynamics in periodic boundary conditions using massively parallel implementations.

Scalable Evaluation of Polarization Energy and Associated Forces in Polarizable Molecular Dynamics: II.Towards Massively Parallel Computations using Smooth Particle Mesh Ewald

PubMed Central

Lagardère, Louis; Lipparini, Filippo; Polack, Étienne; Stamm, Benjamin; Cancès, Éric; Schnieders, Michael; Ren, Pengyu; Maday, Yvon; Piquemal, Jean-Philip

2015-01-01

In this paper, we present a scalable and efficient implementation of point dipole-based polarizable force fields for molecular dynamics (MD) simulations with periodic boundary conditions (PBC). The Smooth Particle-Mesh Ewald technique is combined with two optimal iterative strategies, namely, a preconditioned conjugate gradient solver and a Jacobi solver in conjunction with the Direct Inversion in the Iterative Subspace for convergence acceleration, to solve the polarization equations. We show that both solvers exhibit very good parallel performances and overall very competitive timings in an energy-force computation needed to perform a MD step. Various tests on large systems are provided in the context of the polarizable AMOEBA force field as implemented in the newly developed Tinker-HP package which is the first implementation for a polarizable model making large scale experiments for massively parallel PBC point dipole models possible. We show that using a large number of cores offers a significant acceleration of the overall process involving the iterative methods within the context of spme and a noticeable improvement of the memory management giving access to very large systems (hundreds of thousands of atoms) as the algorithm naturally distributes the data on different cores. Coupled with advanced MD techniques, gains ranging from 2 to 3 orders of magnitude in time are now possible compared to non-optimized, sequential implementations giving new directions for polarizable molecular dynamics in periodic boundary conditions using massively parallel implementations. PMID:26512230

Research of the effectiveness of parallel multithreaded realizations of interpolation methods for scaling raster images

NASA Astrophysics Data System (ADS)

Vnukov, A. A.; Shershnev, M. B.

2018-01-01

The aim of this work is the software implementation of three image scaling algorithms using parallel computations, as well as the development of an application with a graphical user interface for the Windows operating system to demonstrate the operation of algorithms and to study the relationship between system performance, algorithm execution time and the degree of parallelization of computations. Three methods of interpolation were studied, formalized and adapted to scale images. The result of the work is a program for scaling images by different methods. Comparison of the quality of scaling by different methods is given.

Taming parallel I/O complexity with auto-tuning

DOE PAGES

Behzad, Babak; Luu, Huong Vu Thanh; Huchette, Joseph; ...

2013-11-17

We present an auto-tuning system for optimizing I/O performance of HDF5 applications and demonstrate its value across platforms, applications, and at scale. The system uses a genetic algorithm to search a large space of tunable parameters and to identify effective settings at all layers of the parallel I/O stack. The parameter settings are applied transparently by the auto-tuning system via dynamically intercepted HDF5 calls. To validate our auto-tuning system, we applied it to three I/O benchmarks (VPIC, VORPAL, and GCRM) that replicate the I/O activity of their respective applications. We tested the system with different weak-scaling configurations (128, 2048, andmore » 4096 CPU cores) that generate 30 GB to 1 TB of data, and executed these configurations on diverse HPC platforms (Cray XE6, IBM BG/P, and Dell Cluster). In all cases, the auto-tuning framework identified tunable parameters that substantially improved write performance over default system settings. In conclusion, we consistently demonstrate I/O write speedups between 2x and 100x for test configurations.« less

Using parallel banded linear system solvers in generalized eigenvalue problems

NASA Technical Reports Server (NTRS)

Zhang, Hong; Moss, William F.

1993-01-01

Subspace iteration is a reliable and cost effective method for solving positive definite banded symmetric generalized eigenproblems, especially in the case of large scale problems. This paper discusses an algorithm that makes use of two parallel banded solvers in subspace iteration. A shift is introduced to decompose the banded linear systems into relatively independent subsystems and to accelerate the iterations. With this shift, an eigenproblem is mapped efficiently into the memories of a multiprocessor and a high speed-up is obtained for parallel implementations. An optimal shift is a shift that balances total computation and communication costs. Under certain conditions, we show how to estimate an optimal shift analytically using the decay rate for the inverse of a banded matrix, and how to improve this estimate. Computational results on iPSC/2 and iPSC/860 multiprocessors are presented.

Parallel Domain Decomposition Formulation and Software for Large-Scale Sparse Symmetrical/Unsymmetrical Aeroacoustic Applications

NASA Technical Reports Server (NTRS)

Nguyen, D. T.; Watson, Willie R. (Technical Monitor)

2005-01-01

The overall objectives of this research work are to formulate and validate efficient parallel algorithms, and to efficiently design/implement computer software for solving large-scale acoustic problems, arised from the unified frameworks of the finite element procedures. The adopted parallel Finite Element (FE) Domain Decomposition (DD) procedures should fully take advantages of multiple processing capabilities offered by most modern high performance computing platforms for efficient parallel computation. To achieve this objective. the formulation needs to integrate efficient sparse (and dense) assembly techniques, hybrid (or mixed) direct and iterative equation solvers, proper pre-conditioned strategies, unrolling strategies, and effective processors' communicating schemes. Finally, the numerical performance of the developed parallel finite element procedures will be evaluated by solving series of structural, and acoustic (symmetrical and un-symmetrical) problems (in different computing platforms). Comparisons with existing "commercialized" and/or "public domain" software are also included, whenever possible.

Argonne simulation framework for intelligent transportation systems

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

Ewing, T.; Doss, E.; Hanebutte, U.

1996-04-01

A simulation framework has been developed which defines a high-level architecture for a large-scale, comprehensive, scalable simulation of an Intelligent Transportation System (ITS). The simulator is designed to run on parallel computers and distributed (networked) computer systems; however, a version for a stand alone workstation is also available. The ITS simulator includes an Expert Driver Model (EDM) of instrumented ``smart`` vehicles with in-vehicle navigation units. The EDM is capable of performing optimal route planning and communicating with Traffic Management Centers (TMC). A dynamic road map data base is sued for optimum route planning, where the data is updated periodically tomore » reflect any changes in road or weather conditions. The TMC has probe vehicle tracking capabilities (display position and attributes of instrumented vehicles), and can provide 2-way interaction with traffic to provide advisories and link times. Both the in-vehicle navigation module and the TMC feature detailed graphical user interfaces that includes human-factors studies to support safety and operational research. Realistic modeling of variations of the posted driving speed are based on human factor studies that take into consideration weather, road conditions, driver`s personality and behavior and vehicle type. The simulator has been developed on a distributed system of networked UNIX computers, but is designed to run on ANL`s IBM SP-X parallel computer system for large scale problems. A novel feature of the developed simulator is that vehicles will be represented by autonomous computer processes, each with a behavior model which performs independent route selection and reacts to external traffic events much like real vehicles. Vehicle processes interact with each other and with ITS components by exchanging messages. With this approach, one will be able to take advantage of emerging massively parallel processor (MPP) systems.« less

Accelerating large-scale protein structure alignments with graphics processing units

PubMed Central

2012-01-01

Background Large-scale protein structure alignment, an indispensable tool to structural bioinformatics, poses a tremendous challenge on computational resources. To ensure structure alignment accuracy and efficiency, efforts have been made to parallelize traditional alignment algorithms in grid environments. However, these solutions are costly and of limited accessibility. Others trade alignment quality for speedup by using high-level characteristics of structure fragments for structure comparisons. Findings We present ppsAlign, a parallel protein structure Alignment framework designed and optimized to exploit the parallelism of Graphics Processing Units (GPUs). As a general-purpose GPU platform, ppsAlign could take many concurrent methods, such as TM-align and Fr-TM-align, into the parallelized algorithm design. We evaluated ppsAlign on an NVIDIA Tesla C2050 GPU card, and compared it with existing software solutions running on an AMD dual-core CPU. We observed a 36-fold speedup over TM-align, a 65-fold speedup over Fr-TM-align, and a 40-fold speedup over MAMMOTH. Conclusions ppsAlign is a high-performance protein structure alignment tool designed to tackle the computational complexity issues from protein structural data. The solution presented in this paper allows large-scale structure comparisons to be performed using massive parallel computing power of GPU. PMID:22357132

An O(N) and parallel approach to integral problems by a kernel-independent fast multipole method: Application to polarization and magnetization of interacting particles

NASA Astrophysics Data System (ADS)

Jiang, Xikai; Li, Jiyuan; Zhao, Xujun; Qin, Jian; Karpeev, Dmitry; Hernandez-Ortiz, Juan; de Pablo, Juan J.; Heinonen, Olle

2016-08-01

Large classes of materials systems in physics and engineering are governed by magnetic and electrostatic interactions. Continuum or mesoscale descriptions of such systems can be cast in terms of integral equations, whose direct computational evaluation requires O(N2) operations, where N is the number of unknowns. Such a scaling, which arises from the many-body nature of the relevant Green's function, has precluded wide-spread adoption of integral methods for solution of large-scale scientific and engineering problems. In this work, a parallel computational approach is presented that relies on using scalable open source libraries and utilizes a kernel-independent Fast Multipole Method (FMM) to evaluate the integrals in O(N) operations, with O(N) memory cost, thereby substantially improving the scalability and efficiency of computational integral methods. We demonstrate the accuracy, efficiency, and scalability of our approach in the context of two examples. In the first, we solve a boundary value problem for a ferroelectric/ferromagnetic volume in free space. In the second, we solve an electrostatic problem involving polarizable dielectric bodies in an unbounded dielectric medium. The results from these test cases show that our proposed parallel approach, which is built on a kernel-independent FMM, can enable highly efficient and accurate simulations and allow for considerable flexibility in a broad range of applications.

An O( N) and parallel approach to integral problems by a kernel-independent fast multipole method: Application to polarization and magnetization of interacting particles

DOE PAGES

Jiang, Xikai; Li, Jiyuan; Zhao, Xujun; ...

2016-08-10

Large classes of materials systems in physics and engineering are governed by magnetic and electrostatic interactions. Continuum or mesoscale descriptions of such systems can be cast in terms of integral equations, whose direct computational evaluation requires O( N 2) operations, where N is the number of unknowns. Such a scaling, which arises from the many-body nature of the relevant Green's function, has precluded wide-spread adoption of integral methods for solution of large-scale scientific and engineering problems. In this work, a parallel computational approach is presented that relies on using scalable open source libraries and utilizes a kernel-independent Fast Multipole Methodmore » (FMM) to evaluate the integrals in O( N) operations, with O( N) memory cost, thereby substantially improving the scalability and efficiency of computational integral methods. We demonstrate the accuracy, efficiency, and scalability of our approach in the context of two examples. In the first, we solve a boundary value problem for a ferroelectric/ferromagnetic volume in free space. In the second, we solve an electrostatic problem involving polarizable dielectric bodies in an unbounded dielectric medium. Lastly, the results from these test cases show that our proposed parallel approach, which is built on a kernel-independent FMM, can enable highly efficient and accurate simulations and allow for considerable flexibility in a broad range of applications.« less

Multilevel summation method for electrostatic force evaluation.

PubMed

Hardy, David J; Wu, Zhe; Phillips, James C; Stone, John E; Skeel, Robert D; Schulten, Klaus

2015-02-10

The multilevel summation method (MSM) offers an efficient algorithm utilizing convolution for evaluating long-range forces arising in molecular dynamics simulations. Shifting the balance of computation and communication, MSM provides key advantages over the ubiquitous particle–mesh Ewald (PME) method, offering better scaling on parallel computers and permitting more modeling flexibility, with support for periodic systems as does PME but also for semiperiodic and nonperiodic systems. The version of MSM available in the simulation program NAMD is described, and its performance and accuracy are compared with the PME method. The accuracy feasible for MSM in practical applications reproduces PME results for water property calculations of density, diffusion constant, dielectric constant, surface tension, radial distribution function, and distance-dependent Kirkwood factor, even though the numerical accuracy of PME is higher than that of MSM. Excellent agreement between MSM and PME is found also for interface potentials of air–water and membrane–water interfaces, where long-range Coulombic interactions are crucial. Applications demonstrate also the suitability of MSM for systems with semiperiodic and nonperiodic boundaries. For this purpose, simulations have been performed with periodic boundaries along directions parallel to a membrane surface but not along the surface normal, yielding membrane pore formation induced by an imbalance of charge across the membrane. Using a similar semiperiodic boundary condition, ion conduction through a graphene nanopore driven by an ion gradient has been simulated. Furthermore, proteins have been simulated inside a single spherical water droplet. Finally, parallel scalability results show the ability of MSM to outperform PME when scaling a system of modest size (less than 100 K atoms) to over a thousand processors, demonstrating the suitability of MSM for large-scale parallel simulation.

McrEngine: A Scalable Checkpointing System Using Data-Aware Aggregation and Compression

DOE PAGES

Islam, Tanzima Zerin; Mohror, Kathryn; Bagchi, Saurabh; ...

2013-01-01

High performance computing (HPC) systems use checkpoint-restart to tolerate failures. Typically, applications store their states in checkpoints on a parallel file system (PFS). As applications scale up, checkpoint-restart incurs high overheads due to contention for PFS resources. The high overheads force large-scale applications to reduce checkpoint frequency, which means more compute time is lost in the event of failure. We alleviate this problem through a scalable checkpoint-restart system, mcrEngine. McrEngine aggregates checkpoints from multiple application processes with knowledge of the data semantics available through widely-used I/O libraries, e.g., HDF5 and netCDF, and compresses them. Our novel scheme improves compressibility ofmore » checkpoints up to 115% over simple concatenation and compression. Our evaluation with large-scale application checkpoints show that mcrEngine reduces checkpointing overhead by up to 87% and restart overhead by up to 62% over a baseline with no aggregation or compression.« less

Petascale Simulation Initiative Tech Base: FY2007 Final Report

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

May, J; Chen, R; Jefferson, D

The Petascale Simulation Initiative began as an LDRD project in the middle of Fiscal Year 2004. The goal of the project was to develop techniques to allow large-scale scientific simulation applications to better exploit the massive parallelism that will come with computers running at petaflops per second. One of the major products of this work was the design and prototype implementation of a programming model and a runtime system that lets applications extend data-parallel applications to use task parallelism. By adopting task parallelism, applications can use processing resources more flexibly, exploit multiple forms of parallelism, and support more sophisticated multiscalemore » and multiphysics models. Our programming model was originally called the Symponents Architecture but is now known as Cooperative Parallelism, and the runtime software that supports it is called Coop. (However, we sometimes refer to the programming model as Coop for brevity.) We have documented the programming model and runtime system in a submitted conference paper [1]. This report focuses on the specific accomplishments of the Cooperative Parallelism project (as we now call it) under Tech Base funding in FY2007. Development and implementation of the model under LDRD funding alone proceeded to the point of demonstrating a large-scale materials modeling application using Coop on more than 1300 processors by the end of FY2006. Beginning in FY2007, the project received funding from both LDRD and the Computation Directorate Tech Base program. Later in the year, after the three-year term of the LDRD funding ended, the ASC program supported the project with additional funds. The goal of the Tech Base effort was to bring Coop from a prototype to a production-ready system that a variety of LLNL users could work with. Specifically, the major tasks that we planned for the project were: (1) Port SARS [former name of the Coop runtime system] to another LLNL platform, probably Thunder or Peloton (depending on when Peloton becomes available); (2) Improve SARS's robustness and ease-of-use, and develop user documentation; and (3) Work with LLNL code teams to help them determine how Symponents could benefit their applications. The original funding request was $296,000 for the year, and we eventually received $252,000. The remainder of this report describes our efforts and accomplishments for each of the goals listed above.« less

The TeraShake Computational Platform for Large-Scale Earthquake Simulations

NASA Astrophysics Data System (ADS)

Cui, Yifeng; Olsen, Kim; Chourasia, Amit; Moore, Reagan; Maechling, Philip; Jordan, Thomas

Geoscientific and computer science researchers with the Southern California Earthquake Center (SCEC) are conducting a large-scale, physics-based, computationally demanding earthquake system science research program with the goal of developing predictive models of earthquake processes. The computational demands of this program continue to increase rapidly as these researchers seek to perform physics-based numerical simulations of earthquake processes for larger meet the needs of this research program, a multiple-institution team coordinated by SCEC has integrated several scientific codes into a numerical modeling-based research tool we call the TeraShake computational platform (TSCP). A central component in the TSCP is a highly scalable earthquake wave propagation simulation program called the TeraShake anelastic wave propagation (TS-AWP) code. In this chapter, we describe how we extended an existing, stand-alone, wellvalidated, finite-difference, anelastic wave propagation modeling code into the highly scalable and widely used TS-AWP and then integrated this code into the TeraShake computational platform that provides end-to-end (initialization to analysis) research capabilities. We also describe the techniques used to enhance the TS-AWP parallel performance on TeraGrid supercomputers, as well as the TeraShake simulations phases including input preparation, run time, data archive management, and visualization. As a result of our efforts to improve its parallel efficiency, the TS-AWP has now shown highly efficient strong scaling on over 40K processors on IBM’s BlueGene/L Watson computer. In addition, the TSCP has developed into a computational system that is useful to many members of the SCEC community for performing large-scale earthquake simulations.

Oak Ridge Leadership Computing Facility Position Paper

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

Oral, H Sarp; Hill, Jason J; Thach, Kevin G

This paper discusses the business, administration, reliability, and usability aspects of storage systems at the Oak Ridge Leadership Computing Facility (OLCF). The OLCF has developed key competencies in architecting and administration of large-scale Lustre deployments as well as HPSS archival systems. Additionally as these systems are architected, deployed, and expanded over time reliability and availability factors are a primary driver. This paper focuses on the implementation of the Spider parallel Lustre file system as well as the implementation of the HPSS archive at the OLCF.

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

Load Balancing Strategies for Multi-Block Overset Grid Applications

NASA Technical Reports Server (NTRS)

Djomehri, M. Jahed; Biswas, Rupak; Lopez-Benitez, Noe; Biegel, Bryan (Technical Monitor)

2002-01-01

The multi-block overset grid method is a powerful technique for high-fidelity computational fluid dynamics (CFD) simulations about complex aerospace configurations. The solution process uses a grid system that discretizes the problem domain by using separately generated but overlapping structured grids that periodically update and exchange boundary information through interpolation. For efficient high performance computations of large-scale realistic applications using this methodology, the individual grids must be properly partitioned among the parallel processors. Overall performance, therefore, largely depends on the quality of load balancing. In this paper, we present three different load balancing strategies far overset grids and analyze their effects on the parallel efficiency of a Navier-Stokes CFD application running on an SGI Origin2000 machine.

National Laboratory for Advanced Scientific Visualization at UNAM - Mexico

NASA Astrophysics Data System (ADS)

Manea, Marina; Constantin Manea, Vlad; Varela, Alfredo

2016-04-01

In 2015, the National Autonomous University of Mexico (UNAM) joined the family of Universities and Research Centers where advanced visualization and computing plays a key role to promote and advance missions in research, education, community outreach, as well as business-oriented consulting. This initiative provides access to a great variety of advanced hardware and software resources and offers a range of consulting services that spans a variety of areas related to scientific visualization, among which are: neuroanatomy, embryonic development, genome related studies, geosciences, geography, physics and mathematics related disciplines. The National Laboratory for Advanced Scientific Visualization delivers services through three main infrastructure environments: the 3D fully immersive display system Cave, the high resolution parallel visualization system Powerwall, the high resolution spherical displays Earth Simulator. The entire visualization infrastructure is interconnected to a high-performance-computing-cluster (HPCC) called ADA in honor to Ada Lovelace, considered to be the first computer programmer. The Cave is an extra large 3.6m wide room with projected images on the front, left and right, as well as floor walls. Specialized crystal eyes LCD-shutter glasses provide a strong stereo depth perception, and a variety of tracking devices allow software to track the position of a user's hand, head and wand. The Powerwall is designed to bring large amounts of complex data together through parallel computing for team interaction and collaboration. This system is composed by 24 (6x4) high-resolution ultra-thin (2 mm) bezel monitors connected to a high-performance GPU cluster. The Earth Simulator is a large (60") high-resolution spherical display used for global-scale data visualization like geophysical, meteorological, climate and ecology data. The HPCC-ADA, is a 1000+ computing core system, which offers parallel computing resources to applications that requires large quantity of memory as well as large and fast parallel storage systems. The entire system temperature is controlled by an energy and space efficient cooling solution, based on large rear door liquid cooled heat exchangers. This state-of-the-art infrastructure will boost research activities in the region, offer a powerful scientific tool for teaching at undergraduate and graduate levels, and enhance association and cooperation with business-oriented organizations.

Summer Proceedings 2016: The Center for Computing Research at Sandia National Laboratories

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

Carleton, James Brian; Parks, Michael L.

Solving sparse linear systems from the discretization of elliptic partial differential equations (PDEs) is an important building block in many engineering applications. Sparse direct solvers can solve general linear systems, but are usually slower and use much more memory than effective iterative solvers. To overcome these two disadvantages, a hierarchical solver (LoRaSp) based on H2-matrices was introduced in [22]. Here, we have developed a parallel version of the algorithm in LoRaSp to solve large sparse matrices on distributed memory machines. On a single processor, the factorization time of our parallel solver scales almost linearly with the problem size for three-dimensionalmore » problems, as opposed to the quadratic scalability of many existing sparse direct solvers. Moreover, our solver leads to almost constant numbers of iterations, when used as a preconditioner for Poisson problems. On more than one processor, our algorithm has significant speedups compared to sequential runs. With this parallel algorithm, we are able to solve large problems much faster than many existing packages as demonstrated by the numerical experiments.« less

Parallel Finite Element Domain Decomposition for Structural/Acoustic Analysis

NASA Technical Reports Server (NTRS)

Nguyen, Duc T.; Tungkahotara, Siroj; Watson, Willie R.; Rajan, Subramaniam D.

2005-01-01

A domain decomposition (DD) formulation for solving sparse linear systems of equations resulting from finite element analysis is presented. The formulation incorporates mixed direct and iterative equation solving strategics and other novel algorithmic ideas that are optimized to take advantage of sparsity and exploit modern computer architecture, such as memory and parallel computing. The most time consuming part of the formulation is identified and the critical roles of direct sparse and iterative solvers within the framework of the formulation are discussed. Experiments on several computer platforms using several complex test matrices are conducted using software based on the formulation. Small-scale structural examples are used to validate thc steps in the formulation and large-scale (l,000,000+ unknowns) duct acoustic examples are used to evaluate the ORIGIN 2000 processors, and a duster of 6 PCs (running under the Windows environment). Statistics show that the formulation is efficient in both sequential and parallel computing environmental and that the formulation is significantly faster and consumes less memory than that based on one of the best available commercialized parallel sparse solvers.

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.

BioFVM: an efficient, parallelized diffusive transport solver for 3-D biological simulations

PubMed Central

Ghaffarizadeh, Ahmadreza; Friedman, Samuel H.; Macklin, Paul

2016-01-01

Motivation: Computational models of multicellular systems require solving systems of PDEs for release, uptake, decay and diffusion of multiple substrates in 3D, particularly when incorporating the impact of drugs, growth substrates and signaling factors on cell receptors and subcellular systems biology. Results: We introduce BioFVM, a diffusive transport solver tailored to biological problems. BioFVM can simulate release and uptake of many substrates by cell and bulk sources, diffusion and decay in large 3D domains. It has been parallelized with OpenMP, allowing efficient simulations on desktop workstations or single supercomputer nodes. The code is stable even for large time steps, with linear computational cost scalings. Solutions are first-order accurate in time and second-order accurate in space. The code can be run by itself or as part of a larger simulator. Availability and implementation: BioFVM is written in C ++ with parallelization in OpenMP. It is maintained and available for download at http://BioFVM.MathCancer.org and http://BioFVM.sf.net under the Apache License (v2.0). Contact: paul.macklin@usc.edu. Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26656933

Parallel STEPS: Large Scale Stochastic Spatial Reaction-Diffusion Simulation with High Performance Computers

PubMed Central

Chen, Weiliang; De Schutter, Erik

2017-01-01

Stochastic, spatial reaction-diffusion simulations have been widely used in systems biology and computational neuroscience. However, the increasing scale and complexity of models and morphologies have exceeded the capacity of any serial implementation. This led to the development of parallel solutions that benefit from the boost in performance of modern supercomputers. In this paper, we describe an MPI-based, parallel operator-splitting implementation for stochastic spatial reaction-diffusion simulations with irregular tetrahedral meshes. The performance of our implementation is first examined and analyzed with simulations of a simple model. We then demonstrate its application to real-world research by simulating the reaction-diffusion components of a published calcium burst model in both Purkinje neuron sub-branch and full dendrite morphologies. Simulation results indicate that our implementation is capable of achieving super-linear speedup for balanced loading simulations with reasonable molecule density and mesh quality. In the best scenario, a parallel simulation with 2,000 processes runs more than 3,600 times faster than its serial SSA counterpart, and achieves more than 20-fold speedup relative to parallel simulation with 100 processes. In a more realistic scenario with dynamic calcium influx and data recording, the parallel simulation with 1,000 processes and no load balancing is still 500 times faster than the conventional serial SSA simulation. PMID:28239346

Parallel STEPS: Large Scale Stochastic Spatial Reaction-Diffusion Simulation with High Performance Computers.

PubMed

Chen, Weiliang; De Schutter, Erik

2017-01-01

Stochastic, spatial reaction-diffusion simulations have been widely used in systems biology and computational neuroscience. However, the increasing scale and complexity of models and morphologies have exceeded the capacity of any serial implementation. This led to the development of parallel solutions that benefit from the boost in performance of modern supercomputers. In this paper, we describe an MPI-based, parallel operator-splitting implementation for stochastic spatial reaction-diffusion simulations with irregular tetrahedral meshes. The performance of our implementation is first examined and analyzed with simulations of a simple model. We then demonstrate its application to real-world research by simulating the reaction-diffusion components of a published calcium burst model in both Purkinje neuron sub-branch and full dendrite morphologies. Simulation results indicate that our implementation is capable of achieving super-linear speedup for balanced loading simulations with reasonable molecule density and mesh quality. In the best scenario, a parallel simulation with 2,000 processes runs more than 3,600 times faster than its serial SSA counterpart, and achieves more than 20-fold speedup relative to parallel simulation with 100 processes. In a more realistic scenario with dynamic calcium influx and data recording, the parallel simulation with 1,000 processes and no load balancing is still 500 times faster than the conventional serial SSA simulation.

Final Scientific Report: A Scalable Development Environment for Peta-Scale Computing

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

Karbach, Carsten; Frings, Wolfgang

2013-02-22

This document is the final scientific report of the project DE-SC000120 (A scalable Development Environment for Peta-Scale Computing). The objective of this project is the extension of the Parallel Tools Platform (PTP) for applying it to peta-scale systems. PTP is an integrated development environment for parallel applications. It comprises code analysis, performance tuning, parallel debugging and system monitoring. The contribution of the Juelich Supercomputing Centre (JSC) aims to provide a scalable solution for system monitoring of supercomputers. This includes the development of a new communication protocol for exchanging status data between the target remote system and the client running PTP.more » The communication has to work for high latency. PTP needs to be implemented robustly and should hide the complexity of the supercomputer's architecture in order to provide a transparent access to various remote systems via a uniform user interface. This simplifies the porting of applications to different systems, because PTP functions as abstraction layer between parallel application developer and compute resources. The common requirement for all PTP components is that they have to interact with the remote supercomputer. E.g. applications are built remotely and performance tools are attached to job submissions and their output data resides on the remote system. Status data has to be collected by evaluating outputs of the remote job scheduler and the parallel debugger needs to control an application executed on the supercomputer. The challenge is to provide this functionality for peta-scale systems in real-time. The client server architecture of the established monitoring application LLview, developed by the JSC, can be applied to PTP's system monitoring. LLview provides a well-arranged overview of the supercomputer's current status. A set of statistics, a list of running and queued jobs as well as a node display mapping running jobs to their compute resources form the user display of LLview. These monitoring features have to be integrated into the development environment. Besides showing the current status PTP's monitoring also needs to allow for submitting and canceling user jobs. Monitoring peta-scale systems especially deals with presenting the large amount of status data in a useful manner. Users require to select arbitrary levels of detail. The monitoring views have to provide a quick overview of the system state, but also need to allow for zooming into specific parts of the system, into which the user is interested in. At present, the major batch systems running on supercomputers are PBS, TORQUE, ALPS and LoadLeveler, which have to be supported by both the monitoring and the job controlling component. Finally, PTP needs to be designed as generic as possible, so that it can be extended for future batch systems.« less

Parallel Index and Query for Large Scale Data Analysis

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

Chou, Jerry; Wu, Kesheng; Ruebel, Oliver

2011-07-18

Modern scientific datasets present numerous data management and analysis challenges. State-of-the-art index and query technologies are critical for facilitating interactive exploration of large datasets, but numerous challenges remain in terms of designing a system for process- ing general scientific datasets. The system needs to be able to run on distributed multi-core platforms, efficiently utilize underlying I/O infrastructure, and scale to massive datasets. We present FastQuery, a novel software framework that address these challenges. FastQuery utilizes a state-of-the-art index and query technology (FastBit) and is designed to process mas- sive datasets on modern supercomputing platforms. We apply FastQuery to processing ofmore » a massive 50TB dataset generated by a large scale accelerator modeling code. We demonstrate the scalability of the tool to 11,520 cores. Motivated by the scientific need to search for inter- esting particles in this dataset, we use our framework to reduce search time from hours to tens of seconds.« less

A Dual Super-Element Domain Decomposition Approach for Parallel Nonlinear Finite Element Analysis

NASA Astrophysics Data System (ADS)

Jokhio, G. A.; Izzuddin, B. A.

2015-05-01

This article presents a new domain decomposition method for nonlinear finite element analysis introducing the concept of dual partition super-elements. The method extends ideas from the displacement frame method and is ideally suited for parallel nonlinear static/dynamic analysis of structural systems. In the new method, domain decomposition is realized by replacing one or more subdomains in a "parent system," each with a placeholder super-element, where the subdomains are processed separately as "child partitions," each wrapped by a dual super-element along the partition boundary. The analysis of the overall system, including the satisfaction of equilibrium and compatibility at all partition boundaries, is realized through direct communication between all pairs of placeholder and dual super-elements. The proposed method has particular advantages for matrix solution methods based on the frontal scheme, and can be readily implemented for existing finite element analysis programs to achieve parallelization on distributed memory systems with minimal intervention, thus overcoming memory bottlenecks typically faced in the analysis of large-scale problems. Several examples are presented in this article which demonstrate the computational benefits of the proposed parallel domain decomposition approach and its applicability to the nonlinear structural analysis of realistic structural systems.

Implementation and performance of FDPS: a framework for developing parallel particle simulation codes

NASA Astrophysics Data System (ADS)

Iwasawa, Masaki; Tanikawa, Ataru; Hosono, Natsuki; Nitadori, Keigo; Muranushi, Takayuki; Makino, Junichiro

2016-08-01

We present the basic idea, implementation, measured performance, and performance model of FDPS (Framework for Developing Particle Simulators). FDPS is an application-development framework which helps researchers to develop simulation programs using particle methods for large-scale distributed-memory parallel supercomputers. A particle-based simulation program for distributed-memory parallel computers needs to perform domain decomposition, exchange of particles which are not in the domain of each computing node, and gathering of the particle information in other nodes which are necessary for interaction calculation. Also, even if distributed-memory parallel computers are not used, in order to reduce the amount of computation, algorithms such as the Barnes-Hut tree algorithm or the Fast Multipole Method should be used in the case of long-range interactions. For short-range interactions, some methods to limit the calculation to neighbor particles are required. FDPS provides all of these functions which are necessary for efficient parallel execution of particle-based simulations as "templates," which are independent of the actual data structure of particles and the functional form of the particle-particle interaction. By using FDPS, researchers can write their programs with the amount of work necessary to write a simple, sequential and unoptimized program of O(N2) calculation cost, and yet the program, once compiled with FDPS, will run efficiently on large-scale parallel supercomputers. A simple gravitational N-body program can be written in around 120 lines. We report the actual performance of these programs and the performance model. The weak scaling performance is very good, and almost linear speed-up was obtained for up to the full system of the K computer. The minimum calculation time per timestep is in the range of 30 ms (N = 107) to 300 ms (N = 109). These are currently limited by the time for the calculation of the domain decomposition and communication necessary for the interaction calculation. We discuss how we can overcome these bottlenecks.

Large-Scale, Parallel, Multi-Sensor Data Fusion in the Cloud

NASA Astrophysics Data System (ADS)

Wilson, B. D.; Manipon, G.; Hua, H.

2012-12-01

NASA's Earth Observing System (EOS) is an ambitious facility for studying global climate change. The mandate now is to combine measurements from the instruments on the "A-Train" platforms (AIRS, AMSR-E, MODIS, MISR, MLS, and CloudSat) and other Earth probes to enable large-scale studies of climate change over periods of years to decades. However, moving from predominantly single-instrument studies to a multi-sensor, measurement-based model for long-duration analysis of important climate variables presents serious challenges for large-scale data mining and data fusion. For example, one might want to compare temperature and water vapor retrievals from one instrument (AIRS) to another instrument (MODIS), and to a model (ECMWF), stratify the comparisons using a classification of the "cloud scenes" from CloudSat, and repeat the entire analysis over years of AIRS data. To perform such an analysis, one must discover & access multiple datasets from remote sites, find the space/time "matchups" between instruments swaths and model grids, understand the quality flags and uncertainties for retrieved physical variables, assemble merged datasets, and compute fused products for further scientific and statistical analysis. To efficiently assemble such decade-scale datasets in a timely manner, we are utilizing Elastic Computing in the Cloud and parallel map/reduce-based algorithms. "SciReduce" is a Hadoop-like parallel analysis system, programmed in parallel python, that is designed from the ground up for Earth science. SciReduce executes inside VMWare images and scales to any number of nodes in the Cloud. Unlike Hadoop, in which simple tuples (keys & values) are passed between the map and reduce functions, SciReduce operates on bundles of named numeric arrays, which can be passed in memory or serialized to disk in netCDF4 or HDF5. Thus, SciReduce uses the native datatypes (geolocated grids, swaths, and points) that geo-scientists are familiar with. We are deploying within SciReduce a versatile set of python operators for data lookup, access, subsetting, co-registration, mining, fusion, and statistical analysis. All operators take in sets of geo-located arrays and generate more arrays. Large, multi-year satellite and model datasets are automatically "sharded" by time and space across a cluster of nodes so that years of data (millions of granules) can be compared or fused in a massively parallel way. Input variables (arrays) are pulled on-demand into the Cloud using OPeNDAP or webification URLs, thereby minimizing the size of the stored input and intermediate datasets. A typical map function might assemble and quality control AIRS Level-2 water vapor profiles for a year of data in parallel, then a reduce function would average the profiles in lat/lon bins (again, in parallel), and a final reduce would aggregate the climatology and write it to output files. We are using SciReduce to automate the production of multiple versions of a multi-year water vapor climatology (AIRS & MODIS), stratified by Cloudsat cloud classification, and compare it to models (ECMWF & MERRA reanalysis). We will present the architecture of SciReduce, describe the achieved "clock time" speedups in fusing huge datasets on our own nodes and in the Amazon Cloud, and discuss the Cloud cost tradeoffs for storage, compute, and data transfer.

Large-Scale, Parallel, Multi-Sensor Data Fusion in the Cloud

NASA Astrophysics Data System (ADS)

Wilson, B.; Manipon, G.; Hua, H.

2012-04-01

NASA's Earth Observing System (EOS) is an ambitious facility for studying global climate change. The mandate now is to combine measurements from the instruments on the "A-Train" platforms (AIRS, AMSR-E, MODIS, MISR, MLS, and CloudSat) and other Earth probes to enable large-scale studies of climate change over periods of years to decades. However, moving from predominantly single-instrument studies to a multi-sensor, measurement-based model for long-duration analysis of important climate variables presents serious challenges for large-scale data mining and data fusion. For example, one might want to compare temperature and water vapor retrievals from one instrument (AIRS) to another instrument (MODIS), and to a model (ECMWF), stratify the comparisons using a classification of the "cloud scenes" from CloudSat, and repeat the entire analysis over years of AIRS data. To perform such an analysis, one must discover & access multiple datasets from remote sites, find the space/time "matchups" between instruments swaths and model grids, understand the quality flags and uncertainties for retrieved physical variables, assemble merged datasets, and compute fused products for further scientific and statistical analysis. To efficiently assemble such decade-scale datasets in a timely manner, we are utilizing Elastic Computing in the Cloud and parallel map/reduce-based algorithms. "SciReduce" is a Hadoop-like parallel analysis system, programmed in parallel python, that is designed from the ground up for Earth science. SciReduce executes inside VMWare images and scales to any number of nodes in the Cloud. Unlike Hadoop, in which simple tuples (keys & values) are passed between the map and reduce functions, SciReduce operates on bundles of named numeric arrays, which can be passed in memory or serialized to disk in netCDF4 or HDF5. Thus, SciReduce uses the native datatypes (geolocated grids, swaths, and points) that geo-scientists are familiar with. We are deploying within SciReduce a versatile set of python operators for data lookup, access, subsetting, co-registration, mining, fusion, and statistical analysis. All operators take in sets of geo-arrays and generate more arrays. Large, multi-year satellite and model datasets are automatically "sharded" by time and space across a cluster of nodes so that years of data (millions of granules) can be compared or fused in a massively parallel way. Input variables (arrays) are pulled on-demand into the Cloud using OPeNDAP or webification URLs, thereby minimizing the size of the stored input and intermediate datasets. A typical map function might assemble and quality control AIRS Level-2 water vapor profiles for a year of data in parallel, then a reduce function would average the profiles in bins (again, in parallel), and a final reduce would aggregate the climatology and write it to output files. We are using SciReduce to automate the production of multiple versions of a multi-year water vapor climatology (AIRS & MODIS), stratified by Cloudsat cloud classification, and compare it to models (ECMWF & MERRA reanalysis). We will present the architecture of SciReduce, describe the achieved "clock time" speedups in fusing huge datasets on our own nodes and in the Amazon Cloud, and discuss the Cloud cost tradeoffs for storage, compute, and data transfer.

High Performance Geostatistical Modeling of Biospheric Resources

NASA Astrophysics Data System (ADS)

Pedelty, J. A.; Morisette, J. T.; Smith, J. A.; Schnase, J. L.; Crosier, C. S.; Stohlgren, T. J.

2004-12-01

We are using parallel geostatistical codes to study spatial relationships among biospheric resources in several study areas. For example, spatial statistical models based on large- and small-scale variability have been used to predict species richness of both native and exotic plants (hot spots of diversity) and patterns of exotic plant invasion. However, broader use of geostastics in natural resource modeling, especially at regional and national scales, has been limited due to the large computing requirements of these applications. To address this problem, we implemented parallel versions of the kriging spatial interpolation algorithm. The first uses the Message Passing Interface (MPI) in a master/slave paradigm on an open source Linux Beowulf cluster, while the second is implemented with the new proprietary Xgrid distributed processing system on an Xserve G5 cluster from Apple Computer, Inc. These techniques are proving effective and provide the basis for a national decision support capability for invasive species management that is being jointly developed by NASA and the US Geological Survey.

Proteus: a reconfigurable computational network for computer vision

NASA Astrophysics Data System (ADS)

Haralick, Robert M.; Somani, Arun K.; Wittenbrink, Craig M.; Johnson, Robert; Cooper, Kenneth; Shapiro, Linda G.; Phillips, Ihsin T.; Hwang, Jenq N.; Cheung, William; Yao, Yung H.; Chen, Chung-Ho; Yang, Larry; Daugherty, Brian; Lorbeski, Bob; Loving, Kent; Miller, Tom; Parkins, Larye; Soos, Steven L.

1992-04-01

The Proteus architecture is a highly parallel MIMD, multiple instruction, multiple-data machine, optimized for large granularity tasks such as machine vision and image processing The system can achieve 20 Giga-flops (80 Giga-flops peak). It accepts data via multiple serial links at a rate of up to 640 megabytes/second. The system employs a hierarchical reconfigurable interconnection network with the highest level being a circuit switched Enhanced Hypercube serial interconnection network for internal data transfers. The system is designed to use 256 to 1,024 RISC processors. The processors use one megabyte external Read/Write Allocating Caches for reduced multiprocessor contention. The system detects, locates, and replaces faulty subsystems using redundant hardware to facilitate fault tolerance. The parallelism is directly controllable through an advanced software system for partitioning, scheduling, and development. System software includes a translator for the INSIGHT language, a parallel debugger, low and high level simulators, and a message passing system for all control needs. Image processing application software includes a variety of point operators neighborhood, operators, convolution, and the mathematical morphology operations of binary and gray scale dilation, erosion, opening, and closing.

GW/Bethe-Salpeter calculations for charged and model systems from real-space DFT

NASA Astrophysics Data System (ADS)

Strubbe, David A.

GW and Bethe-Salpeter (GW/BSE) calculations use mean-field input from density-functional theory (DFT) calculations to compute excited states of a condensed-matter system. Many parts of a GW/BSE calculation are efficiently performed in a plane-wave basis, and extensive effort has gone into optimizing and parallelizing plane-wave GW/BSE codes for large-scale computations. Most straightforwardly, plane-wave DFT can be used as a starting point, but real-space DFT is also an attractive starting point: it is systematically convergeable like plane waves, can take advantage of efficient domain parallelization for large systems, and is well suited physically for finite and especially charged systems. The flexibility of a real-space grid also allows convenient calculations on non-atomic model systems. I will discuss the interfacing of a real-space (TD)DFT code (Octopus, www.tddft.org/programs/octopus) with a plane-wave GW/BSE code (BerkeleyGW, www.berkeleygw.org), consider performance issues and accuracy, and present some applications to simple and paradigmatic systems that illuminate fundamental properties of these approximations in many-body perturbation theory.

Grid-Enabled Quantitative Analysis of Breast Cancer

DTIC Science & Technology

2009-10-01

large-scale, multi-modality computerized image analysis . The central hypothesis of this research is that large-scale image analysis for breast cancer...pilot study to utilize large scale parallel Grid computing to harness the nationwide cluster infrastructure for optimization of medical image ... analysis parameters. Additionally, we investigated the use of cutting edge dataanalysis/ mining techniques as applied to Ultrasound, FFDM, and DCE-MRI Breast

Hi-Corrector: a fast, scalable and memory-efficient package for normalizing large-scale Hi-C data.

PubMed

Li, Wenyuan; Gong, Ke; Li, Qingjiao; Alber, Frank; Zhou, Xianghong Jasmine

2015-03-15

Genome-wide proximity ligation assays, e.g. Hi-C and its variant TCC, have recently become important tools to study spatial genome organization. Removing biases from chromatin contact matrices generated by such techniques is a critical preprocessing step of subsequent analyses. The continuing decline of sequencing costs has led to an ever-improving resolution of the Hi-C data, resulting in very large matrices of chromatin contacts. Such large-size matrices, however, pose a great challenge on the memory usage and speed of its normalization. Therefore, there is an urgent need for fast and memory-efficient methods for normalization of Hi-C data. We developed Hi-Corrector, an easy-to-use, open source implementation of the Hi-C data normalization algorithm. Its salient features are (i) scalability-the software is capable of normalizing Hi-C data of any size in reasonable times; (ii) memory efficiency-the sequential version can run on any single computer with very limited memory, no matter how little; (iii) fast speed-the parallel version can run very fast on multiple computing nodes with limited local memory. The sequential version is implemented in ANSI C and can be easily compiled on any system; the parallel version is implemented in ANSI C with the MPI library (a standardized and portable parallel environment designed for solving large-scale scientific problems). The package is freely available at http://zhoulab.usc.edu/Hi-Corrector/. © The Author 2014. Published by Oxford University Press.

Anderson acceleration of the Jacobi iterative method: An efficient alternative to Krylov methods for large, sparse linear systems

DOE PAGES

Pratapa, Phanisri P.; Suryanarayana, Phanish; Pask, John E.

2015-12-01

We employ Anderson extrapolation to accelerate the classical Jacobi iterative method for large, sparse linear systems. Specifically, we utilize extrapolation at periodic intervals within the Jacobi iteration to develop the Alternating Anderson–Jacobi (AAJ) method. We verify the accuracy and efficacy of AAJ in a range of test cases, including nonsymmetric systems of equations. We demonstrate that AAJ possesses a favorable scaling with system size that is accompanied by a small prefactor, even in the absence of a preconditioner. In particular, we show that AAJ is able to accelerate the classical Jacobi iteration by over four orders of magnitude, with speed-upsmore » that increase as the system gets larger. Moreover, we find that AAJ significantly outperforms the Generalized Minimal Residual (GMRES) method in the range of problems considered here, with the relative performance again improving with size of the system. As a result, the proposed method represents a simple yet efficient technique that is particularly attractive for large-scale parallel solutions of linear systems of equations.« less

Anderson acceleration of the Jacobi iterative method: An efficient alternative to Krylov methods for large, sparse linear systems

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

Pratapa, Phanisri P.; Suryanarayana, Phanish; Pask, John E.

We employ Anderson extrapolation to accelerate the classical Jacobi iterative method for large, sparse linear systems. Specifically, we utilize extrapolation at periodic intervals within the Jacobi iteration to develop the Alternating Anderson–Jacobi (AAJ) method. We verify the accuracy and efficacy of AAJ in a range of test cases, including nonsymmetric systems of equations. We demonstrate that AAJ possesses a favorable scaling with system size that is accompanied by a small prefactor, even in the absence of a preconditioner. In particular, we show that AAJ is able to accelerate the classical Jacobi iteration by over four orders of magnitude, with speed-upsmore » that increase as the system gets larger. Moreover, we find that AAJ significantly outperforms the Generalized Minimal Residual (GMRES) method in the range of problems considered here, with the relative performance again improving with size of the system. As a result, the proposed method represents a simple yet efficient technique that is particularly attractive for large-scale parallel solutions of linear systems of equations.« less

Parallel Geospatial Data Management for Multi-Scale Environmental Data Analysis on GPUs

NASA Astrophysics Data System (ADS)

Wang, D.; Zhang, J.; Wei, Y.

2013-12-01

As the spatial and temporal resolutions of Earth observatory data and Earth system simulation outputs are getting higher, in-situ and/or post- processing such large amount of geospatial data increasingly becomes a bottleneck in scientific inquires of Earth systems and their human impacts. Existing geospatial techniques that are based on outdated computing models (e.g., serial algorithms and disk-resident systems), as have been implemented in many commercial and open source packages, are incapable of processing large-scale geospatial data and achieve desired level of performance. In this study, we have developed a set of parallel data structures and algorithms that are capable of utilizing massively data parallel computing power available on commodity Graphics Processing Units (GPUs) for a popular geospatial technique called Zonal Statistics. Given two input datasets with one representing measurements (e.g., temperature or precipitation) and the other one represent polygonal zones (e.g., ecological or administrative zones), Zonal Statistics computes major statistics (or complete distribution histograms) of the measurements in all regions. Our technique has four steps and each step can be mapped to GPU hardware by identifying its inherent data parallelisms. First, a raster is divided into blocks and per-block histograms are derived. Second, the Minimum Bounding Boxes (MBRs) of polygons are computed and are spatially matched with raster blocks; matched polygon-block pairs are tested and blocks that are either inside or intersect with polygons are identified. Third, per-block histograms are aggregated to polygons for blocks that are completely within polygons. Finally, for blocks that intersect with polygon boundaries, all the raster cells within the blocks are examined using point-in-polygon-test and cells that are within polygons are used to update corresponding histograms. As the task becomes I/O bound after applying spatial indexing and GPU hardware acceleration, we have developed a GPU-based data compression technique by reusing our previous work on Bitplane Quadtree (or BPQ-Tree) based indexing of binary bitmaps. Results have shown that our GPU-based parallel Zonal Statistic technique on 3000+ US counties over 20+ billion NASA SRTM 30 meter resolution Digital Elevation (DEM) raster cells has achieved impressive end-to-end runtimes: 101 seconds and 46 seconds a low-end workstation equipped with a Nvidia GTX Titan GPU using cold and hot cache, respectively; and, 60-70 seconds using a single OLCF TITAN computing node and 10-15 seconds using 8 nodes. Our experiment results clearly show the potentials of using high-end computing facilities for large-scale geospatial processing.

Partition-of-unity finite-element method for large scale quantum molecular dynamics on massively parallel computational platforms

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

Pask, J E; Sukumar, N; Guney, M

2011-02-28

Over the course of the past two decades, quantum mechanical calculations have emerged as a key component of modern materials research. However, the solution of the required quantum mechanical equations is a formidable task and this has severely limited the range of materials systems which can be investigated by such accurate, quantum mechanical means. The current state of the art for large-scale quantum simulations is the planewave (PW) method, as implemented in now ubiquitous VASP, ABINIT, and QBox codes, among many others. However, since the PW method uses a global Fourier basis, with strictly uniform resolution at all points inmore » space, and in which every basis function overlaps every other at every point, it suffers from substantial inefficiencies in calculations involving atoms with localized states, such as first-row and transition-metal atoms, and requires substantial nonlocal communications in parallel implementations, placing critical limits on scalability. In recent years, real-space methods such as finite-differences (FD) and finite-elements (FE) have been developed to address these deficiencies by reformulating the required quantum mechanical equations in a strictly local representation. However, while addressing both resolution and parallel-communications problems, such local real-space approaches have been plagued by one key disadvantage relative to planewaves: excessive degrees of freedom (grid points, basis functions) needed to achieve the required accuracies. And so, despite critical limitations, the PW method remains the standard today. In this work, we show for the first time that this key remaining disadvantage of real-space methods can in fact be overcome: by building known atomic physics into the solution process using modern partition-of-unity (PU) techniques in finite element analysis. Indeed, our results show order-of-magnitude reductions in basis size relative to state-of-the-art planewave based methods. The method developed here is completely general, applicable to any crystal symmetry and to both metals and insulators alike. We have developed and implemented a full self-consistent Kohn-Sham method, including both total energies and forces for molecular dynamics, and developed a full MPI parallel implementation for large-scale calculations. We have applied the method to the gamut of physical systems, from simple insulating systems with light atoms to complex d- and f-electron systems, requiring large numbers of atomic-orbital enrichments. In every case, the new PU FE method attained the required accuracies with substantially fewer degrees of freedom, typically by an order of magnitude or more, than the current state-of-the-art PW method. Finally, our initial MPI implementation has shown excellent parallel scaling of the most time-critical parts of the code up to 1728 processors, with clear indications of what will be required to achieve comparable scaling for the rest. Having shown that the key remaining disadvantage of real-space methods can in fact be overcome, the work has attracted significant attention: with sixteen invited talks, both domestic and international, so far; two papers published and another in preparation; and three new university and/or national laboratory collaborations, securing external funding to pursue a number of related research directions. Having demonstrated the proof of principle, work now centers on the necessary extensions and optimizations required to bring the prototype method and code delivered here to production applications.« less

Measurement of large parallel and perpendicular electric fields on electron spatial scales in the terrestrial bow shock.

PubMed

Bale, S D; Mozer, F S

2007-05-18

Large parallel (

The Metropolis Monte Carlo method with CUDA enabled Graphic Processing Units

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

Hall, Clifford; School of Physics, Astronomy, and Computational Sciences, George Mason University, 4400 University Dr., Fairfax, VA 22030; Ji, Weixiao

2014-02-01

We present a CPU–GPU system for runtime acceleration of large molecular simulations using GPU computation and memory swaps. The memory architecture of the GPU can be used both as container for simulation data stored on the graphics card and as floating-point code target, providing an effective means for the manipulation of atomistic or molecular data on the GPU. To fully take advantage of this mechanism, efficient GPU realizations of algorithms used to perform atomistic and molecular simulations are essential. Our system implements a versatile molecular engine, including inter-molecule interactions and orientational variables for performing the Metropolis Monte Carlo (MMC) algorithm,more » which is one type of Markov chain Monte Carlo. By combining memory objects with floating-point code fragments we have implemented an MMC parallel engine that entirely avoids the communication time of molecular data at runtime. Our runtime acceleration system is a forerunner of a new class of CPU–GPU algorithms exploiting memory concepts combined with threading for avoiding bus bandwidth and communication. The testbed molecular system used here is a condensed phase system of oligopyrrole chains. A benchmark shows a size scaling speedup of 60 for systems with 210,000 pyrrole monomers. Our implementation can easily be combined with MPI to connect in parallel several CPU–GPU duets. -- Highlights: •We parallelize the Metropolis Monte Carlo (MMC) algorithm on one CPU—GPU duet. •The Adaptive Tempering Monte Carlo employs MMC and profits from this CPU—GPU implementation. •Our benchmark shows a size scaling-up speedup of 62 for systems with 225,000 particles. •The testbed involves a polymeric system of oligopyrroles in the condensed phase. •The CPU—GPU parallelization includes dipole—dipole and Mie—Jones classic potentials.« less

Efficient Parallelization of a Dynamic Unstructured Application on the Tera MTA

NASA Technical Reports Server (NTRS)

Oliker, Leonid; Biswas, Rupak

1999-01-01

The success of parallel computing in solving real-life computationally-intensive problems relies on their efficient mapping and execution on large-scale multiprocessor architectures. Many important applications are both unstructured and dynamic in nature, making their efficient parallel implementation a daunting task. This paper presents the parallelization of a dynamic unstructured mesh adaptation algorithm using three popular programming paradigms on three leading supercomputers. We examine an MPI message-passing implementation on the Cray T3E and the SGI Origin2OOO, a shared-memory implementation using cache coherent nonuniform memory access (CC-NUMA) of the Origin2OOO, and a multi-threaded version on the newly-released Tera Multi-threaded Architecture (MTA). We compare several critical factors of this parallel code development, including runtime, scalability, programmability, and memory overhead. Our overall results demonstrate that multi-threaded systems offer tremendous potential for quickly and efficiently solving some of the most challenging real-life problems on parallel computers.

Cloud Computing Boosts Business Intelligence of Telecommunication Industry

NASA Astrophysics Data System (ADS)

Xu, Meng; Gao, Dan; Deng, Chao; Luo, Zhiguo; Sun, Shaoling

Business Intelligence becomes an attracting topic in today's data intensive applications, especially in telecommunication industry. Meanwhile, Cloud Computing providing IT supporting Infrastructure with excellent scalability, large scale storage, and high performance becomes an effective way to implement parallel data processing and data mining algorithms. BC-PDM (Big Cloud based Parallel Data Miner) is a new MapReduce based parallel data mining platform developed by CMRI (China Mobile Research Institute) to fit the urgent requirements of business intelligence in telecommunication industry. In this paper, the architecture, functionality and performance of BC-PDM are presented, together with the experimental evaluation and case studies of its applications. The evaluation result demonstrates both the usability and the cost-effectiveness of Cloud Computing based Business Intelligence system in applications of telecommunication industry.

Large-Scale, Multi-Sensor Atmospheric Data Fusion Using Hybrid Cloud Computing

NASA Astrophysics Data System (ADS)

Wilson, Brian; Manipon, Gerald; Hua, Hook; Fetzer, Eric

2014-05-01

NASA's Earth Observing System (EOS) is an ambitious facility for studying global climate change. The mandate now is to combine measurements from the instruments on the "A-Train" platforms (AIRS, AMSR-E, MODIS, MISR, MLS, and CloudSat) and other Earth probes to enable large-scale studies of climate change over decades. Moving to multi-sensor, long-duration analyses of important climate variables presents serious challenges for large-scale data mining and fusion. For example, one might want to compare temperature and water vapor retrievals from one instrument (AIRS) to another (MODIS), and to a model (ECMWF), stratify the comparisons using a classification of the "cloud scenes" from CloudSat, and repeat the entire analysis over 10 years of data. To efficiently assemble such datasets, we are utilizing Elastic Computing in the Cloud and parallel map-reduce-based algorithms. However, these problems are Data Intensive computing so the data transfer times and storage costs (for caching) are key issues. SciReduce is a Hadoop-like parallel analysis system, programmed in parallel python, that is designed from the ground up for Earth science. SciReduce executes inside VMWare images and scales to any number of nodes in a hybrid Cloud (private eucalyptus & public Amazon). Unlike Hadoop, SciReduce operates on bundles of named numeric arrays, which can be passed in memory or serialized to disk in netCDF4 or HDF5. Multi-year datasets are automatically "sharded" by time and space across a cluster of nodes so that years of data (millions of files) can be processed in a massively parallel way. Input variables (arrays) are pulled on-demand into the Cloud using OPeNDAP URLs or other subsetting services, thereby minimizing the size of the cached input and intermediate datasets. We are using SciReduce to automate the production of multiple versions of a ten-year A-Train water vapor climatology under a NASA MEASURES grant. We will present the architecture of SciReduce, describe the achieved "clock time" speedups in fusing datasets on our own nodes and in the Cloud, and discuss the Cloud cost tradeoffs for storage, compute, and data transfer. We will also present a concept and prototype for staging NASA's A-Train Atmospheric datasets (Levels 2 & 3) in the Amazon Cloud so that any number of compute jobs can be executed "near" the multi-sensor data. Given such a system, multi-sensor climate studies over 10-20 years of data could be perform

Design Aspects of the Rayleigh Convection Code

NASA Astrophysics Data System (ADS)

Featherstone, N. A.

2017-12-01

Understanding the long-term generation of planetary or stellar magnetic field requires complementary knowledge of the large-scale fluid dynamics pervading large fractions of the object's interior. Such large-scale motions are sensitive to the system's geometry which, in planets and stars, is spherical to a good approximation. As a result, computational models designed to study such systems often solve the MHD equations in spherical geometry, frequently employing a spectral approach involving spherical harmonics. We present computational and user-interface design aspects of one such modeling tool, the Rayleigh convection code, which is suitable for deployment on desktop and petascale-hpc architectures alike. In this poster, we will present an overview of this code's parallel design and its built-in diagnostics-output package. Rayleigh has been developed with NSF support through the Computational Infrastructure for Geodynamics and is expected to be released as open-source software in winter 2017/2018.

High subsonic flow tests of a parallel pipe followed by a large area ratio diffuser

NASA Technical Reports Server (NTRS)

Barna, P. S.

1975-01-01

Experiments were performed on a pilot model duct system in order to explore its aerodynamic characteristics. The model was scaled from a design projected for the high speed operation mode of the Aircraft Noise Reduction Laboratory. The test results show that the model performed satisfactorily and therefore the projected design will most likely meet the specifications.

Highly Efficient Parallel Multigrid Solver For Large-Scale Simulation of Grain Growth Using the Structural Phase Field Crystal Model

NASA Astrophysics Data System (ADS)

Guan, Zhen; Pekurovsky, Dmitry; Luce, Jason; Thornton, Katsuyo; Lowengrub, John

The structural phase field crystal (XPFC) model can be used to model grain growth in polycrystalline materials at diffusive time-scales while maintaining atomic scale resolution. However, the governing equation of the XPFC model is an integral-partial-differential-equation (IPDE), which poses challenges in implementation onto high performance computing (HPC) platforms. In collaboration with the XSEDE Extended Collaborative Support Service, we developed a distributed memory HPC solver for the XPFC model, which combines parallel multigrid and P3DFFT. The performance benchmarking on the Stampede supercomputer indicates near linear strong and weak scaling for both multigrid and transfer time between multigrid and FFT modules up to 1024 cores. Scalability of the FFT module begins to decline at 128 cores, but it is sufficient for the type of problem we will be examining. We have demonstrated simulations using 1024 cores, and we expect to achieve 4096 cores and beyond. Ongoing work involves optimization of MPI/OpenMP-based codes for the Intel KNL Many-Core Architecture. This optimizes the code for coming pre-exascale systems, in particular many-core systems such as Stampede 2.0 and Cori 2 at NERSC, without sacrificing efficiency on other general HPC systems.

Spectral enstrophy budget in a shear-less flow with turbulent/non-turbulent interface

NASA Astrophysics Data System (ADS)

Cimarelli, Andrea; Cocconi, Giacomo; Frohnapfel, Bettina; De Angelis, Elisabetta

2015-12-01

A numerical analysis of the interaction between decaying shear free turbulence and quiescent fluid is performed by means of global statistical budgets of enstrophy, both, at the single-point and two point levels. The single-point enstrophy budget allows us to recognize three physically relevant layers: a bulk turbulent region, an inhomogeneous turbulent layer, and an interfacial layer. Within these layers, enstrophy is produced, transferred, and finally destroyed while leading to a propagation of the turbulent front. These processes do not only depend on the position in the flow field but are also strongly scale dependent. In order to tackle this multi-dimensional behaviour of enstrophy in the space of scales and in physical space, we analyse the spectral enstrophy budget equation. The picture consists of an inviscid spatial cascade of enstrophy from large to small scales parallel to the interface moving towards the interface. At the interface, this phenomenon breaks, leaving place to an anisotropic cascade where large scale structures exhibit only a cascade process normal to the interface thus reducing their thickness while retaining their lengths parallel to the interface. The observed behaviour could be relevant for both the theoretical and the modelling approaches to flow with interacting turbulent/nonturbulent regions. The scale properties of the turbulent propagation mechanisms highlight that the inviscid turbulent transport is a large-scale phenomenon. On the contrary, the viscous diffusion, commonly associated with small scale mechanisms, highlights a much richer physics involving small lengths, normal to the interface, but at the same time large scales, parallel to the interface.

Implementing Parquet equations using HPX

NASA Astrophysics Data System (ADS)

Kellar, Samuel; Wagle, Bibek; Yang, Shuxiang; Tam, Ka-Ming; Kaiser, Hartmut; Moreno, Juana; Jarrell, Mark

A new C++ runtime system (HPX) enables simulations of complex systems to run more efficiently on parallel and heterogeneous systems. This increased efficiency allows for solutions to larger simulations of the parquet approximation for a system with impurities. The relevancy of the parquet equations depends upon the ability to solve systems which require long runs and large amounts of memory. These limitations, in addition to numerical complications arising from stability of the solutions, necessitate running on large distributed systems. As the computational resources trend towards the exascale and the limitations arising from computational resources vanish efficiency of large scale simulations becomes a focus. HPX facilitates efficient simulations through intelligent overlapping of computation and communication. Simulations such as the parquet equations which require the transfer of large amounts of data should benefit from HPX implementations. Supported by the the NSF EPSCoR Cooperative Agreement No. EPS-1003897 with additional support from the Louisiana Board of Regents.

Particle Based Simulations of Complex Systems with MP2C : Hydrodynamics and Electrostatics

NASA Astrophysics Data System (ADS)

Sutmann, Godehard; Westphal, Lidia; Bolten, Matthias

2010-09-01

Particle based simulation methods are well established paths to explore system behavior on microscopic to mesoscopic time and length scales. With the development of new computer architectures it becomes more and more important to concentrate on local algorithms which do not need global data transfer or reorganisation of large arrays of data across processors. This requirement strongly addresses long-range interactions in particle systems, i.e. mainly hydrodynamic and electrostatic contributions. In this article, emphasis is given to the implementation and parallelization of the Multi-Particle Collision Dynamics method for hydrodynamic contributions and a splitting scheme based on Multigrid for electrostatic contributions. Implementations are done for massively parallel architectures and are demonstrated for the IBM Blue Gene/P architecture Jugene in Jülich.

Approximate kernel competitive learning.

PubMed

Wu, Jian-Sheng; Zheng, Wei-Shi; Lai, Jian-Huang

2015-03-01

Kernel competitive learning has been successfully used to achieve robust clustering. However, kernel competitive learning (KCL) is not scalable for large scale data processing, because (1) it has to calculate and store the full kernel matrix that is too large to be calculated and kept in the memory and (2) it cannot be computed in parallel. In this paper we develop a framework of approximate kernel competitive learning for processing large scale dataset. The proposed framework consists of two parts. First, it derives an approximate kernel competitive learning (AKCL), which learns kernel competitive learning in a subspace via sampling. We provide solid theoretical analysis on why the proposed approximation modelling would work for kernel competitive learning, and furthermore, we show that the computational complexity of AKCL is largely reduced. Second, we propose a pseudo-parallelled approximate kernel competitive learning (PAKCL) based on a set-based kernel competitive learning strategy, which overcomes the obstacle of using parallel programming in kernel competitive learning and significantly accelerates the approximate kernel competitive learning for large scale clustering. The empirical evaluation on publicly available datasets shows that the proposed AKCL and PAKCL can perform comparably as KCL, with a large reduction on computational cost. Also, the proposed methods achieve more effective clustering performance in terms of clustering precision against related approximate clustering approaches. Copyright © 2014 Elsevier Ltd. All rights reserved.

System for inspecting large size structural components

DOEpatents

Birks, Albert S.; Skorpik, James R.

1990-01-01

The present invention relates to a system for inspecting large scale structural components such as concrete walls or the like. The system includes a mobile gamma radiation source and a mobile gamma radiation detector. The source and detector are constructed and arranged for simultaneous movement along parallel paths in alignment with one another on opposite sides of a structural component being inspected. A control system provides signals which coordinate the movements of the source and detector and receives and records the radiation level data developed by the detector as a function of source and detector positions. The radiation level data is then analyzed to identify areas containing defects corresponding to unexpected variations in the radiation levels detected.

MOOSE: A parallel computational framework for coupled systems of nonlinear equations.

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

Derek Gaston; Chris Newman; Glen Hansen

Systems of coupled, nonlinear partial differential equations (PDEs) often arise in simulation of nuclear processes. MOOSE: Multiphysics Object Oriented Simulation Environment, a parallel computational framework targeted at the solution of such systems, is presented. As opposed to traditional data-flow oriented computational frameworks, MOOSE is instead founded on the mathematical principle of Jacobian-free Newton-Krylov (JFNK) solution methods. Utilizing the mathematical structure present in JFNK, physics expressions are modularized into `Kernels,'' allowing for rapid production of new simulation tools. In addition, systems are solved implicitly and fully coupled, employing physics based preconditioning, which provides great flexibility even with large variance in timemore » scales. A summary of the mathematics, an overview of the structure of MOOSE, and several representative solutions from applications built on the framework are presented.« less

Pollutant Transport and Fate: Relations Between Flow-paths and Downstream Impacts of Human Activities

NASA Astrophysics Data System (ADS)

Thorslund, J.; Jarsjo, J.; Destouni, G.

2017-12-01

The quality of freshwater resources is increasingly impacted by human activities. Humans also extensively change the structure of landscapes, which may alter natural hydrological processes. To manage and maintain freshwater of good water quality, it is critical to understand how pollutants are released into, transported and transformed within the hydrological system. Some key scientific questions include: What are net downstream impacts of pollutants across different hydroclimatic and human disturbance conditions, and on different scales? What are the functions within and between components of the landscape, such as wetlands, on mitigating pollutant load delivery to downstream recipients? We explore these questions by synthesizing results from several relevant case study examples of intensely human-impacted hydrological systems. These case study sites have been specifically evaluated in terms of net impact of human activities on pollutant input to the aquatic system, as well as flow-path distributions trough wetlands as a potential ecosystem service of pollutant mitigation. Results shows that although individual wetlands have high retention capacity, efficient net retention effects were not always achieved at a larger landscape scale. Evidence suggests that the function of wetlands as mitigation solutions to pollutant loads is largely controlled by large-scale parallel and circular flow-paths, through which multiple wetlands are interconnected in the landscape. To achieve net mitigation effects at large scale, a large fraction of the polluted large-scale flows must be transported through multiple connected wetlands. Although such large-scale flow interactions are critical for assessing water pollution spreading and fate through the landscape, our synthesis shows a frequent lack of knowledge at such scales. We suggest ways forward for addressing the mismatch between the large scales at which key pollutant pressures and water quality changes take place and the relatively scale at which most studies and implementations are currently made. These suggestions can help bridge critical knowledge gaps, as needed for improving water quality predictions and mitigation solutions under human and environmental changes.

Parallel Visualization of Large-Scale Aerodynamics Calculations: A Case Study on the Cray T3E

NASA Technical Reports Server (NTRS)

Ma, Kwan-Liu; Crockett, Thomas W.

1999-01-01

This paper reports the performance of a parallel volume rendering algorithm for visualizing a large-scale, unstructured-grid dataset produced by a three-dimensional aerodynamics simulation. This dataset, containing over 18 million tetrahedra, allows us to extend our performance results to a problem which is more than 30 times larger than the one we examined previously. This high resolution dataset also allows us to see fine, three-dimensional features in the flow field. All our tests were performed on the Silicon Graphics Inc. (SGI)/Cray T3E operated by NASA's Goddard Space Flight Center. Using 511 processors, a rendering rate of almost 9 million tetrahedra/second was achieved with a parallel overhead of 26%.

Compiled MPI: Cost-Effective Exascale Applications Development

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

Bronevetsky, G; Quinlan, D; Lumsdaine, A

2012-04-10

The complexity of petascale and exascale machines makes it increasingly difficult to develop applications that can take advantage of them. Future systems are expected to feature billion-way parallelism, complex heterogeneous compute nodes and poor availability of memory (Peter Kogge, 2008). This new challenge for application development is motivating a significant amount of research and development on new programming models and runtime systems designed to simplify large-scale application development. Unfortunately, DoE has significant multi-decadal investment in a large family of mission-critical scientific applications. Scaling these applications to exascale machines will require a significant investment that will dwarf the costs of hardwaremore » procurement. A key reason for the difficulty in transitioning today's applications to exascale hardware is their reliance on explicit programming techniques, such as the Message Passing Interface (MPI) programming model to enable parallelism. MPI provides a portable and high performance message-passing system that enables scalable performance on a wide variety of platforms. However, it also forces developers to lock the details of parallelization together with application logic, making it very difficult to adapt the application to significant changes in the underlying system. Further, MPI's explicit interface makes it difficult to separate the application's synchronization and communication structure, reducing the amount of support that can be provided by compiler and run-time tools. This is in contrast to the recent research on more implicit parallel programming models such as Chapel, OpenMP and OpenCL, which promise to provide significantly more flexibility at the cost of reimplementing significant portions of the application. We are developing CoMPI, a novel compiler-driven approach to enable existing MPI applications to scale to exascale systems with minimal modifications that can be made incrementally over the application's lifetime. It includes: (1) New set of source code annotations, inserted either manually or automatically, that will clarify the application's use of MPI to the compiler infrastructure, enabling greater accuracy where needed; (2) A compiler transformation framework that leverages these annotations to transform the original MPI source code to improve its performance and scalability; (3) Novel MPI runtime implementation techniques that will provide a rich set of functionality extensions to be used by applications that have been transformed by our compiler; and (4) A novel compiler analysis that leverages simple user annotations to automatically extract the application's communication structure and synthesize most complex code annotations.« less

The EMCC / DARPA Massively Parallel Electromagnetic Scattering Project

NASA Technical Reports Server (NTRS)

Woo, Alex C.; Hill, Kueichien C.

1996-01-01

The Electromagnetic Code Consortium (EMCC) was sponsored by the Advanced Research Program Agency (ARPA) to demonstrate the effectiveness of massively parallel computing in large scale radar signature predictions. The EMCC/ARPA project consisted of three parts.

Towards a Standard Mixed-Signal Parallel Processing Architecture for Miniature and Microrobotics.

PubMed

Sadler, Brian M; Hoyos, Sebastian

2014-01-01

The conventional analog-to-digital conversion (ADC) and digital signal processing (DSP) architecture has led to major advances in miniature and micro-systems technology over the past several decades. The outlook for these systems is significantly enhanced by advances in sensing, signal processing, communications and control, and the combination of these technologies enables autonomous robotics on the miniature to micro scales. In this article we look at trends in the combination of analog and digital (mixed-signal) processing, and consider a generalized sampling architecture. Employing a parallel analog basis expansion of the input signal, this scalable approach is adaptable and reconfigurable, and is suitable for a large variety of current and future applications in networking, perception, cognition, and control.

Towards a Standard Mixed-Signal Parallel Processing Architecture for Miniature and Microrobotics

PubMed Central

Sadler, Brian M; Hoyos, Sebastian

2014-01-01

The conventional analog-to-digital conversion (ADC) and digital signal processing (DSP) architecture has led to major advances in miniature and micro-systems technology over the past several decades. The outlook for these systems is significantly enhanced by advances in sensing, signal processing, communications and control, and the combination of these technologies enables autonomous robotics on the miniature to micro scales. In this article we look at trends in the combination of analog and digital (mixed-signal) processing, and consider a generalized sampling architecture. Employing a parallel analog basis expansion of the input signal, this scalable approach is adaptable and reconfigurable, and is suitable for a large variety of current and future applications in networking, perception, cognition, and control. PMID:26601042

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

Chow, Edmond

Solving sparse problems is at the core of many DOE computational science applications. We focus on the challenge of developing sparse algorithms that can fully exploit the parallelism in extreme-scale computing systems, in particular systems with massive numbers of cores per node. Our approach is to express a sparse matrix factorization as a large number of bilinear constraint equations, and then solving these equations via an asynchronous iterative method. The unknowns in these equations are the matrix entries of the factorization that is desired.

Development of mpi_EPIC model for global agroecosystem modeling

DOE PAGES

Kang, Shujiang; Wang, Dali; Jeff A. Nichols; ...

2014-12-31

Models that address policy-maker concerns about multi-scale effects of food and bioenergy production systems are computationally demanding. We integrated the message passing interface algorithm into the process-based EPIC model to accelerate computation of ecosystem effects. Simulation performance was further enhanced by applying the Vampir framework. When this enhanced mpi_EPIC model was tested, total execution time for a global 30-year simulation of a switchgrass cropping system was shortened to less than 0.5 hours on a supercomputer. The results illustrate that mpi_EPIC using parallel design can balance simulation workloads and facilitate large-scale, high-resolution analysis of agricultural production systems, management alternatives and environmentalmore » effects.« less

Implementation and verification of a four-probe motion error measurement system for a large-scale roll lathe used in hybrid manufacturing

NASA Astrophysics Data System (ADS)

Chen, Yuan-Liu; Niu, Zengyuan; Matsuura, Daiki; Lee, Jung Chul; Shimizu, Yuki; Gao, Wei; Oh, Jeong Seok; Park, Chun Hong

2017-10-01

In this paper, a four-probe measurement system is implemented and verified for the carriage slide motion error measurement of a large-scale roll lathe used in hybrid manufacturing where a laser machining probe and a diamond cutting tool are placed on two sides of a roll workpiece for manufacturing. The motion error of the carriage slide of the roll lathe is composed of two straightness motion error components and two parallelism motion error components in the vertical and horizontal planes. Four displacement measurement probes, which are mounted on the carriage slide with respect to four opposing sides of the roll workpiece, are employed for the measurement. Firstly, based on the reversal technique, the four probes are moved by the carriage slide to scan the roll workpiece before and after a 180-degree rotation of the roll workpiece. Taking into consideration the fact that the machining accuracy of the lathe is influenced by not only the carriage slide motion error but also the gravity deformation of the large-scale roll workpiece due to its heavy weight, the vertical motion error is thus characterized relating to the deformed axis of the roll workpiece. The horizontal straightness motion error can also be synchronously obtained based on the reversal technique. In addition, based on an error separation algorithm, the vertical and horizontal parallelism motion error components are identified by scanning the rotating roll workpiece at the start and the end positions of the carriage slide, respectively. The feasibility and reliability of the proposed motion error measurement system are demonstrated by the experimental results and the measurement uncertainty analysis.

Large-Scale Cubic-Scaling Random Phase Approximation Correlation Energy Calculations Using a Gaussian Basis.

PubMed

Wilhelm, Jan; Seewald, Patrick; Del Ben, Mauro; Hutter, Jürg

2016-12-13

We present an algorithm for computing the correlation energy in the random phase approximation (RPA) in a Gaussian basis requiring [Formula: see text] operations and [Formula: see text] memory. The method is based on the resolution of the identity (RI) with the overlap metric, a reformulation of RI-RPA in the Gaussian basis, imaginary time, and imaginary frequency integration techniques, and the use of sparse linear algebra. Additional memory reduction without extra computations can be achieved by an iterative scheme that overcomes the memory bottleneck of canonical RPA implementations. We report a massively parallel implementation that is the key for the application to large systems. Finally, cubic-scaling RPA is applied to a thousand water molecules using a correlation-consistent triple-ζ quality basis.

Constructing Neuronal Network Models in Massively Parallel Environments.

PubMed

Ippen, Tammo; Eppler, Jochen M; Plesser, Hans E; Diesmann, Markus

2017-01-01

Recent advances in the development of data structures to represent spiking neuron network models enable us to exploit the complete memory of petascale computers for a single brain-scale network simulation. In this work, we investigate how well we can exploit the computing power of such supercomputers for the creation of neuronal networks. Using an established benchmark, we divide the runtime of simulation code into the phase of network construction and the phase during which the dynamical state is advanced in time. We find that on multi-core compute nodes network creation scales well with process-parallel code but exhibits a prohibitively large memory consumption. Thread-parallel network creation, in contrast, exhibits speedup only up to a small number of threads but has little overhead in terms of memory. We further observe that the algorithms creating instances of model neurons and their connections scale well for networks of ten thousand neurons, but do not show the same speedup for networks of millions of neurons. Our work uncovers that the lack of scaling of thread-parallel network creation is due to inadequate memory allocation strategies and demonstrates that thread-optimized memory allocators recover excellent scaling. An analysis of the loop order used for network construction reveals that more complex tests on the locality of operations significantly improve scaling and reduce runtime by allowing construction algorithms to step through large networks more efficiently than in existing code. The combination of these techniques increases performance by an order of magnitude and harnesses the increasingly parallel compute power of the compute nodes in high-performance clusters and supercomputers.

Constructing Neuronal Network Models in Massively Parallel Environments

PubMed Central

Ippen, Tammo; Eppler, Jochen M.; Plesser, Hans E.; Diesmann, Markus

2017-01-01

Recent advances in the development of data structures to represent spiking neuron network models enable us to exploit the complete memory of petascale computers for a single brain-scale network simulation. In this work, we investigate how well we can exploit the computing power of such supercomputers for the creation of neuronal networks. Using an established benchmark, we divide the runtime of simulation code into the phase of network construction and the phase during which the dynamical state is advanced in time. We find that on multi-core compute nodes network creation scales well with process-parallel code but exhibits a prohibitively large memory consumption. Thread-parallel network creation, in contrast, exhibits speedup only up to a small number of threads but has little overhead in terms of memory. We further observe that the algorithms creating instances of model neurons and their connections scale well for networks of ten thousand neurons, but do not show the same speedup for networks of millions of neurons. Our work uncovers that the lack of scaling of thread-parallel network creation is due to inadequate memory allocation strategies and demonstrates that thread-optimized memory allocators recover excellent scaling. An analysis of the loop order used for network construction reveals that more complex tests on the locality of operations significantly improve scaling and reduce runtime by allowing construction algorithms to step through large networks more efficiently than in existing code. The combination of these techniques increases performance by an order of magnitude and harnesses the increasingly parallel compute power of the compute nodes in high-performance clusters and supercomputers. PMID:28559808

Global Load Balancing with Parallel Mesh Adaption on Distributed-Memory Systems

NASA Technical Reports Server (NTRS)

Biswas, Rupak; Oliker, Leonid; Sohn, Andrew

1996-01-01

Dynamic mesh adaption on unstructured grids is a powerful tool for efficiently computing unsteady problems to resolve solution features of interest. Unfortunately, this causes load imbalance among processors on a parallel machine. This paper describes the parallel implementation of a tetrahedral mesh adaption scheme and a new global load balancing method. A heuristic remapping algorithm is presented that assigns partitions to processors such that the redistribution cost is minimized. Results indicate that the parallel performance of the mesh adaption code depends on the nature of the adaption region and show a 35.5X speedup on 64 processors of an SP2 when 35% of the mesh is randomly adapted. For large-scale scientific computations, our load balancing strategy gives almost a sixfold reduction in solver execution times over non-balanced loads. Furthermore, our heuristic remapper yields processor assignments that are less than 3% off the optimal solutions but requires only 1% of the computational time.

Cooperative parallel adaptive neighbourhood search for the disjunctively constrained knapsack problem

NASA Astrophysics Data System (ADS)

Quan, Zhe; Wu, Lei

2017-09-01

This article investigates the use of parallel computing for solving the disjunctively constrained knapsack problem. The proposed parallel computing model can be viewed as a cooperative algorithm based on a multi-neighbourhood search. The cooperation system is composed of a team manager and a crowd of team members. The team members aim at applying their own search strategies to explore the solution space. The team manager collects the solutions from the members and shares the best one with them. The performance of the proposed method is evaluated on a group of benchmark data sets. The results obtained are compared to those reached by the best methods from the literature. The results show that the proposed method is able to provide the best solutions in most cases. In order to highlight the robustness of the proposed parallel computing model, a new set of large-scale instances is introduced. Encouraging results have been obtained.

Parallel software for lattice N = 4 supersymmetric Yang-Mills theory

NASA Astrophysics Data System (ADS)

Schaich, David; DeGrand, Thomas

2015-05-01

We present new parallel software, SUSY LATTICE, for lattice studies of four-dimensional N = 4 supersymmetric Yang-Mills theory with gauge group SU(N). The lattice action is constructed to exactly preserve a single supersymmetry charge at non-zero lattice spacing, up to additional potential terms included to stabilize numerical simulations. The software evolved from the MILC code for lattice QCD, and retains a similar large-scale framework despite the different target theory. Many routines are adapted from an existing serial code (Catterall and Joseph, 2012), which SUSY LATTICE supersedes. This paper provides an overview of the new parallel software, summarizing the lattice system, describing the applications that are currently provided and explaining their basic workflow for non-experts in lattice gauge theory. We discuss the parallel performance of the code, and highlight some notable aspects of the documentation for those interested in contributing to its future development.

Hybrid MPI-OpenMP Parallelism in the ONETEP Linear-Scaling Electronic Structure Code: Application to the Delamination of Cellulose Nanofibrils.

PubMed

Wilkinson, Karl A; Hine, Nicholas D M; Skylaris, Chris-Kriton

2014-11-11

We present a hybrid MPI-OpenMP implementation of Linear-Scaling Density Functional Theory within the ONETEP code. We illustrate its performance on a range of high performance computing (HPC) platforms comprising shared-memory nodes with fast interconnect. Our work has focused on applying OpenMP parallelism to the routines which dominate the computational load, attempting where possible to parallelize different loops from those already parallelized within MPI. This includes 3D FFT box operations, sparse matrix algebra operations, calculation of integrals, and Ewald summation. While the underlying numerical methods are unchanged, these developments represent significant changes to the algorithms used within ONETEP to distribute the workload across CPU cores. The new hybrid code exhibits much-improved strong scaling relative to the MPI-only code and permits calculations with a much higher ratio of cores to atoms. These developments result in a significantly shorter time to solution than was possible using MPI alone and facilitate the application of the ONETEP code to systems larger than previously feasible. We illustrate this with benchmark calculations from an amyloid fibril trimer containing 41,907 atoms. We use the code to study the mechanism of delamination of cellulose nanofibrils when undergoing sonification, a process which is controlled by a large number of interactions that collectively determine the structural properties of the fibrils. Many energy evaluations were needed for these simulations, and as these systems comprise up to 21,276 atoms this would not have been feasible without the developments described here.

Chebyshev polynomial filtered subspace iteration in the discontinuous Galerkin method for large-scale electronic structure calculations

DOE PAGES

Banerjee, Amartya S.; Lin, Lin; Hu, Wei; ...

2016-10-21

The Discontinuous Galerkin (DG) electronic structure method employs an adaptive local basis (ALB) set to solve the Kohn-Sham equations of density functional theory in a discontinuous Galerkin framework. The adaptive local basis is generated on-the-fly to capture the local material physics and can systematically attain chemical accuracy with only a few tens of degrees of freedom per atom. A central issue for large-scale calculations, however, is the computation of the electron density (and subsequently, ground state properties) from the discretized Hamiltonian in an efficient and scalable manner. We show in this work how Chebyshev polynomial filtered subspace iteration (CheFSI) canmore » be used to address this issue and push the envelope in large-scale materials simulations in a discontinuous Galerkin framework. We describe how the subspace filtering steps can be performed in an efficient and scalable manner using a two-dimensional parallelization scheme, thanks to the orthogonality of the DG basis set and block-sparse structure of the DG Hamiltonian matrix. The on-the-fly nature of the ALB functions requires additional care in carrying out the subspace iterations. We demonstrate the parallel scalability of the DG-CheFSI approach in calculations of large-scale twodimensional graphene sheets and bulk three-dimensional lithium-ion electrolyte systems. In conclusion, employing 55 296 computational cores, the time per self-consistent field iteration for a sample of the bulk 3D electrolyte containing 8586 atoms is 90 s, and the time for a graphene sheet containing 11 520 atoms is 75 s.« less

MMS Observations of Parallel Electric Fields During a Quasi-Perpendicular Bow Shock Crossing

NASA Astrophysics Data System (ADS)

Goodrich, K.; Schwartz, S. J.; Ergun, R.; Wilder, F. D.; Holmes, J.; Burch, J. L.; Gershman, D. J.; Giles, B. L.; Khotyaintsev, Y. V.; Le Contel, O.; Lindqvist, P. A.; Strangeway, R. J.; Russell, C.; Torbert, R. B.

2016-12-01

Previous observations of the terrestrial bow shock have frequently shown large-amplitude fluctuations in the parallel electric field. These parallel electric fields are seen as both nonlinear solitary structures, such as double layers and electron phase-space holes, and short-wavelength waves, which can reach amplitudes greater than 100 mV/m. The Magnetospheric Multi-Scale (MMS) Mission has crossed the Earth's bow shock more than 200 times. The parallel electric field signatures observed in these crossings are seen in very discrete packets and evolve over time scales of less than a second, indicating the presence of a wealth of kinetic-scale activity. The high time resolution of the Fast Particle Instrument (FPI) available on MMS offers greater detail of the kinetic-scale physics that occur at bow shocks than ever before, allowing greater insight into the overall effect of these observed electric fields. We present a characterization of these parallel electric fields found in a single bow shock event and how it reflects the kinetic-scale activity that can occur at the terrestrial bow shock.

Scaling of Multimillion-Atom Biological Molecular Dynamics Simulation on a Petascale Supercomputer

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

Schulz, Roland; Lindner, Benjamin; Petridis, Loukas

2009-01-01

A strategy is described for a fast all-atom molecular dynamics simulation of multimillion-atom biological systems on massively parallel supercomputers. The strategy is developed using benchmark systems of particular interest to bioenergy research, comprising models of cellulose and lignocellulosic biomass in an aqueous solution. The approach involves using the reaction field (RF) method for the computation of long-range electrostatic interactions, which permits efficient scaling on many thousands of cores. Although the range of applicability of the RF method for biomolecular systems remains to be demonstrated, for the benchmark systems the use of the RF produces molecular dipole moments, Kirkwood G factors,more » other structural properties, and mean-square fluctuations in excellent agreement with those obtained with the commonly used Particle Mesh Ewald method. With RF, three million- and five million atom biological systems scale well up to 30k cores, producing 30 ns/day. Atomistic simulations of very large systems for time scales approaching the microsecond would, therefore, appear now to be within reach.« less

Scaling of Multimillion-Atom Biological Molecular Dynamics Simulation on a Petascale Supercomputer.

PubMed

Schulz, Roland; Lindner, Benjamin; Petridis, Loukas; Smith, Jeremy C

2009-10-13

A strategy is described for a fast all-atom molecular dynamics simulation of multimillion-atom biological systems on massively parallel supercomputers. The strategy is developed using benchmark systems of particular interest to bioenergy research, comprising models of cellulose and lignocellulosic biomass in an aqueous solution. The approach involves using the reaction field (RF) method for the computation of long-range electrostatic interactions, which permits efficient scaling on many thousands of cores. Although the range of applicability of the RF method for biomolecular systems remains to be demonstrated, for the benchmark systems the use of the RF produces molecular dipole moments, Kirkwood G factors, other structural properties, and mean-square fluctuations in excellent agreement with those obtained with the commonly used Particle Mesh Ewald method. With RF, three million- and five million-atom biological systems scale well up to ∼30k cores, producing ∼30 ns/day. Atomistic simulations of very large systems for time scales approaching the microsecond would, therefore, appear now to be within reach.

Frequency-encoded photonic qubits for scalable quantum information processing

DOE PAGES

Lukens, Joseph M.; Lougovski, Pavel

2016-12-21

Among the objectives for large-scale quantum computation is the quantum interconnect: a device that uses photons to interface qubits that otherwise could not interact. However, the current approaches require photons indistinguishable in frequency—a major challenge for systems experiencing different local environments or of different physical compositions altogether. Here, we develop an entirely new platform that actually exploits such frequency mismatch for processing quantum information. Labeled “spectral linear optical quantum computation” (spectral LOQC), our protocol offers favorable linear scaling of optical resources and enjoys an unprecedented degree of parallelism, as an arbitrary Ν-qubit quantum gate may be performed in parallel onmore » multiple Ν-qubit sets in the same linear optical device. Here, not only does spectral LOQC offer new potential for optical interconnects, but it also brings the ubiquitous technology of high-speed fiber optics to bear on photonic quantum information, making wavelength-configurable and robust optical quantum systems within reach.« less

Frequency-encoded photonic qubits for scalable quantum information processing

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

Lukens, Joseph M.; Lougovski, Pavel

Among the objectives for large-scale quantum computation is the quantum interconnect: a device that uses photons to interface qubits that otherwise could not interact. However, the current approaches require photons indistinguishable in frequency—a major challenge for systems experiencing different local environments or of different physical compositions altogether. Here, we develop an entirely new platform that actually exploits such frequency mismatch for processing quantum information. Labeled “spectral linear optical quantum computation” (spectral LOQC), our protocol offers favorable linear scaling of optical resources and enjoys an unprecedented degree of parallelism, as an arbitrary Ν-qubit quantum gate may be performed in parallel onmore » multiple Ν-qubit sets in the same linear optical device. Here, not only does spectral LOQC offer new potential for optical interconnects, but it also brings the ubiquitous technology of high-speed fiber optics to bear on photonic quantum information, making wavelength-configurable and robust optical quantum systems within reach.« less

An FPGA-Based Massively Parallel Neuromorphic Cortex Simulator

PubMed Central

Wang, Runchun M.; Thakur, Chetan S.; van Schaik, André

2018-01-01

This paper presents a massively parallel and scalable neuromorphic cortex simulator designed for simulating large and structurally connected spiking neural networks, such as complex models of various areas of the cortex. The main novelty of this work is the abstraction of a neuromorphic architecture into clusters represented by minicolumns and hypercolumns, analogously to the fundamental structural units observed in neurobiology. Without this approach, simulating large-scale fully connected networks needs prohibitively large memory to store look-up tables for point-to-point connections. Instead, we use a novel architecture, based on the structural connectivity in the neocortex, such that all the required parameters and connections can be stored in on-chip memory. The cortex simulator can be easily reconfigured for simulating different neural networks without any change in hardware structure by programming the memory. A hierarchical communication scheme allows one neuron to have a fan-out of up to 200 k neurons. As a proof-of-concept, an implementation on one Altera Stratix V FPGA was able to simulate 20 million to 2.6 billion leaky-integrate-and-fire (LIF) neurons in real time. We verified the system by emulating a simplified auditory cortex (with 100 million neurons). This cortex simulator achieved a low power dissipation of 1.62 μW per neuron. With the advent of commercially available FPGA boards, our system offers an accessible and scalable tool for the design, real-time simulation, and analysis of large-scale spiking neural networks. PMID:29692702

An FPGA-Based Massively Parallel Neuromorphic Cortex Simulator.

PubMed

Wang, Runchun M; Thakur, Chetan S; van Schaik, André

2018-01-01

This paper presents a massively parallel and scalable neuromorphic cortex simulator designed for simulating large and structurally connected spiking neural networks, such as complex models of various areas of the cortex. The main novelty of this work is the abstraction of a neuromorphic architecture into clusters represented by minicolumns and hypercolumns, analogously to the fundamental structural units observed in neurobiology. Without this approach, simulating large-scale fully connected networks needs prohibitively large memory to store look-up tables for point-to-point connections. Instead, we use a novel architecture, based on the structural connectivity in the neocortex, such that all the required parameters and connections can be stored in on-chip memory. The cortex simulator can be easily reconfigured for simulating different neural networks without any change in hardware structure by programming the memory. A hierarchical communication scheme allows one neuron to have a fan-out of up to 200 k neurons. As a proof-of-concept, an implementation on one Altera Stratix V FPGA was able to simulate 20 million to 2.6 billion leaky-integrate-and-fire (LIF) neurons in real time. We verified the system by emulating a simplified auditory cortex (with 100 million neurons). This cortex simulator achieved a low power dissipation of 1.62 μW per neuron. With the advent of commercially available FPGA boards, our system offers an accessible and scalable tool for the design, real-time simulation, and analysis of large-scale spiking neural networks.

Efficient parallel simulation of CO2 geologic sequestration insaline aquifers

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

Zhang, Keni; Doughty, Christine; Wu, Yu-Shu

2007-01-01

An efficient parallel simulator for large-scale, long-termCO2 geologic sequestration in saline aquifers has been developed. Theparallel simulator is a three-dimensional, fully implicit model thatsolves large, sparse linear systems arising from discretization of thepartial differential equations for mass and energy balance in porous andfractured media. The simulator is based on the ECO2N module of the TOUGH2code and inherits all the process capabilities of the single-CPU TOUGH2code, including a comprehensive description of the thermodynamics andthermophysical properties of H2O-NaCl- CO2 mixtures, modeling singleand/or two-phase isothermal or non-isothermal flow processes, two-phasemixtures, fluid phases appearing or disappearing, as well as saltprecipitation or dissolution. The newmore » parallel simulator uses MPI forparallel implementation, the METIS software package for simulation domainpartitioning, and the iterative parallel linear solver package Aztec forsolving linear equations by multiple processors. In addition, theparallel simulator has been implemented with an efficient communicationscheme. Test examples show that a linear or super-linear speedup can beobtained on Linux clusters as well as on supercomputers. Because of thesignificant improvement in both simulation time and memory requirement,the new simulator provides a powerful tool for tackling larger scale andmore complex problems than can be solved by single-CPU codes. Ahigh-resolution simulation example is presented that models buoyantconvection, induced by a small increase in brine density caused bydissolution of CO2.« less

Application of high-performance computing to numerical simulation of human movement

NASA Technical Reports Server (NTRS)

Anderson, F. C.; Ziegler, J. M.; Pandy, M. G.; Whalen, R. T.

1995-01-01

We have examined the feasibility of using massively-parallel and vector-processing supercomputers to solve large-scale optimization problems for human movement. Specifically, we compared the computational expense of determining the optimal controls for the single support phase of gait using a conventional serial machine (SGI Iris 4D25), a MIMD parallel machine (Intel iPSC/860), and a parallel-vector-processing machine (Cray Y-MP 8/864). With the human body modeled as a 14 degree-of-freedom linkage actuated by 46 musculotendinous units, computation of the optimal controls for gait could take up to 3 months of CPU time on the Iris. Both the Cray and the Intel are able to reduce this time to practical levels. The optimal solution for gait can be found with about 77 hours of CPU on the Cray and with about 88 hours of CPU on the Intel. Although the overall speeds of the Cray and the Intel were found to be similar, the unique capabilities of each machine are better suited to different portions of the computational algorithm used. The Intel was best suited to computing the derivatives of the performance criterion and the constraints whereas the Cray was best suited to parameter optimization of the controls. These results suggest that the ideal computer architecture for solving very large-scale optimal control problems is a hybrid system in which a vector-processing machine is integrated into the communication network of a MIMD parallel machine.

Large-scale molecular dynamics simulation of DNA: implementation and validation of the AMBER98 force field in LAMMPS.

PubMed

Grindon, Christina; Harris, Sarah; Evans, Tom; Novik, Keir; Coveney, Peter; Laughton, Charles

2004-07-15

Molecular modelling played a central role in the discovery of the structure of DNA by Watson and Crick. Today, such modelling is done on computers: the more powerful these computers are, the more detailed and extensive can be the study of the dynamics of such biological macromolecules. To fully harness the power of modern massively parallel computers, however, we need to develop and deploy algorithms which can exploit the structure of such hardware. The Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) is a scalable molecular dynamics code including long-range Coulomb interactions, which has been specifically designed to function efficiently on parallel platforms. Here we describe the implementation of the AMBER98 force field in LAMMPS and its validation for molecular dynamics investigations of DNA structure and flexibility against the benchmark of results obtained with the long-established code AMBER6 (Assisted Model Building with Energy Refinement, version 6). Extended molecular dynamics simulations on the hydrated DNA dodecamer d(CTTTTGCAAAAG)(2), which has previously been the subject of extensive dynamical analysis using AMBER6, show that it is possible to obtain excellent agreement in terms of static, dynamic and thermodynamic parameters between AMBER6 and LAMMPS. In comparison with AMBER6, LAMMPS shows greatly improved scalability in massively parallel environments, opening up the possibility of efficient simulations of order-of-magnitude larger systems and/or for order-of-magnitude greater simulation times.

Parallel Simulation of Three-Dimensional Free Surface Fluid Flow Problems

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

BAER,THOMAS A.; SACKINGER,PHILIP A.; SUBIA,SAMUEL R.

1999-10-14

Simulation of viscous three-dimensional fluid flow typically involves a large number of unknowns. When free surfaces are included, the number of unknowns increases dramatically. Consequently, this class of problem is an obvious application of parallel high performance computing. We describe parallel computation of viscous, incompressible, free surface, Newtonian fluid flow problems that include dynamic contact fines. The Galerkin finite element method was used to discretize the fully-coupled governing conservation equations and a ''pseudo-solid'' mesh mapping approach was used to determine the shape of the free surface. In this approach, the finite element mesh is allowed to deform to satisfy quasi-staticmore » solid mechanics equations subject to geometric or kinematic constraints on the boundaries. As a result, nodal displacements must be included in the set of unknowns. Other issues discussed are the proper constraints appearing along the dynamic contact line in three dimensions. Issues affecting efficient parallel simulations include problem decomposition to equally distribute computational work among a SPMD computer and determination of robust, scalable preconditioners for the distributed matrix systems that must be solved. Solution continuation strategies important for serial simulations have an enhanced relevance in a parallel coquting environment due to the difficulty of solving large scale systems. Parallel computations will be demonstrated on an example taken from the coating flow industry: flow in the vicinity of a slot coater edge. This is a three dimensional free surface problem possessing a contact line that advances at the web speed in one region but transitions to static behavior in another region. As such, a significant fraction of the computational time is devoted to processing boundary data. Discussion focuses on parallel speed ups for fixed problem size, a class of problems of immediate practical importance.« less

A master-slave parallel hybrid multi-objective evolutionary algorithm for groundwater remediation design under general hydrogeological conditions

NASA Astrophysics Data System (ADS)

Wu, J.; Yang, Y.; Luo, Q.; Wu, J.

2012-12-01

This study presents a new hybrid multi-objective evolutionary algorithm, the niched Pareto tabu search combined with a genetic algorithm (NPTSGA), whereby the global search ability of niched Pareto tabu search (NPTS) is improved by the diversification of candidate solutions arose from the evolving nondominated sorting genetic algorithm II (NSGA-II) population. Also, the NPTSGA coupled with the commonly used groundwater flow and transport codes, MODFLOW and MT3DMS, is developed for multi-objective optimal design of groundwater remediation systems. The proposed methodology is then applied to a large-scale field groundwater remediation system for cleanup of large trichloroethylene (TCE) plume at the Massachusetts Military Reservation (MMR) in Cape Cod, Massachusetts. Furthermore, a master-slave (MS) parallelization scheme based on the Message Passing Interface (MPI) is incorporated into the NPTSGA to implement objective function evaluations in distributed processor environment, which can greatly improve the efficiency of the NPTSGA in finding Pareto-optimal solutions to the real-world application. This study shows that the MS parallel NPTSGA in comparison with the original NPTS and NSGA-II can balance the tradeoff between diversity and optimality of solutions during the search process and is an efficient and effective tool for optimizing the multi-objective design of groundwater remediation systems under complicated hydrogeologic conditions.

Performance of parallel computation using CUDA for solving the one-dimensional elasticity equations

NASA Astrophysics Data System (ADS)

Darmawan, J. B. B.; Mungkasi, S.

2017-01-01

In this paper, we investigate the performance of parallel computation in solving the one-dimensional elasticity equations. Elasticity equations are usually implemented in engineering science. Solving these equations fast and efficiently is desired. Therefore, we propose the use of parallel computation. Our parallel computation uses CUDA of the NVIDIA. Our research results show that parallel computation using CUDA has a great advantage and is powerful when the computation is of large scale.

Parallel VLSI architecture emulation and the organization of APSA/MPP

NASA Technical Reports Server (NTRS)

Odonnell, John T.

1987-01-01

The Applicative Programming System Architecture (APSA) combines an applicative language interpreter with a novel parallel computer architecture that is well suited for Very Large Scale Integration (VLSI) implementation. The Massively Parallel Processor (MPP) can simulate VLSI circuits by allocating one processing element in its square array to an area on a square VLSI chip. As long as there are not too many long data paths, the MPP can simulate a VLSI clock cycle very rapidly. The APSA circuit contains a binary tree with a few long paths and many short ones. A skewed H-tree layout allows every processing element to simulate a leaf cell and up to four tree nodes, with no loss in parallelism. Emulation of a key APSA algorithm on the MPP resulted in performance 16,000 times faster than a Vax. This speed will make it possible for the APSA language interpreter to run fast enough to support research in parallel list processing algorithms.

Parallel and Efficient Sensitivity Analysis of Microscopy Image Segmentation Workflows in Hybrid Systems

PubMed Central

Barreiros, Willian; Teodoro, George; Kurc, Tahsin; Kong, Jun; Melo, Alba C. M. A.; Saltz, Joel

2017-01-01

We investigate efficient sensitivity analysis (SA) of algorithms that segment and classify image features in a large dataset of high-resolution images. Algorithm SA is the process of evaluating variations of methods and parameter values to quantify differences in the output. A SA can be very compute demanding because it requires re-processing the input dataset several times with different parameters to assess variations in output. In this work, we introduce strategies to efficiently speed up SA via runtime optimizations targeting distributed hybrid systems and reuse of computations from runs with different parameters. We evaluate our approach using a cancer image analysis workflow on a hybrid cluster with 256 nodes, each with an Intel Phi and a dual socket CPU. The SA attained a parallel efficiency of over 90% on 256 nodes. The cooperative execution using the CPUs and the Phi available in each node with smart task assignment strategies resulted in an additional speedup of about 2×. Finally, multi-level computation reuse lead to an additional speedup of up to 2.46× on the parallel version. The level of performance attained with the proposed optimizations will allow the use of SA in large-scale studies. PMID:29081725

On the impact of approximate computation in an analog DeSTIN architecture.

PubMed

Young, Steven; Lu, Junjie; Holleman, Jeremy; Arel, Itamar

2014-05-01

Deep machine learning (DML) holds the potential to revolutionize machine learning by automating rich feature extraction, which has become the primary bottleneck of human engineering in pattern recognition systems. However, the heavy computational burden renders DML systems implemented on conventional digital processors impractical for large-scale problems. The highly parallel computations required to implement large-scale deep learning systems are well suited to custom hardware. Analog computation has demonstrated power efficiency advantages of multiple orders of magnitude relative to digital systems while performing nonideal computations. In this paper, we investigate typical error sources introduced by analog computational elements and their impact on system-level performance in DeSTIN--a compositional deep learning architecture. These inaccuracies are evaluated on a pattern classification benchmark, clearly demonstrating the robustness of the underlying algorithm to the errors introduced by analog computational elements. A clear understanding of the impacts of nonideal computations is necessary to fully exploit the efficiency of analog circuits.

Parallel-vector solution of large-scale structural analysis problems on supercomputers

NASA Technical Reports Server (NTRS)

Storaasli, Olaf O.; Nguyen, Duc T.; Agarwal, Tarun K.

1989-01-01

A direct linear equation solution method based on the Choleski factorization procedure is presented which exploits both parallel and vector features of supercomputers. The new equation solver is described, and its performance is evaluated by solving structural analysis problems on three high-performance computers. The method has been implemented using Force, a generic parallel FORTRAN language.

Correlation of generation interval and scale of large-scale submarine landslides using 3D seismic data off Shimokita Peninsula, Northeast Japan

NASA Astrophysics Data System (ADS)

Nakamura, Yuki; Ashi, Juichiro; Morita, Sumito

2016-04-01

To clarify timing and scale of past submarine landslides is important to understand formation processes of the landslides. The study area is in a part of continental slope of the Japan Trench, where a number of large-scale submarine landslide (slump) deposits have been identified in Pliocene and Quaternary formations by analysing METI's 3D seismic data "Sanrikuoki 3D" off Shimokita Peninsula (Morita et al., 2011). As structural features, swarm of parallel dikes which are likely dewatering paths formed accompanying the slumping deformation, and slip directions are basically perpendicular to the parallel dikes. Therefore, parallel dikes are good indicator for estimation of slip directions. Slip direction of each slide was determined one kilometre grid in the survey area of 40 km x 20 km. The remarkable slip direction varies from Pliocene to Quaternary in the survey area. Parallel dike structure is also available for the distinguishment of the slump deposit and normal deposit on time slice images. By tracing outline of slump deposits at each depth, we identified general morphology of the overall slump deposits, and calculated the volume of the extracted slump deposits so as to estimate the scale of each event. We investigated temporal and spatial variation of depositional pattern of the slump deposits. Calculating the generation interval of the slumps, some periodicity is likely recognized, especially large slump do not occur in succession. Additionally, examining the relationship of the cumulative volume and the generation interval, certain correlation is observed in Pliocene and Quaternary. Key words: submarine landslides, 3D seismic data, Shimokita Peninsula

A nonrecursive order N preconditioned conjugate gradient: Range space formulation of MDOF dynamics

NASA Technical Reports Server (NTRS)

Kurdila, Andrew J.

1990-01-01

While excellent progress has been made in deriving algorithms that are efficient for certain combinations of system topologies and concurrent multiprocessing hardware, several issues must be resolved to incorporate transient simulation in the control design process for large space structures. Specifically, strategies must be developed that are applicable to systems with numerous degrees of freedom. In addition, the algorithms must have a growth potential in that they must also be amenable to implementation on forthcoming parallel system architectures. For mechanical system simulation, this fact implies that algorithms are required that induce parallelism on a fine scale, suitable for the emerging class of highly parallel processors; and transient simulation methods must be automatically load balancing for a wider collection of system topologies and hardware configurations. These problems are addressed by employing a combination range space/preconditioned conjugate gradient formulation of multi-degree-of-freedom dynamics. The method described has several advantages. In a sequential computing environment, the method has the features that: by employing regular ordering of the system connectivity graph, an extremely efficient preconditioner can be derived from the 'range space metric', as opposed to the system coefficient matrix; because of the effectiveness of the preconditioner, preliminary studies indicate that the method can achieve performance rates that depend linearly upon the number of substructures, hence the title 'Order N'; and the method is non-assembling. Furthermore, the approach is promising as a potential parallel processing algorithm in that the method exhibits a fine parallel granularity suitable for a wide collection of combinations of physical system topologies/computer architectures; and the method is easily load balanced among processors, and does not rely upon system topology to induce parallelism.

Study of Solid State Drives performance in PROOF distributed analysis system

NASA Astrophysics Data System (ADS)

Panitkin, S. Y.; Ernst, M.; Petkus, R.; Rind, O.; Wenaus, T.

2010-04-01

Solid State Drives (SSD) is a promising storage technology for High Energy Physics parallel analysis farms. Its combination of low random access time and relatively high read speed is very well suited for situations where multiple jobs concurrently access data located on the same drive. It also has lower energy consumption and higher vibration tolerance than Hard Disk Drive (HDD) which makes it an attractive choice in many applications raging from personal laptops to large analysis farms. The Parallel ROOT Facility - PROOF is a distributed analysis system which allows to exploit inherent event level parallelism of high energy physics data. PROOF is especially efficient together with distributed local storage systems like Xrootd, when data are distributed over computing nodes. In such an architecture the local disk subsystem I/O performance becomes a critical factor, especially when computing nodes use multi-core CPUs. We will discuss our experience with SSDs in PROOF environment. We will compare performance of HDD with SSD in I/O intensive analysis scenarios. In particular we will discuss PROOF system performance scaling with a number of simultaneously running analysis jobs.

Casimir force in O(n) systems with a diffuse interface.

PubMed

Dantchev, Daniel; Grüneberg, Daniel

2009-04-01

We study the behavior of the Casimir force in O(n) systems with a diffuse interface and slab geometry infinity;{d-1}xL , where 2infinity limit of O(n) models with antiperiodic boundary conditions applied along the finite dimension L of the film. We observe that the Casimir amplitude Delta_{Casimir}(dmid R:J_{ perpendicular},J_{ parallel}) of the anisotropic d -dimensional system is related to that of the isotropic system Delta_{Casimir}(d) via Delta_{Casimir}(dmid R:J_{ perpendicular},J_{ parallel})=(J_{ perpendicular}J_{ parallel});{(d-1)2}Delta_{Casimir}(d) . For d=3 we derive the exact Casimir amplitude Delta_{Casimir}(3,mid R:J_{ perpendicular},J_{ parallel})=[Cl_{2}(pi3)3-zeta(3)(6pi)](J_{ perpendicular}J_{ parallel}) , as well as the exact scaling functions of the Casimir force and of the helicity modulus Upsilon(T,L) . We obtain that beta_{c}Upsilon(T_{c},L)=(2pi;{2})[Cl_{2}(pi3)3+7zeta(3)(30pi)](J_{ perpendicular}J_{ parallel})L;{-1} , where T_{c} is the critical temperature of the bulk system. We find that the contributions in the excess free energy due to the existence of a diffuse interface result in a repulsive Casimir force in the whole temperature region.

Hyperswitch communication network

NASA Technical Reports Server (NTRS)

Peterson, J.; Pniel, M.; Upchurch, E.

1991-01-01

The Hyperswitch Communication Network (HCN) is a large scale parallel computer prototype being developed at JPL. Commercial versions of the HCN computer are planned. The HCN computer being designed is a message passing multiple instruction multiple data (MIMD) computer, and offers many advantages in price-performance ratio, reliability and availability, and manufacturing over traditional uniprocessors and bus based multiprocessors. The design of the HCN operating system is a uniquely flexible environment that combines both parallel processing and distributed processing. This programming paradigm can achieve a balance among the following competing factors: performance in processing and communications, user friendliness, and fault tolerance. The prototype is being designed to accommodate a maximum of 64 state of the art microprocessors. The HCN is classified as a distributed supercomputer. The HCN system is described, and the performance/cost analysis and other competing factors within the system design are reviewed.

Exploring the protein folding free energy landscape: coupling replica exchange method with P3ME/RESPA algorithm.

PubMed

Zhou, Ruhong

2004-05-01

A highly parallel replica exchange method (REM) that couples with a newly developed molecular dynamics algorithm particle-particle particle-mesh Ewald (P3ME)/RESPA has been proposed for efficient sampling of protein folding free energy landscape. The algorithm is then applied to two separate protein systems, beta-hairpin and a designed protein Trp-cage. The all-atom OPLSAA force field with an explicit solvent model is used for both protein folding simulations. Up to 64 replicas of solvated protein systems are simulated in parallel over a wide range of temperatures. The combined trajectories in temperature and configurational space allow a replica to overcome free energy barriers present at low temperatures. These large scale simulations reveal detailed results on folding mechanisms, intermediate state structures, thermodynamic properties and the temperature dependences for both protein systems.

Continuous Easy-Plane Deconfined Phase Transition on the Kagome Lattice

NASA Astrophysics Data System (ADS)

Zhang, Xue-Feng; He, Yin-Chen; Eggert, Sebastian; Moessner, Roderich; Pollmann, Frank

2018-03-01

We use large scale quantum Monte Carlo simulations to study an extended Hubbard model of hard core bosons on the kagome lattice. In the limit of strong nearest-neighbor interactions at 1 /3 filling, the interplay between frustration and quantum fluctuations leads to a valence bond solid ground state. The system undergoes a quantum phase transition to a superfluid phase as the interaction strength is decreased. It is still under debate whether the transition is weakly first order or represents an unconventional continuous phase transition. We present a theory in terms of an easy plane noncompact C P1 gauge theory describing the phase transition at 1 /3 filling. Utilizing large scale quantum Monte Carlo simulations with parallel tempering in the canonical ensemble up to 15552 spins, we provide evidence that the phase transition is continuous at exactly 1 /3 filling. A careful finite size scaling analysis reveals an unconventional scaling behavior hinting at deconfined quantum criticality.

Performance Analysis and Scaling Behavior of the Terrestrial Systems Modeling Platform TerrSysMP in Large-Scale Supercomputing Environments

NASA Astrophysics Data System (ADS)

Kollet, S. J.; Goergen, K.; Gasper, F.; Shresta, P.; Sulis, M.; Rihani, J.; Simmer, C.; Vereecken, H.

2013-12-01

In studies of the terrestrial hydrologic, energy and biogeochemical cycles, integrated multi-physics simulation platforms take a central role in characterizing non-linear interactions, variances and uncertainties of system states and fluxes in reciprocity with observations. Recently developed integrated simulation platforms attempt to honor the complexity of the terrestrial system across multiple time and space scales from the deeper subsurface including groundwater dynamics into the atmosphere. Technically, this requires the coupling of atmospheric, land surface, and subsurface-surface flow models in supercomputing environments, while ensuring a high-degree of efficiency in the utilization of e.g., standard Linux clusters and massively parallel resources. A systematic performance analysis including profiling and tracing in such an application is crucial in the understanding of the runtime behavior, to identify optimum model settings, and is an efficient way to distinguish potential parallel deficiencies. On sophisticated leadership-class supercomputers, such as the 28-rack 5.9 petaFLOP IBM Blue Gene/Q 'JUQUEEN' of the Jülich Supercomputing Centre (JSC), this is a challenging task, but even more so important, when complex coupled component models are to be analysed. Here we want to present our experience from coupling, application tuning (e.g. 5-times speedup through compiler optimizations), parallel scaling and performance monitoring of the parallel Terrestrial Systems Modeling Platform TerrSysMP. The modeling platform consists of the weather prediction system COSMO of the German Weather Service; the Community Land Model, CLM of NCAR; and the variably saturated surface-subsurface flow code ParFlow. The model system relies on the Multiple Program Multiple Data (MPMD) execution model where the external Ocean-Atmosphere-Sea-Ice-Soil coupler (OASIS3) links the component models. TerrSysMP has been instrumented with the performance analysis tool Scalasca and analyzed on JUQUEEN with processor counts on the order of 10,000. The instrumentation is used in weak and strong scaling studies with real data cases and hypothetical idealized numerical experiments for detailed profiling and tracing analysis. The profiling is not only useful in identifying wait states that are due to the MPMD execution model, but also in fine-tuning resource allocation to the component models in search of the most suitable load balancing. This is especially necessary, as with numerical experiments that cover multiple (high resolution) spatial scales, the time stepping, coupling frequencies, and communication overheads are constantly shifting, which makes it necessary to re-determine the model setup with each new experimental design.

First Observations of a Foreshock Bubble at Earth: Implications for Magnetospheric Activity and Energetic Particle Acceleration

NASA Technical Reports Server (NTRS)

Turner, D. L.; Omidi, N.; Sibeck, D. G.; Angelopoulos, V.

2011-01-01

Earth?s foreshock, which is the quasi-parallel region upstream of the bow shock, is a unique plasma region capable of generating several kinds of large-scale phenomena, each of which can impact the magnetosphere resulting in global effects. Interestingly, such phenomena have also been observed at planetary foreshocks throughout our solar system. Recently, a new type of foreshock phenomena has been predicted: foreshock bubbles, which are large-scale disruptions of both the foreshock and incident solar wind plasmas that can result in global magnetospheric disturbances. Here we present unprecedented, multi-point observations of foreshock bubbles at Earth using a combination of spacecraft and ground observations primarily from the Time History of Events and Macroscale Interactions during Substorms (THEMIS) mission, and we include detailed analysis of the events? global effects on the magnetosphere and the energetic ions and electrons accelerated by them, potentially by a combination of first and second order Fermi and shock drift acceleration processes. This new phenomena should play a role in energetic particle acceleration at collisionless, quasi-parallel shocks throughout the Universe.

GPU-based Space Situational Awareness Simulation utilising Parallelism for Enhanced Multi-sensor Management

NASA Astrophysics Data System (ADS)

Hobson, T.; Clarkson, V.

2012-09-01

As a result of continual space activity since the 1950s, there are now a large number of man-made Resident Space Objects (RSOs) orbiting the Earth. Because of the large number of items and their relative speeds, the possibility of destructive collisions involving important space assets is now of significant concern to users and operators of space-borne technologies. As a result, a growing number of international agencies are researching methods for improving techniques to maintain Space Situational Awareness (SSA). Computer simulation is a method commonly used by many countries to validate competing methodologies prior to full scale adoption. The use of supercomputing and/or reduced scale testing is often necessary to effectively simulate such a complex problem on todays computers. Recently the authors presented a simulation aimed at reducing the computational burden by selecting the minimum level of fidelity necessary for contrasting methodologies and by utilising multi-core CPU parallelism for increased computational efficiency. The resulting simulation runs on a single PC while maintaining the ability to effectively evaluate competing methodologies. Nonetheless, the ability to control the scale and expand upon the computational demands of the sensor management system is limited. In this paper, we examine the advantages of increasing the parallelism of the simulation by means of General Purpose computing on Graphics Processing Units (GPGPU). As many sub-processes pertaining to SSA management are independent, we demonstrate how parallelisation via GPGPU has the potential to significantly enhance not only research into techniques for maintaining SSA, but also to enhance the level of sophistication of existing space surveillance sensors and sensor management systems. Nonetheless, the use of GPGPU imposes certain limitations and adds to the implementation complexity, both of which require consideration to achieve an effective system. We discuss these challenges and how they can be overcome. We further describe an application of the parallelised system where visibility prediction is used to enhance sensor management. This facilitates significant improvement in maximum catalogue error when RSOs become temporarily unobservable. The objective is to demonstrate the enhanced scalability and increased computational capability of the system.

Liter-scale production of uniform gas bubbles via parallelization of flow-focusing generators.

PubMed

Jeong, Heon-Ho; Yadavali, Sagar; Issadore, David; Lee, Daeyeon

2017-07-25

Microscale gas bubbles have demonstrated enormous utility as versatile templates for the synthesis of functional materials in medicine, ultra-lightweight materials and acoustic metamaterials. In many of these applications, high uniformity of the size of the gas bubbles is critical to achieve the desired properties and functionality. While microfluidics have been used with success to create gas bubbles that have a uniformity not achievable using conventional methods, the inherently low volumetric flow rate of microfluidics has limited its use in most applications. Parallelization of liquid droplet generators, in which many droplet generators are incorporated onto a single chip, has shown great promise for the large scale production of monodisperse liquid emulsion droplets. However, the scale-up of monodisperse gas bubbles using such an approach has remained a challenge because of possible coupling between parallel bubbles generators and feedback effects from the downstream channels. In this report, we systematically investigate the effect of factors such as viscosity of the continuous phase, capillary number, and gas pressure as well as the channel uniformity on the size distribution of gas bubbles in a parallelized microfluidic device. We show that, by optimizing the flow conditions, a device with 400 parallel flow focusing generators on a footprint of 5 × 5 cm 2 can be used to generate gas bubbles with a coefficient of variation of less than 5% at a production rate of approximately 1 L h -1 . Our results suggest that the optimization of flow conditions using a device with a small number (e.g., 8) of parallel FFGs can facilitate large-scale bubble production.

Scalable Molecular Dynamics with NAMD

PubMed Central

Phillips, James C.; Braun, Rosemary; Wang, Wei; Gumbart, James; Tajkhorshid, Emad; Villa, Elizabeth; Chipot, Christophe; Skeel, Robert D.; Kalé, Laxmikant; Schulten, Klaus

2008-01-01

NAMD is a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems. NAMD scales to hundreds of processors on high-end parallel platforms, as well as tens of processors on low-cost commodity clusters, and also runs on individual desktop and laptop computers. NAMD works with AMBER and CHARMM potential functions, parameters, and file formats. This paper, directed to novices as well as experts, first introduces concepts and methods used in the NAMD program, describing the classical molecular dynamics force field, equations of motion, and integration methods along with the efficient electrostatics evaluation algorithms employed and temperature and pressure controls used. Features for steering the simulation across barriers and for calculating both alchemical and conformational free energy differences are presented. The motivations for and a roadmap to the internal design of NAMD, implemented in C++ and based on Charm++ parallel objects, are outlined. The factors affecting the serial and parallel performance of a simulation are discussed. Next, typical NAMD use is illustrated with representative applications to a small, a medium, and a large biomolecular system, highlighting particular features of NAMD, e.g., the Tcl scripting language. Finally, the paper provides a list of the key features of NAMD and discusses the benefits of combining NAMD with the molecular graphics/sequence analysis software VMD and the grid computing/collaboratory software BioCoRE. NAMD is distributed free of charge with source code at www.ks.uiuc.edu. PMID:16222654

Parallelization Issues and Particle-In Codes.

NASA Astrophysics Data System (ADS)

Elster, Anne Cathrine

1994-01-01

"Everything should be made as simple as possible, but not simpler." Albert Einstein. The field of parallel scientific computing has concentrated on parallelization of individual modules such as matrix solvers and factorizers. However, many applications involve several interacting modules. Our analyses of a particle-in-cell code modeling charged particles in an electric field, show that these accompanying dependencies affect data partitioning and lead to new parallelization strategies concerning processor, memory and cache utilization. Our test-bed, a KSR1, is a distributed memory machine with a globally shared addressing space. However, most of the new methods presented hold generally for hierarchical and/or distributed memory systems. We introduce a novel approach that uses dual pointers on the local particle arrays to keep the particle locations automatically partially sorted. Complexity and performance analyses with accompanying KSR benchmarks, have been included for both this scheme and for the traditional replicated grids approach. The latter approach maintains load-balance with respect to particles. However, our results demonstrate it fails to scale properly for problems with large grids (say, greater than 128-by-128) running on as few as 15 KSR nodes, since the extra storage and computation time associated with adding the grid copies, becomes significant. Our grid partitioning scheme, although harder to implement, does not need to replicate the whole grid. Consequently, it scales well for large problems on highly parallel systems. It may, however, require load balancing schemes for non-uniform particle distributions. Our dual pointer approach may facilitate this through dynamically partitioned grids. We also introduce hierarchical data structures that store neighboring grid-points within the same cache -line by reordering the grid indexing. This alignment produces a 25% savings in cache-hits for a 4-by-4 cache. A consideration of the input data's effect on the simulation may lead to further improvements. For example, in the case of mean particle drift, it is often advantageous to partition the grid primarily along the direction of the drift. The particle-in-cell codes for this study were tested using physical parameters, which lead to predictable phenomena including plasma oscillations and two-stream instabilities. An overview of the most central references related to parallel particle codes is also given.

Microfluidic biolector-microfluidic bioprocess control in microtiter plates.

PubMed

Funke, Matthias; Buchenauer, Andreas; Schnakenberg, Uwe; Mokwa, Wilfried; Diederichs, Sylvia; Mertens, Alan; Müller, Carsten; Kensy, Frank; Büchs, Jochen

2010-10-15

In industrial-scale biotechnological processes, the active control of the pH-value combined with the controlled feeding of substrate solutions (fed-batch) is the standard strategy to cultivate both prokaryotic and eukaryotic cells. On the contrary, for small-scale cultivations, much simpler batch experiments with no process control are performed. This lack of process control often hinders researchers to scale-up and scale-down fermentation experiments, because the microbial metabolism and thereby the growth and production kinetics drastically changes depending on the cultivation strategy applied. While small-scale batches are typically performed highly parallel and in high throughput, large-scale cultivations demand sophisticated equipment for process control which is in most cases costly and difficult to handle. Currently, there is no technical system on the market that realizes simple process control in high throughput. The novel concept of a microfermentation system described in this work combines a fiber-optic online-monitoring device for microtiter plates (MTPs)--the BioLector technology--together with microfluidic control of cultivation processes in volumes below 1 mL. In the microfluidic chip, a micropump is integrated to realize distinct substrate flow rates during fed-batch cultivation in microscale. Hence, a cultivation system with several distinct advantages could be established: (1) high information output on a microscale; (2) many experiments can be performed in parallel and be automated using MTPs; (3) this system is user-friendly and can easily be transferred to a disposable single-use system. This article elucidates this new concept and illustrates applications in fermentations of Escherichia coli under pH-controlled and fed-batch conditions in shaken MTPs. Copyright 2010 Wiley Periodicals, Inc.

High performance computing and communications: Advancing the frontiers of information technology

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

NONE

1997-12-31

This report, which supplements the President`s Fiscal Year 1997 Budget, describes the interagency High Performance Computing and Communications (HPCC) Program. The HPCC Program will celebrate its fifth anniversary in October 1996 with an impressive array of accomplishments to its credit. Over its five-year history, the HPCC Program has focused on developing high performance computing and communications technologies that can be applied to computation-intensive applications. Major highlights for FY 1996: (1) High performance computing systems enable practical solutions to complex problems with accuracies not possible five years ago; (2) HPCC-funded research in very large scale networking techniques has been instrumental inmore » the evolution of the Internet, which continues exponential growth in size, speed, and availability of information; (3) The combination of hardware capability measured in gigaflop/s, networking technology measured in gigabit/s, and new computational science techniques for modeling phenomena has demonstrated that very large scale accurate scientific calculations can be executed across heterogeneous parallel processing systems located thousands of miles apart; (4) Federal investments in HPCC software R and D support researchers who pioneered the development of parallel languages and compilers, high performance mathematical, engineering, and scientific libraries, and software tools--technologies that allow scientists to use powerful parallel systems to focus on Federal agency mission applications; and (5) HPCC support for virtual environments has enabled the development of immersive technologies, where researchers can explore and manipulate multi-dimensional scientific and engineering problems. Educational programs fostered by the HPCC Program have brought into classrooms new science and engineering curricula designed to teach computational science. This document contains a small sample of the significant HPCC Program accomplishments in FY 1996.« less

A parallel metaheuristic for large mixed-integer dynamic optimization problems, with applications in computational biology

PubMed Central

Henriques, David; González, Patricia; Doallo, Ramón; Saez-Rodriguez, Julio; Banga, Julio R.

2017-01-01

Background We consider a general class of global optimization problems dealing with nonlinear dynamic models. Although this class is relevant to many areas of science and engineering, here we are interested in applying this framework to the reverse engineering problem in computational systems biology, which yields very large mixed-integer dynamic optimization (MIDO) problems. In particular, we consider the framework of logic-based ordinary differential equations (ODEs). Methods We present saCeSS2, a parallel method for the solution of this class of problems. This method is based on an parallel cooperative scatter search metaheuristic, with new mechanisms of self-adaptation and specific extensions to handle large mixed-integer problems. We have paid special attention to the avoidance of convergence stagnation using adaptive cooperation strategies tailored to this class of problems. Results We illustrate its performance with a set of three very challenging case studies from the domain of dynamic modelling of cell signaling. The simpler case study considers a synthetic signaling pathway and has 84 continuous and 34 binary decision variables. A second case study considers the dynamic modeling of signaling in liver cancer using high-throughput data, and has 135 continuous and 109 binaries decision variables. The third case study is an extremely difficult problem related with breast cancer, involving 690 continuous and 138 binary decision variables. We report computational results obtained in different infrastructures, including a local cluster, a large supercomputer and a public cloud platform. Interestingly, the results show how the cooperation of individual parallel searches modifies the systemic properties of the sequential algorithm, achieving superlinear speedups compared to an individual search (e.g. speedups of 15 with 10 cores), and significantly improving (above a 60%) the performance with respect to a non-cooperative parallel scheme. The scalability of the method is also good (tests were performed using up to 300 cores). Conclusions These results demonstrate that saCeSS2 can be used to successfully reverse engineer large dynamic models of complex biological pathways. Further, these results open up new possibilities for other MIDO-based large-scale applications in the life sciences such as metabolic engineering, synthetic biology, drug scheduling. PMID:28813442

A parallel metaheuristic for large mixed-integer dynamic optimization problems, with applications in computational biology.

PubMed

Penas, David R; Henriques, David; González, Patricia; Doallo, Ramón; Saez-Rodriguez, Julio; Banga, Julio R

2017-01-01

We consider a general class of global optimization problems dealing with nonlinear dynamic models. Although this class is relevant to many areas of science and engineering, here we are interested in applying this framework to the reverse engineering problem in computational systems biology, which yields very large mixed-integer dynamic optimization (MIDO) problems. In particular, we consider the framework of logic-based ordinary differential equations (ODEs). We present saCeSS2, a parallel method for the solution of this class of problems. This method is based on an parallel cooperative scatter search metaheuristic, with new mechanisms of self-adaptation and specific extensions to handle large mixed-integer problems. We have paid special attention to the avoidance of convergence stagnation using adaptive cooperation strategies tailored to this class of problems. We illustrate its performance with a set of three very challenging case studies from the domain of dynamic modelling of cell signaling. The simpler case study considers a synthetic signaling pathway and has 84 continuous and 34 binary decision variables. A second case study considers the dynamic modeling of signaling in liver cancer using high-throughput data, and has 135 continuous and 109 binaries decision variables. The third case study is an extremely difficult problem related with breast cancer, involving 690 continuous and 138 binary decision variables. We report computational results obtained in different infrastructures, including a local cluster, a large supercomputer and a public cloud platform. Interestingly, the results show how the cooperation of individual parallel searches modifies the systemic properties of the sequential algorithm, achieving superlinear speedups compared to an individual search (e.g. speedups of 15 with 10 cores), and significantly improving (above a 60%) the performance with respect to a non-cooperative parallel scheme. The scalability of the method is also good (tests were performed using up to 300 cores). These results demonstrate that saCeSS2 can be used to successfully reverse engineer large dynamic models of complex biological pathways. Further, these results open up new possibilities for other MIDO-based large-scale applications in the life sciences such as metabolic engineering, synthetic biology, drug scheduling.

Parallel Large-Scale Molecular Dynamics Simulation Opens New Perspective to Clarify the Effect of a Porous Structure on the Sintering Process of Ni/YSZ Multiparticles.

PubMed

Xu, Jingxiang; Higuchi, Yuji; Ozawa, Nobuki; Sato, Kazuhisa; Hashida, Toshiyuki; Kubo, Momoji

2017-09-20

Ni sintering in the Ni/YSZ porous anode of a solid oxide fuel cell changes the porous structure, leading to degradation. Preventing sintering and degradation during operation is a great challenge. Usually, a sintering molecular dynamics (MD) simulation model consisting of two particles on a substrate is used; however, the model cannot reflect the porous structure effect on sintering. In our previous study, a multi-nanoparticle sintering modeling method with tens of thousands of atoms revealed the effect of the particle framework and porosity on sintering. However, the method cannot reveal the effect of the particle size on sintering and the effect of sintering on the change in the porous structure. In the present study, we report a strategy to reveal them in the porous structure by using our multi-nanoparticle modeling method and a parallel large-scale multimillion-atom MD simulator. We used this method to investigate the effect of YSZ particle size and tortuosity on sintering and degradation in the Ni/YSZ anodes. Our parallel large-scale MD simulation showed that the sintering degree decreased as the YSZ particle size decreased. The gas fuel diffusion path, which reflects the overpotential, was blocked by pore coalescence during sintering. The degradation of gas diffusion performance increased as the YSZ particle size increased. Furthermore, the gas diffusion performance was quantified by a tortuosity parameter and an optimal YSZ particle size, which is equal to that of Ni, was found for good diffusion after sintering. These findings cannot be obtained by previous MD sintering studies with tens of thousands of atoms. The present parallel large-scale multimillion-atom MD simulation makes it possible to clarify the effects of the particle size and tortuosity on sintering and degradation.

Trace: a high-throughput tomographic reconstruction engine for large-scale datasets

DOE PAGES

Bicer, Tekin; Gursoy, Doga; Andrade, Vincent De; ...

2017-01-28

Here, synchrotron light source and detector technologies enable scientists to perform advanced experiments. These scientific instruments and experiments produce data at such scale and complexity that large-scale computation is required to unleash their full power. One of the widely used data acquisition technique at light sources is Computed Tomography, which can generate tens of GB/s depending on x-ray range. A large-scale tomographic dataset, such as mouse brain, may require hours of computation time with a medium size workstation. In this paper, we present Trace, a data-intensive computing middleware we developed for implementation and parallelization of iterative tomographic reconstruction algorithms. Tracemore » provides fine-grained reconstruction of tomography datasets using both (thread level) shared memory and (process level) distributed memory parallelization. Trace utilizes a special data structure called replicated reconstruction object to maximize application performance. We also present the optimizations we have done on the replicated reconstruction objects and evaluate them using a shale and a mouse brain sinogram. Our experimental evaluations show that the applied optimizations and parallelization techniques can provide 158x speedup (using 32 compute nodes) over single core configuration, which decreases the reconstruction time of a sinogram (with 4501 projections and 22400 detector resolution) from 12.5 hours to less than 5 minutes per iteration.« less

Trace: a high-throughput tomographic reconstruction engine for large-scale datasets

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

Bicer, Tekin; Gursoy, Doga; Andrade, Vincent De

Here, synchrotron light source and detector technologies enable scientists to perform advanced experiments. These scientific instruments and experiments produce data at such scale and complexity that large-scale computation is required to unleash their full power. One of the widely used data acquisition technique at light sources is Computed Tomography, which can generate tens of GB/s depending on x-ray range. A large-scale tomographic dataset, such as mouse brain, may require hours of computation time with a medium size workstation. In this paper, we present Trace, a data-intensive computing middleware we developed for implementation and parallelization of iterative tomographic reconstruction algorithms. Tracemore » provides fine-grained reconstruction of tomography datasets using both (thread level) shared memory and (process level) distributed memory parallelization. Trace utilizes a special data structure called replicated reconstruction object to maximize application performance. We also present the optimizations we have done on the replicated reconstruction objects and evaluate them using a shale and a mouse brain sinogram. Our experimental evaluations show that the applied optimizations and parallelization techniques can provide 158x speedup (using 32 compute nodes) over single core configuration, which decreases the reconstruction time of a sinogram (with 4501 projections and 22400 detector resolution) from 12.5 hours to less than 5 minutes per iteration.« less

SCORPIO: A Scalable Two-Phase Parallel I/O Library With Application To A Large Scale Subsurface Simulator

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

Sreepathi, Sarat; Sripathi, Vamsi; Mills, Richard T

2013-01-01

Inefficient parallel I/O is known to be a major bottleneck among scientific applications employed on supercomputers as the number of processor cores grows into the thousands. Our prior experience indicated that parallel I/O libraries such as HDF5 that rely on MPI-IO do not scale well beyond 10K processor cores, especially on parallel file systems (like Lustre) with single point of resource contention. Our previous optimization efforts for a massively parallel multi-phase and multi-component subsurface simulator (PFLOTRAN) led to a two-phase I/O approach at the application level where a set of designated processes participate in the I/O process by splitting themore » I/O operation into a communication phase and a disk I/O phase. The designated I/O processes are created by splitting the MPI global communicator into multiple sub-communicators. The root process in each sub-communicator is responsible for performing the I/O operations for the entire group and then distributing the data to rest of the group. This approach resulted in over 25X speedup in HDF I/O read performance and 3X speedup in write performance for PFLOTRAN at over 100K processor cores on the ORNL Jaguar supercomputer. This research describes the design and development of a general purpose parallel I/O library, SCORPIO (SCalable block-ORiented Parallel I/O) that incorporates our optimized two-phase I/O approach. The library provides a simplified higher level abstraction to the user, sitting atop existing parallel I/O libraries (such as HDF5) and implements optimized I/O access patterns that can scale on larger number of processors. Performance results with standard benchmark problems and PFLOTRAN indicate that our library is able to maintain the same speedups as before with the added flexibility of being applicable to a wider range of I/O intensive applications.« less

Space Technology 5 Multi-point Observations of Field-aligned Currents: Temporal Variability of Meso-Scale Structures

NASA Technical Reports Server (NTRS)

Le, Guan; Wang, Yongli; Slavin, James A.; Strangeway, Robert J.

2007-01-01

Space Technology 5 (ST5) is a three micro-satellite constellation deployed into a 300 x 4500 km, dawn-dusk, sun-synchronous polar orbit from March 22 to June 21, 2006, for technology validations. In this paper, we present a study of the temporal variability of field-aligned currents using multi-point magnetic field measurements from ST5. The data demonstrate that meso-scale current structures are commonly embedded within large-scale field-aligned current sheets. The meso-scale current structures are very dynamic with highly variable current density and/or polarity in time scales of - 10 min. They exhibit large temporal variations during both quiet and disturbed times in such time scales. On the other hand, the data also shown that the time scales for the currents to be relatively stable are approx. 1 min for meso-scale currents and approx. 10 min for large scale current sheets. These temporal features are obviously associated with dynamic variations of their particle carriers (mainly electrons) as they respond to the variations of the parallel electric field in auroral acceleration region. The characteristic time scales for the temporal variability of meso-scale field-aligned currents are found to be consistent with those of auroral parallel electric field.

Smoldyn on graphics processing units: massively parallel Brownian dynamics simulations.

PubMed

Dematté, Lorenzo

2012-01-01

Space is a very important aspect in the simulation of biochemical systems; recently, the need for simulation algorithms able to cope with space is becoming more and more compelling. Complex and detailed models of biochemical systems need to deal with the movement of single molecules and particles, taking into consideration localized fluctuations, transportation phenomena, and diffusion. A common drawback of spatial models lies in their complexity: models can become very large, and their simulation could be time consuming, especially if we want to capture the systems behavior in a reliable way using stochastic methods in conjunction with a high spatial resolution. In order to deliver the promise done by systems biology to be able to understand a system as whole, we need to scale up the size of models we are able to simulate, moving from sequential to parallel simulation algorithms. In this paper, we analyze Smoldyn, a widely diffused algorithm for stochastic simulation of chemical reactions with spatial resolution and single molecule detail, and we propose an alternative, innovative implementation that exploits the parallelism of Graphics Processing Units (GPUs). The implementation executes the most computational demanding steps (computation of diffusion, unimolecular, and bimolecular reaction, as well as the most common cases of molecule-surface interaction) on the GPU, computing them in parallel on each molecule of the system. The implementation offers good speed-ups and real time, high quality graphics output

Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices: Design Considerations

PubMed Central

Gokmen, Tayfun; Vlasov, Yurii

2016-01-01

In recent years, deep neural networks (DNN) have demonstrated significant business impact in large scale analysis and classification tasks such as speech recognition, visual object detection, pattern extraction, etc. Training of large DNNs, however, is universally considered as time consuming and computationally intensive task that demands datacenter-scale computational resources recruited for many days. Here we propose a concept of resistive processing unit (RPU) devices that can potentially accelerate DNN training by orders of magnitude while using much less power. The proposed RPU device can store and update the weight values locally thus minimizing data movement during training and allowing to fully exploit the locality and the parallelism of the training algorithm. We evaluate the effect of various RPU device features/non-idealities and system parameters on performance in order to derive the device and system level specifications for implementation of an accelerator chip for DNN training in a realistic CMOS-compatible technology. For large DNNs with about 1 billion weights this massively parallel RPU architecture can achieve acceleration factors of 30, 000 × compared to state-of-the-art microprocessors while providing power efficiency of 84, 000 GigaOps∕s∕W. Problems that currently require days of training on a datacenter-size cluster with thousands of machines can be addressed within hours on a single RPU accelerator. A system consisting of a cluster of RPU accelerators will be able to tackle Big Data problems with trillions of parameters that is impossible to address today like, for example, natural speech recognition and translation between all world languages, real-time analytics on large streams of business and scientific data, integration, and analysis of multimodal sensory data flows from a massive number of IoT (Internet of Things) sensors. PMID:27493624

Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices: Design Considerations.

PubMed

Gokmen, Tayfun; Vlasov, Yurii

2016-01-01

In recent years, deep neural networks (DNN) have demonstrated significant business impact in large scale analysis and classification tasks such as speech recognition, visual object detection, pattern extraction, etc. Training of large DNNs, however, is universally considered as time consuming and computationally intensive task that demands datacenter-scale computational resources recruited for many days. Here we propose a concept of resistive processing unit (RPU) devices that can potentially accelerate DNN training by orders of magnitude while using much less power. The proposed RPU device can store and update the weight values locally thus minimizing data movement during training and allowing to fully exploit the locality and the parallelism of the training algorithm. We evaluate the effect of various RPU device features/non-idealities and system parameters on performance in order to derive the device and system level specifications for implementation of an accelerator chip for DNN training in a realistic CMOS-compatible technology. For large DNNs with about 1 billion weights this massively parallel RPU architecture can achieve acceleration factors of 30, 000 × compared to state-of-the-art microprocessors while providing power efficiency of 84, 000 GigaOps∕s∕W. Problems that currently require days of training on a datacenter-size cluster with thousands of machines can be addressed within hours on a single RPU accelerator. A system consisting of a cluster of RPU accelerators will be able to tackle Big Data problems with trillions of parameters that is impossible to address today like, for example, natural speech recognition and translation between all world languages, real-time analytics on large streams of business and scientific data, integration, and analysis of multimodal sensory data flows from a massive number of IoT (Internet of Things) sensors.

Parallel workflow manager for non-parallel bioinformatic applications to solve large-scale biological problems on a supercomputer.

PubMed

Suplatov, Dmitry; Popova, Nina; Zhumatiy, Sergey; Voevodin, Vladimir; Švedas, Vytas

2016-04-01

Rapid expansion of online resources providing access to genomic, structural, and functional information associated with biological macromolecules opens an opportunity to gain a deeper understanding of the mechanisms of biological processes due to systematic analysis of large datasets. This, however, requires novel strategies to optimally utilize computer processing power. Some methods in bioinformatics and molecular modeling require extensive computational resources. Other algorithms have fast implementations which take at most several hours to analyze a common input on a modern desktop station, however, due to multiple invocations for a large number of subtasks the full task requires a significant computing power. Therefore, an efficient computational solution to large-scale biological problems requires both a wise parallel implementation of resource-hungry methods as well as a smart workflow to manage multiple invocations of relatively fast algorithms. In this work, a new computer software mpiWrapper has been developed to accommodate non-parallel implementations of scientific algorithms within the parallel supercomputing environment. The Message Passing Interface has been implemented to exchange information between nodes. Two specialized threads - one for task management and communication, and another for subtask execution - are invoked on each processing unit to avoid deadlock while using blocking calls to MPI. The mpiWrapper can be used to launch all conventional Linux applications without the need to modify their original source codes and supports resubmission of subtasks on node failure. We show that this approach can be used to process huge amounts of biological data efficiently by running non-parallel programs in parallel mode on a supercomputer. The C++ source code and documentation are available from http://biokinet.belozersky.msu.ru/mpiWrapper .

Design and Verification of Remote Sensing Image Data Center Storage Architecture Based on Hadoop

NASA Astrophysics Data System (ADS)

Tang, D.; Zhou, X.; Jing, Y.; Cong, W.; Li, C.

2018-04-01

The data center is a new concept of data processing and application proposed in recent years. It is a new method of processing technologies based on data, parallel computing, and compatibility with different hardware clusters. While optimizing the data storage management structure, it fully utilizes cluster resource computing nodes and improves the efficiency of data parallel application. This paper used mature Hadoop technology to build a large-scale distributed image management architecture for remote sensing imagery. Using MapReduce parallel processing technology, it called many computing nodes to process image storage blocks and pyramids in the background to improve the efficiency of image reading and application and sovled the need for concurrent multi-user high-speed access to remotely sensed data. It verified the rationality, reliability and superiority of the system design by testing the storage efficiency of different image data and multi-users and analyzing the distributed storage architecture to improve the application efficiency of remote sensing images through building an actual Hadoop service system.

Extreme-Scale De Novo Genome Assembly

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

Georganas, Evangelos; Hofmeyr, Steven; Egan, Rob

De novo whole genome assembly reconstructs genomic sequence from short, overlapping, and potentially erroneous DNA segments and is one of the most important computations in modern genomics. This work presents HipMER, a high-quality end-to-end de novo assembler designed for extreme scale analysis, via efficient parallelization of the Meraculous code. Genome assembly software has many components, each of which stresses different components of a computer system. This chapter explains the computational challenges involved in each step of the HipMer pipeline, the key distributed data structures, and communication costs in detail. We present performance results of assembling the human genome and themore » large hexaploid wheat genome on large supercomputers up to tens of thousands of cores.« less

A Parallel Sliding Region Algorithm to Make Agent-Based Modeling Possible for a Large-Scale Simulation: Modeling Hepatitis C Epidemics in Canada.

PubMed

Wong, William W L; Feng, Zeny Z; Thein, Hla-Hla

2016-11-01

Agent-based models (ABMs) are computer simulation models that define interactions among agents and simulate emergent behaviors that arise from the ensemble of local decisions. ABMs have been increasingly used to examine trends in infectious disease epidemiology. However, the main limitation of ABMs is the high computational cost for a large-scale simulation. To improve the computational efficiency for large-scale ABM simulations, we built a parallelizable sliding region algorithm (SRA) for ABM and compared it to a nonparallelizable ABM. We developed a complex agent network and performed two simulations to model hepatitis C epidemics based on the real demographic data from Saskatchewan, Canada. The first simulation used the SRA that processed on each postal code subregion subsequently. The second simulation processed the entire population simultaneously. It was concluded that the parallelizable SRA showed computational time saving with comparable results in a province-wide simulation. Using the same method, SRA can be generalized for performing a country-wide simulation. Thus, this parallel algorithm enables the possibility of using ABM for large-scale simulation with limited computational resources.

Transport of cosmic-ray protons in intermittent heliospheric turbulence: Model and simulations

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

Alouani-Bibi, Fathallah; Le Roux, Jakobus A., E-mail: fb0006@uah.edu

The transport of charged energetic particles in the presence of strong intermittent heliospheric turbulence is computationally analyzed based on known properties of the interplanetary magnetic field and solar wind plasma at 1 astronomical unit. The turbulence is assumed to be static, composite, and quasi-three-dimensional with a varying energy distribution between a one-dimensional Alfvénic (slab) and a structured two-dimensional component. The spatial fluctuations of the turbulent magnetic field are modeled either as homogeneous with a Gaussian probability distribution function (PDF), or as intermittent on large and small scales with a q-Gaussian PDF. Simulations showed that energetic particle diffusion coefficients both parallelmore » and perpendicular to the background magnetic field are significantly affected by intermittency in the turbulence. This effect is especially strong for parallel transport where for large-scale intermittency results show an extended phase of subdiffusive parallel transport during which cross-field transport diffusion dominates. The effects of intermittency are found to depend on particle rigidity and the fraction of slab energy in the turbulence, yielding a perpendicular to parallel mean free path ratio close to 1 for large-scale intermittency. Investigation of higher order transport moments (kurtosis) indicates that non-Gaussian statistical properties of the intermittent turbulent magnetic field are present in the parallel transport, especially for low rigidity particles at all times.« less

A Parallel Finite Set Statistical Simulator for Multi-Target Detection and Tracking

NASA Astrophysics Data System (ADS)

Hussein, I.; MacMillan, R.

2014-09-01

Finite Set Statistics (FISST) is a powerful Bayesian inference tool for the joint detection, classification and tracking of multi-target environments. FISST is capable of handling phenomena such as clutter, misdetections, and target birth and decay. Implicit within the approach are solutions to the data association and target label-tracking problems. Finally, FISST provides generalized information measures that can be used for sensor allocation across different types of tasks such as: searching for new targets, and classification and tracking of known targets. These FISST capabilities have been demonstrated on several small-scale illustrative examples. However, for implementation in a large-scale system as in the Space Situational Awareness problem, these capabilities require a lot of computational power. In this paper, we implement FISST in a parallel environment for the joint detection and tracking of multi-target systems. In this implementation, false alarms and misdetections will be modeled. Target birth and decay will not be modeled in the present paper. We will demonstrate the success of the method for as many targets as we possibly can in a desktop parallel environment. Performance measures will include: number of targets in the simulation, certainty of detected target tracks, computational time as a function of clutter returns and number of targets, among other factors.

Finding Tropical Cyclones on a Cloud Computing Cluster: Using Parallel Virtualization for Large-Scale Climate Simulation Analysis

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

Hasenkamp, Daren; Sim, Alexander; Wehner, Michael

Extensive computing power has been used to tackle issues such as climate changes, fusion energy, and other pressing scientific challenges. These computations produce a tremendous amount of data; however, many of the data analysis programs currently only run a single processor. In this work, we explore the possibility of using the emerging cloud computing platform to parallelize such sequential data analysis tasks. As a proof of concept, we wrap a program for analyzing trends of tropical cyclones in a set of virtual machines (VMs). This approach allows the user to keep their familiar data analysis environment in the VMs, whilemore » we provide the coordination and data transfer services to ensure the necessary input and output are directed to the desired locations. This work extensively exercises the networking capability of the cloud computing systems and has revealed a number of weaknesses in the current cloud system software. In our tests, we are able to scale the parallel data analysis job to a modest number of VMs and achieve a speedup that is comparable to running the same analysis task using MPI. However, compared to MPI based parallelization, the cloud-based approach has a number of advantages. The cloud-based approach is more flexible because the VMs can capture arbitrary software dependencies without requiring the user to rewrite their programs. The cloud-based approach is also more resilient to failure; as long as a single VM is running, it can make progress while as soon as one MPI node fails the whole analysis job fails. In short, this initial work demonstrates that a cloud computing system is a viable platform for distributed scientific data analyses traditionally conducted on dedicated supercomputing systems.« less

Efficient multitasking of Choleski matrix factorization on CRAY supercomputers

NASA Technical Reports Server (NTRS)

Overman, Andrea L.; Poole, Eugene L.

1991-01-01

A Choleski method is described and used to solve linear systems of equations that arise in large scale structural analysis. The method uses a novel variable-band storage scheme and is structured to exploit fast local memory caches while minimizing data access delays between main memory and vector registers. Several parallel implementations of this method are described for the CRAY-2 and CRAY Y-MP computers demonstrating the use of microtasking and autotasking directives. A portable parallel language, FORCE, is used for comparison with the microtasked and autotasked implementations. Results are presented comparing the matrix factorization times for three representative structural analysis problems from runs made in both dedicated and multi-user modes on both computers. CPU and wall clock timings are given for the parallel implementations and are compared to single processor timings of the same algorithm.

Space Technology 5 Multi-Point Observations of Temporal Variability of Field-Aligned Currents

NASA Technical Reports Server (NTRS)

Le, Guan; Wang, Yongli; Slavin, James A.; Strangeway, Robert J.

2008-01-01

Space Technology 5 (ST5) is a three micro-satellite constellation deployed into a 300 x 4500 km, dawn-dusk, sun-synchronous polar orbit from March 22 to June 21, 2006, for technology validations. In this paper, we present a study of the temporal variability of field-aligned currents using multi-point magnetic field measurements from ST5. The data demonstrate that meso-scale current structures are commonly embedded within large-scale field-aligned current sheets. The meso-scale current structures are very dynamic with highly variable current density and/or polarity in time scales of approximately 10 min. They exhibit large temporal variations during both quiet and disturbed times in such time scales. On the other hand, the data also shown that the time scales for the currents to be relatively stable are approximately 1 min for meso-scale currents and approximately 10 min for large scale current sheets. These temporal features are obviously associated with dynamic variations of their particle carriers (mainly electrons) as they respond to the variations of the parallel electric field in auroral acceleration region. The characteristic time scales for the temporal variability of meso-scale field-aligned currents are found to be consistent with those of auroral parallel electric field.

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.

Portability and Cross-Platform Performance of an MPI-Based Parallel Polygon Renderer

NASA Technical Reports Server (NTRS)

Crockett, Thomas W.

1999-01-01

Visualizing the results of computations performed on large-scale parallel computers is a challenging problem, due to the size of the datasets involved. One approach is to perform the visualization and graphics operations in place, exploiting the available parallelism to obtain the necessary rendering performance. Over the past several years, we have been developing algorithms and software to support visualization applications on NASA's parallel supercomputers. Our results have been incorporated into a parallel polygon rendering system called PGL. PGL was initially developed on tightly-coupled distributed-memory message-passing systems, including Intel's iPSC/860 and Paragon, and IBM's SP2. Over the past year, we have ported it to a variety of additional platforms, including the HP Exemplar, SGI Origin2OOO, Cray T3E, and clusters of Sun workstations. In implementing PGL, we have had two primary goals: cross-platform portability and high performance. Portability is important because (1) our manpower resources are limited, making it difficult to develop and maintain multiple versions of the code, and (2) NASA's complement of parallel computing platforms is diverse and subject to frequent change. Performance is important in delivering adequate rendering rates for complex scenes and ensuring that parallel computing resources are used effectively. Unfortunately, these two goals are often at odds. In this paper we report on our experiences with portability and performance of the PGL polygon renderer across a range of parallel computing platforms.

Performance Characterization of Global Address Space Applications: A Case Study with NWChem

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

Hammond, Jeffrey R.; Krishnamoorthy, Sriram; Shende, Sameer

The use of global address space languages and one-sided communication for complex applications is gaining attention in the parallel computing community. However, lack of good evaluative methods to observe multiple levels of performance makes it difficult to isolate the cause of performance deficiencies and to understand the fundamental limitations of system and application design for future improvement. NWChem is a popular computational chemistry package which depends on the Global Arrays/ ARMCI suite for partitioned global address space functionality to deliver high-end molecular modeling capabilities. A workload characterization methodology was developed to support NWChem performance engineering on large-scale parallel platforms. Themore » research involved both the integration of performance instrumentation and measurement in the NWChem software, as well as the analysis of one-sided communication performance in the context of NWChem workloads. Scaling studies were conducted for NWChem on Blue Gene/P and on two large-scale clusters using different generation Infiniband interconnects and x86 processors. The performance analysis and results show how subtle changes in the runtime parameters related to the communication subsystem could have significant impact on performance behavior. The tool has successfully identified several algorithmic bottlenecks which are already being tackled by computational chemists to improve NWChem performance.« less

Visual Analysis of Cloud Computing Performance Using Behavioral Lines.

PubMed

Muelder, Chris; Zhu, Biao; Chen, Wei; Zhang, Hongxin; Ma, Kwan-Liu

2016-02-29

Cloud computing is an essential technology to Big Data analytics and services. A cloud computing system is often comprised of a large number of parallel computing and storage devices. Monitoring the usage and performance of such a system is important for efficient operations, maintenance, and security. Tracing every application on a large cloud system is untenable due to scale and privacy issues. But profile data can be collected relatively efficiently by regularly sampling the state of the system, including properties such as CPU load, memory usage, network usage, and others, creating a set of multivariate time series for each system. Adequate tools for studying such large-scale, multidimensional data are lacking. In this paper, we present a visual based analysis approach to understanding and analyzing the performance and behavior of cloud computing systems. Our design is based on similarity measures and a layout method to portray the behavior of each compute node over time. When visualizing a large number of behavioral lines together, distinct patterns often appear suggesting particular types of performance bottleneck. The resulting system provides multiple linked views, which allow the user to interactively explore the data by examining the data or a selected subset at different levels of detail. Our case studies, which use datasets collected from two different cloud systems, show that this visual based approach is effective in identifying trends and anomalies of the systems.

Multiple Independent File Parallel I/O with HDF5

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

Miller, M. C.

2016-07-13

The HDF5 library has supported the I/O requirements of HPC codes at Lawrence Livermore National Labs (LLNL) since the late 90’s. In particular, HDF5 used in the Multiple Independent File (MIF) parallel I/O paradigm has supported LLNL code’s scalable I/O requirements and has recently been gainfully used at scales as large as O(10 6) parallel tasks.

Approaching the exa-scale: a real-world evaluation of rendering extremely large data sets

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

Patchett, John M; Ahrens, James P; Lo, Li - Ta

2010-10-15

Extremely large scale analysis is becoming increasingly important as supercomputers and their simulations move from petascale to exascale. The lack of dedicated hardware acceleration for rendering on today's supercomputing platforms motivates our detailed evaluation of the possibility of interactive rendering on the supercomputer. In order to facilitate our understanding of rendering on the supercomputing platform, we focus on scalability of rendering algorithms and architecture envisioned for exascale datasets. To understand tradeoffs for dealing with extremely large datasets, we compare three different rendering algorithms for large polygonal data: software based ray tracing, software based rasterization and hardware accelerated rasterization. We presentmore » a case study of strong and weak scaling of rendering extremely large data on both GPU and CPU based parallel supercomputers using Para View, a parallel visualization tool. Wc use three different data sets: two synthetic and one from a scientific application. At an extreme scale, algorithmic rendering choices make a difference and should be considered while approaching exascale computing, visualization, and analysis. We find software based ray-tracing offers a viable approach for scalable rendering of the projected future massive data sizes.« less

Active Storage with Analytics Capabilities and I/O Runtime System for Petascale Systems

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

Choudhary, Alok

Computational scientists must understand results from experimental, observational and computational simulation generated data to gain insights and perform knowledge discovery. As systems approach the petascale range, problems that were unimaginable a few years ago are within reach. With the increasing volume and complexity of data produced by ultra-scale simulations and high-throughput experiments, understanding the science is largely hampered by the lack of comprehensive I/O, storage, acceleration of data manipulation, analysis, and mining tools. Scientists require techniques, tools and infrastructure to facilitate better understanding of their data, in particular the ability to effectively perform complex data analysis, statistical analysis and knowledgemore » discovery. The goal of this work is to enable more effective analysis of scientific datasets through the integration of enhancements in the I/O stack, from active storage support at the file system layer to MPI-IO and high-level I/O library layers. We propose to provide software components to accelerate data analytics, mining, I/O, and knowledge discovery for large-scale scientific applications, thereby increasing productivity of both scientists and the systems. Our approaches include 1) design the interfaces in high-level I/O libraries, such as parallel netCDF, for applications to activate data mining operations at the lower I/O layers; 2) Enhance MPI-IO runtime systems to incorporate the functionality developed as a part of the runtime system design; 3) Develop parallel data mining programs as part of runtime library for server-side file system in PVFS file system; and 4) Prototype an active storage cluster, which will utilize multicore CPUs, GPUs, and FPGAs to carry out the data mining workload.« less

Scalable non-negative matrix tri-factorization.

PubMed

Čopar, Andrej; Žitnik, Marinka; Zupan, Blaž

2017-01-01

Matrix factorization is a well established pattern discovery tool that has seen numerous applications in biomedical data analytics, such as gene expression co-clustering, patient stratification, and gene-disease association mining. Matrix factorization learns a latent data model that takes a data matrix and transforms it into a latent feature space enabling generalization, noise removal and feature discovery. However, factorization algorithms are numerically intensive, and hence there is a pressing challenge to scale current algorithms to work with large datasets. Our focus in this paper is matrix tri-factorization, a popular method that is not limited by the assumption of standard matrix factorization about data residing in one latent space. Matrix tri-factorization solves this by inferring a separate latent space for each dimension in a data matrix, and a latent mapping of interactions between the inferred spaces, making the approach particularly suitable for biomedical data mining. We developed a block-wise approach for latent factor learning in matrix tri-factorization. The approach partitions a data matrix into disjoint submatrices that are treated independently and fed into a parallel factorization system. An appealing property of the proposed approach is its mathematical equivalence with serial matrix tri-factorization. In a study on large biomedical datasets we show that our approach scales well on multi-processor and multi-GPU architectures. On a four-GPU system we demonstrate that our approach can be more than 100-times faster than its single-processor counterpart. A general approach for scaling non-negative matrix tri-factorization is proposed. The approach is especially useful parallel matrix factorization implemented in a multi-GPU environment. We expect the new approach will be useful in emerging procedures for latent factor analysis, notably for data integration, where many large data matrices need to be collectively factorized.

Parallel Computing for Probabilistic Response Analysis of High Temperature Composites

NASA Technical Reports Server (NTRS)

Sues, R. H.; Lua, Y. J.; Smith, M. D.

1994-01-01

The objective of this Phase I research was to establish the required software and hardware strategies to achieve large scale parallelism in solving PCM problems. To meet this objective, several investigations were conducted. First, we identified the multiple levels of parallelism in PCM and the computational strategies to exploit these parallelisms. Next, several software and hardware efficiency investigations were conducted. These involved the use of three different parallel programming paradigms and solution of two example problems on both a shared-memory multiprocessor and a distributed-memory network of workstations.

Hierarchical Parallelism in Finite Difference Analysis of Heat Conduction

NASA Technical Reports Server (NTRS)

Padovan, Joseph; Krishna, Lala; Gute, Douglas

1997-01-01

Based on the concept of hierarchical parallelism, this research effort resulted in highly efficient parallel solution strategies for very large scale heat conduction problems. Overall, the method of hierarchical parallelism involves the partitioning of thermal models into several substructured levels wherein an optimal balance into various associated bandwidths is achieved. The details are described in this report. Overall, the report is organized into two parts. Part 1 describes the parallel modelling methodology and associated multilevel direct, iterative and mixed solution schemes. Part 2 establishes both the formal and computational properties of the scheme.

Open | SpeedShop: An Open Source Infrastructure for Parallel Performance Analysis

DOE PAGES

Schulz, Martin; Galarowicz, Jim; Maghrak, Don; ...

2008-01-01

Over the last decades a large number of performance tools has been developed to analyze and optimize high performance applications. Their acceptance by end users, however, has been slow: each tool alone is often limited in scope and comes with widely varying interfaces and workflow constraints, requiring different changes in the often complex build and execution infrastructure of the target application. We started the Open | SpeedShop project about 3 years ago to overcome these limitations and provide efficient, easy to apply, and integrated performance analysis for parallel systems. Open | SpeedShop has two different faces: it provides an interoperable tool set covering themore » most common analysis steps as well as a comprehensive plugin infrastructure for building new tools. In both cases, the tools can be deployed to large scale parallel applications using DPCL/Dyninst for distributed binary instrumentation. Further, all tools developed within or on top of Open | SpeedShop are accessible through multiple fully equivalent interfaces including an easy-to-use GUI as well as an interactive command line interface reducing the usage threshold for those tools.« less

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.

Space Technology 5 (ST-5) Observations of Field-Aligned Currents: Temporal Variability

NASA Technical Reports Server (NTRS)

Le, Guan

2010-01-01

Space Technology 5 (ST-5) is a three micro-satellite constellation deployed into a 300 x 4500 km, dawn-dusk, sun-synchronous polar orbit from March 22 to June 21, 2006, for technology validations. In this paper, we present a study of the temporal variability of field-aligned currents using multi-point magnetic field measurements from STS. The data demonstrate that masoscale current structures are commonly embedded within large-scale field-aligned current sheets. The meso-scale current structures are very dynamic with highly variable current density and/or polarity in time scales of about 10 min. They exhibit large temporal variations during both quiet and disturbed times in such time scales. On the other hand, the data also shown that the time scales for the currents to be relatively stable are about I min for meso-scale currents and about 10 min for large scale current sheets. These temporal features are obviously associated with dynamic variations of their particle carriers (mainly electrons) as they respond to the variations of the parallel electric field in auroral acceleration region. The characteristic time scales for the temporal variability of meso-scale field-aligned currents are found to be consistent with those of auroral parallel electric field.

Space Technology 5 (ST-5) Multipoint Observations of Temporal and Spatial Variability of Field-Aligned Currents

NASA Technical Reports Server (NTRS)

Le, Guan

2010-01-01

Space Technology 5 (ST-5) is a three micro-satellite constellation deployed into a 300 x 4500 km, dawn-dusk, sun-synchronous polar orbit from March 22 to June 21, 2006, for technology validations. In this paper, we present a study of the temporal variability of field-aligned currents using multi-point magnetic field measurements from ST5. The data demonstrate that mesoscale current structures are commonly embedded within large-scale field-aligned current sheets. The meso-scale current structures are very dynamic with highly variable current density and/or polarity in time scales of about 10 min. They exhibit large temporal variations during both quiet and disturbed times in such time scales. On the other hand, the data also shown that the time scales for the currents to be relatively stable are about 1 min for meso-scale currents and about 10 min for large scale current sheets. These temporal features are obviously associated with dynamic variations of their particle carriers (mainly electrons) as they respond to the variations of the parallel electric field in auroral acceleration region. The characteristic time scales for the temporal variability of meso-scale field-aligned currents are found to be consistent with those of auroral parallel electric field.

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.

Effects of imbalanced currents on large-format LiFePO4/graphite batteries systems connected in parallel

NASA Astrophysics Data System (ADS)

Shi, Wei; Hu, Xiaosong; Jin, Chao; Jiang, Jiuchun; Zhang, Yanru; Yip, Tony

2016-05-01

With the development and popularization of electric vehicles, it is urgent and necessary to develop effective management and diagnosis technology for battery systems. In this work, we design a parallel battery model, according to equivalent circuits of parallel voltage and branch current, to study effects of imbalanced currents on parallel large-format LiFePO4/graphite battery systems. Taking a 60 Ah LiFePO4/graphite battery system manufactured by ATL (Amperex Technology Limited, China) as an example, causes of imbalanced currents in the parallel connection are analyzed using our model, and the associated effect mechanisms on long-term stability of each single battery are examined. Theoretical and experimental results show that continuously increasing imbalanced currents during cycling are mainly responsible for the capacity fade of LiFePO4/graphite parallel batteries. It is thus a good way to avoid fast performance fade of parallel battery systems by suppressing variations of branch currents.

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

Pratapa, Phanisri P.; Suryanarayana, Phanish; Pask, John E.

We present the Clenshaw–Curtis Spectral Quadrature (SQ) method for real-space O(N) Density Functional Theory (DFT) calculations. In this approach, all quantities of interest are expressed as bilinear forms or sums over bilinear forms, which are then approximated by spatially localized Clenshaw–Curtis quadrature rules. This technique is identically applicable to both insulating and metallic systems, and in conjunction with local reformulation of the electrostatics, enables the O(N) evaluation of the electronic density, energy, and atomic forces. The SQ approach also permits infinite-cell calculations without recourse to Brillouin zone integration or large supercells. We employ a finite difference representation in order tomore » exploit the locality of electronic interactions in real space, enable systematic convergence, and facilitate large-scale parallel implementation. In particular, we derive expressions for the electronic density, total energy, and atomic forces that can be evaluated in O(N) operations. We demonstrate the systematic convergence of energies and forces with respect to quadrature order as well as truncation radius to the exact diagonalization result. In addition, we show convergence with respect to mesh size to established O(N 3) planewave results. In conclusion, we establish the efficiency of the proposed approach for high temperature calculations and discuss its particular suitability for large-scale parallel computation.« less

Spectral Quadrature method for accurate O ( N ) electronic structure calculations of metals and insulators

DOE PAGES

Pratapa, Phanisri P.; Suryanarayana, Phanish; Pask, John E.

2015-12-02

We present the Clenshaw–Curtis Spectral Quadrature (SQ) method for real-space O(N) Density Functional Theory (DFT) calculations. In this approach, all quantities of interest are expressed as bilinear forms or sums over bilinear forms, which are then approximated by spatially localized Clenshaw–Curtis quadrature rules. This technique is identically applicable to both insulating and metallic systems, and in conjunction with local reformulation of the electrostatics, enables the O(N) evaluation of the electronic density, energy, and atomic forces. The SQ approach also permits infinite-cell calculations without recourse to Brillouin zone integration or large supercells. We employ a finite difference representation in order tomore » exploit the locality of electronic interactions in real space, enable systematic convergence, and facilitate large-scale parallel implementation. In particular, we derive expressions for the electronic density, total energy, and atomic forces that can be evaluated in O(N) operations. We demonstrate the systematic convergence of energies and forces with respect to quadrature order as well as truncation radius to the exact diagonalization result. In addition, we show convergence with respect to mesh size to established O(N 3) planewave results. In conclusion, we establish the efficiency of the proposed approach for high temperature calculations and discuss its particular suitability for large-scale parallel computation.« less

Trace: a high-throughput tomographic reconstruction engine for large-scale datasets.

PubMed

Bicer, Tekin; Gürsoy, Doğa; Andrade, Vincent De; Kettimuthu, Rajkumar; Scullin, William; Carlo, Francesco De; Foster, Ian T

2017-01-01

Modern synchrotron light sources and detectors produce data at such scale and complexity that large-scale computation is required to unleash their full power. One of the widely used imaging techniques that generates data at tens of gigabytes per second is computed tomography (CT). Although CT experiments result in rapid data generation, the analysis and reconstruction of the collected data may require hours or even days of computation time with a medium-sized workstation, which hinders the scientific progress that relies on the results of analysis. We present Trace, a data-intensive computing engine that we have developed to enable high-performance implementation of iterative tomographic reconstruction algorithms for parallel computers. Trace provides fine-grained reconstruction of tomography datasets using both (thread-level) shared memory and (process-level) distributed memory parallelization. Trace utilizes a special data structure called replicated reconstruction object to maximize application performance. We also present the optimizations that we apply to the replicated reconstruction objects and evaluate them using tomography datasets collected at the Advanced Photon Source. Our experimental evaluations show that our optimizations and parallelization techniques can provide 158× speedup using 32 compute nodes (384 cores) over a single-core configuration and decrease the end-to-end processing time of a large sinogram (with 4501 × 1 × 22,400 dimensions) from 12.5 h to <5 min per iteration. The proposed tomographic reconstruction engine can efficiently process large-scale tomographic data using many compute nodes and minimize reconstruction times.

Adapting Wave-front Algorithms to Efficiently Utilize Systems with Deep Communication Hierarchies

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

Kerbyson, Darren J.; Lang, Michael; Pakin, Scott

2011-09-30

Large-scale systems increasingly exhibit a differential between intra-chip and inter-chip communication performance especially in hybrid systems using accelerators. Processorcores on the same socket are able to communicate at lower latencies, and with higher bandwidths, than cores on different sockets either within the same node or between nodes. A key challenge is to efficiently use this communication hierarchy and hence optimize performance. We consider here the class of applications that contains wavefront processing. In these applications data can only be processed after their upstream neighbors have been processed. Similar dependencies result between processors in which communication is required to pass boundarymore » data downstream and whose cost is typically impacted by the slowest communication channel in use. In this work we develop a novel hierarchical wave-front approach that reduces the use of slower communications in the hierarchy but at the cost of additional steps in the parallel computation and higher use of on-chip communications. This tradeoff is explored using a performance model. An implementation using the Reverse-acceleration programming model on the petascale Roadrunner system demonstrates a 27% performance improvement at full system-scale on a kernel application. The approach is generally applicable to large-scale multi-core and accelerated systems where a differential in system communication performance exists.« less

Magnetic intermittency of solar wind turbulence in the dissipation range

NASA Astrophysics Data System (ADS)

Pei, Zhongtian; He, Jiansen; Tu, Chuanyi; Marsch, Eckart; Wang, Linghua

2016-04-01

The feature, nature, and fate of intermittency in the dissipation range are an interesting topic in the solar wind turbulence. We calculate the distribution of flatness for the magnetic field fluctuations as a functionof angle and scale. The flatness distribution shows a "butterfly" pattern, with two wings located at angles parallel/anti-parallel to local mean magnetic field direction and main body located at angles perpendicular to local B0. This "butterfly" pattern illustrates that the flatness profile in (anti-) parallel direction approaches to the maximum value at larger scale and drops faster than that in perpendicular direction. The contours for probability distribution functions at different scales illustrate a "vase" pattern, more clear in parallel direction, which confirms the scale-variation of flatness and indicates the intermittency generation and dissipation. The angular distribution of structure function in the dissipation range shows an anisotropic pattern. The quasi-mono-fractal scaling of structure function in the dissipation range is also illustrated and investigated with the mathematical model for inhomogeneous cascading (extended p-model). Different from the inertial range, the extended p-model for the dissipation range results in approximate uniform fragmentation measure. However, more complete mathematicaland physical model involving both non-uniform cascading and dissipation is needed. The nature of intermittency may be strong structures or large amplitude fluctuations, which may be tested with magnetic helicity. In one case study, we find the heating effect in terms of entropy for large amplitude fluctuations seems to be more obvious than strong structures.

Sirocco Storage Server v. pre-alpha 0.1

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

Curry, Matthew L.; Danielson, Geoffrey; Ward, H. Lee

Sirocco is a parallel storage system under development, designed for write-intensive workloads on large-scale HPC platforms. It implements a keyvalue object store on top of a set of loosely federated storage servers that cooperate to ensure data integrity and performance. It includes support for a range of different types of storage transactions. This software release constitutes a conformant storage server, along with the client-side libraries to access the storage over a network.

Cholesky-decomposed density MP2 with density fitting: Accurate MP2 and double-hybrid DFT energies for large systems

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

Maurer, Simon A.; Clin, Lucien; Ochsenfeld, Christian, E-mail: christian.ochsenfeld@uni-muenchen.de

2014-06-14

Our recently developed QQR-type integral screening is introduced in our Cholesky-decomposed pseudo-densities Møller-Plesset perturbation theory of second order (CDD-MP2) method. We use the resolution-of-the-identity (RI) approximation in combination with efficient integral transformations employing sparse matrix multiplications. The RI-CDD-MP2 method shows an asymptotic cubic scaling behavior with system size and a small prefactor that results in an early crossover to conventional methods for both small and large basis sets. We also explore the use of local fitting approximations which allow to further reduce the scaling behavior for very large systems. The reliability of our method is demonstrated on test sets formore » interaction and reaction energies of medium sized systems and on a diverse selection from our own benchmark set for total energies of larger systems. Timings on DNA systems show that fast calculations for systems with more than 500 atoms are feasible using a single processor core. Parallelization extends the range of accessible system sizes on one computing node with multiple cores to more than 1000 atoms in a double-zeta basis and more than 500 atoms in a triple-zeta basis.« less

GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit

PubMed Central

Pronk, Sander; Páll, Szilárd; Schulz, Roland; Larsson, Per; Bjelkmar, Pär; Apostolov, Rossen; Shirts, Michael R.; Smith, Jeremy C.; Kasson, Peter M.; van der Spoel, David; Hess, Berk; Lindahl, Erik

2013-01-01

Motivation: Molecular simulation has historically been a low-throughput technique, but faster computers and increasing amounts of genomic and structural data are changing this by enabling large-scale automated simulation of, for instance, many conformers or mutants of biomolecules with or without a range of ligands. At the same time, advances in performance and scaling now make it possible to model complex biomolecular interaction and function in a manner directly testable by experiment. These applications share a need for fast and efficient software that can be deployed on massive scale in clusters, web servers, distributed computing or cloud resources. Results: Here, we present a range of new simulation algorithms and features developed during the past 4 years, leading up to the GROMACS 4.5 software package. The software now automatically handles wide classes of biomolecules, such as proteins, nucleic acids and lipids, and comes with all commonly used force fields for these molecules built-in. GROMACS supports several implicit solvent models, as well as new free-energy algorithms, and the software now uses multithreading for efficient parallelization even on low-end systems, including windows-based workstations. Together with hand-tuned assembly kernels and state-of-the-art parallelization, this provides extremely high performance and cost efficiency for high-throughput as well as massively parallel simulations. Availability: GROMACS is an open source and free software available from http://www.gromacs.org. Contact: erik.lindahl@scilifelab.se Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23407358

Global Load Balancing with Parallel Mesh Adaption on Distributed-Memory Systems

NASA Technical Reports Server (NTRS)

Biswas, Rupak; Oliker, Leonid; Sohn, Andrew

1996-01-01

Dynamic mesh adaptation on unstructured grids is a powerful tool for efficiently computing unsteady problems to resolve solution features of interest. Unfortunately, this causes load inbalances among processors on a parallel machine. This paper described the parallel implementation of a tetrahedral mesh adaption scheme and a new global load balancing method. A heuristic remapping algorithm is presented that assigns partitions to processors such that the redistribution coast is minimized. Results indicate that the parallel performance of the mesh adaption code depends on the nature of the adaption region and show a 35.5X speedup on 64 processors of an SP2 when 35 percent of the mesh is randomly adapted. For large scale scientific computations, our load balancing strategy gives an almost sixfold reduction in solver execution times over non-balanced loads. Furthermore, our heuristic remappier yields processor assignments that are less than 3 percent of the optimal solutions, but requires only 1 percent of the computational time.

Parallel implementation of geometrical shock dynamics for two dimensional converging shock waves

NASA Astrophysics Data System (ADS)

Qiu, Shi; Liu, Kuang; Eliasson, Veronica

2016-10-01

Geometrical shock dynamics (GSD) theory is an appealing method to predict the shock motion in the sense that it is more computationally efficient than solving the traditional Euler equations, especially for converging shock waves. However, to solve and optimize large scale configurations, the main bottleneck is the computational cost. Among the existing numerical GSD schemes, there is only one that has been implemented on parallel computers, with the purpose to analyze detonation waves. To extend the computational advantage of the GSD theory to more general applications such as converging shock waves, a numerical implementation using a spatial decomposition method has been coupled with a front tracking approach on parallel computers. In addition, an efficient tridiagonal system solver for massively parallel computers has been applied to resolve the most expensive function in this implementation, resulting in an efficiency of 0.93 while using 32 HPCC cores. Moreover, symmetric boundary conditions have been developed to further reduce the computational cost, achieving a speedup of 19.26 for a 12-sided polygonal converging shock.

Solving Partial Differential Equations in a data-driven multiprocessor environment

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

Gaudiot, J.L.; Lin, C.M.; Hosseiniyar, M.

1988-12-31

Partial differential equations can be found in a host of engineering and scientific problems. The emergence of new parallel architectures has spurred research in the definition of parallel PDE solvers. Concurrently, highly programmable systems such as data-how architectures have been proposed for the exploitation of large scale parallelism. The implementation of some Partial Differential Equation solvers (such as the Jacobi method) on a tagged token data-flow graph is demonstrated here. Asynchronous methods (chaotic relaxation) are studied and new scheduling approaches (the Token No-Labeling scheme) are introduced in order to support the implementation of the asychronous methods in a data-driven environment.more » New high-level data-flow language program constructs are introduced in order to handle chaotic operations. Finally, the performance of the program graphs is demonstrated by a deterministic simulation of a message passing data-flow multiprocessor. An analysis of the overhead in the data-flow graphs is undertaken to demonstrate the limits of parallel operations in dataflow PDE program graphs.« less

Visualization Co-Processing of a CFD Simulation

NASA Technical Reports Server (NTRS)

Vaziri, Arsi

1999-01-01

OVERFLOW, a widely used CFD simulation code, is combined with a visualization system, pV3, to experiment with an environment for simulation/visualization co-processing on a SGI Origin 2000 computer(O2K) system. The shared memory version of the solver is used with the O2K 'pfa' preprocessor invoked to automatically discover parallelism in the source code. No other explicit parallelism is enabled. In order to study the scaling and performance of the visualization co-processing system, sample runs are made with different processor groups in the range of 1 to 254 processors. The data exchange between the visualization system and the simulation system is rapid enough for user interactivity when the problem size is small. This shared memory version of OVERFLOW, with minimal parallelization, does not scale well to an increasing number of available processors. The visualization task takes about 18 to 30% of the total processing time and does not appear to be a major contributor to the poor scaling. Improper load balancing and inter-processor communication overhead are contributors to this poor performance. Work is in progress which is aimed at obtaining improved parallel performance of the solver and removing the limitations of serial data transfer to pV3 by examining various parallelization/communication strategies, including the use of the explicit message passing.

ADHydro: A Parallel Implementation of a Large-scale High-Resolution Multi-Physics Distributed Water Resources Model Using the Charm++ Run Time System

NASA Astrophysics Data System (ADS)

Steinke, R. C.; Ogden, F. L.; Lai, W.; Moreno, H. A.; Pureza, L. G.

2014-12-01

Physics-based watershed models are useful tools for hydrologic studies, water resources management and economic analyses in the contexts of climate, land-use, and water-use changes. This poster presents a parallel implementation of a quasi 3-dimensional, physics-based, high-resolution, distributed water resources model suitable for simulating large watersheds in a massively parallel computing environment. Developing this model is one of the objectives of the NSF EPSCoR RII Track II CI-WATER project, which is joint between Wyoming and Utah EPSCoR jurisdictions. The model, which we call ADHydro, is aimed at simulating important processes in the Rocky Mountain west, including: rainfall and infiltration, snowfall and snowmelt in complex terrain, vegetation and evapotranspiration, soil heat flux and freezing, overland flow, channel flow, groundwater flow, water management and irrigation. Model forcing is provided by the Weather Research and Forecasting (WRF) model, and ADHydro is coupled with the NOAH-MP land-surface scheme for calculating fluxes between the land and atmosphere. The ADHydro implementation uses the Charm++ parallel run time system. Charm++ is based on location transparent message passing between migrateable C++ objects. Each object represents an entity in the model such as a mesh element. These objects can be migrated between processors or serialized to disk allowing the Charm++ system to automatically provide capabilities such as load balancing and checkpointing. Objects interact with each other by passing messages that the Charm++ system routes to the correct destination object regardless of its current location. This poster discusses the algorithms, communication patterns, and caching strategies used to implement ADHydro with Charm++. The ADHydro model code will be released to the hydrologic community in late 2014.

Architectural Implications for Spatial Object Association Algorithms*

PubMed Central

Kumar, Vijay S.; Kurc, Tahsin; Saltz, Joel; Abdulla, Ghaleb; Kohn, Scott R.; Matarazzo, Celeste

2013-01-01

Spatial object association, also referred to as crossmatch of spatial datasets, is the problem of identifying and comparing objects in two or more datasets based on their positions in a common spatial coordinate system. In this work, we evaluate two crossmatch algorithms that are used for astronomical sky surveys, on the following database system architecture configurations: (1) Netezza Performance Server®, a parallel database system with active disk style processing capabilities, (2) MySQL Cluster, a high-throughput network database system, and (3) a hybrid configuration consisting of a collection of independent database system instances with data replication support. Our evaluation provides insights about how architectural characteristics of these systems affect the performance of the spatial crossmatch algorithms. We conducted our study using real use-case scenarios borrowed from a large-scale astronomy application known as the Large Synoptic Survey Telescope (LSST). PMID:25692244

Development of an automated large-scale protein-crystallization and monitoring system for high-throughput protein-structure analyses.

PubMed

Hiraki, Masahiko; Kato, Ryuichi; Nagai, Minoru; Satoh, Tadashi; Hirano, Satoshi; Ihara, Kentaro; Kudo, Norio; Nagae, Masamichi; Kobayashi, Masanori; Inoue, Michio; Uejima, Tamami; Oda, Shunichiro; Chavas, Leonard M G; Akutsu, Masato; Yamada, Yusuke; Kawasaki, Masato; Matsugaki, Naohiro; Igarashi, Noriyuki; Suzuki, Mamoru; Wakatsuki, Soichi

2006-09-01

Protein crystallization remains one of the bottlenecks in crystallographic analysis of macromolecules. An automated large-scale protein-crystallization system named PXS has been developed consisting of the following subsystems, which proceed in parallel under unified control software: dispensing precipitants and protein solutions, sealing crystallization plates, carrying robot, incubators, observation system and image-storage server. A sitting-drop crystallization plate specialized for PXS has also been designed and developed. PXS can set up 7680 drops for vapour diffusion per hour, which includes time for replenishing supplies such as disposable tips and crystallization plates. Images of the crystallization drops are automatically recorded according to a preprogrammed schedule and can be viewed by users remotely using web-based browser software. A number of protein crystals were successfully produced and several protein structures could be determined directly from crystals grown by PXS. In other cases, X-ray quality crystals were obtained by further optimization by manual screening based on the conditions found by PXS.

A scalable PC-based parallel computer for lattice QCD

NASA Astrophysics Data System (ADS)

Fodor, Z.; Katz, S. D.; Pappa, G.

2003-05-01

A PC-based parallel computer for medium/large scale lattice QCD simulations is suggested. The Eo¨tvo¨s Univ., Inst. Theor. Phys. cluster consists of 137 Intel P4-1.7GHz nodes. Gigabit Ethernet cards are used for nearest neighbor communication in a two-dimensional mesh. The sustained performance for dynamical staggered (wilson) quarks on large lattices is around 70(110) GFlops. The exceptional price/performance ratio is below $1/Mflop.

The International Conference on Vector and Parallel Computing (2nd)

DTIC Science & Technology

1989-01-17

Computation of the SVD of Bidiagonal Matrices" ...................................... 11 " Lattice QCD -As a Large Scale Scientific Computation...vectorizcd for the IBM 3090 Vector Facility. In addition, elapsed times " Lattice QCD -As a Large Scale Scientific have been reduced by using 3090...benchmarked Lattice QCD on a large number ofcompu- come from the wavefront solver routine. This was exten- ters: CrayX-MP and Cray 2 (vector

A parallel-vector algorithm for rapid structural analysis on high-performance computers

NASA Technical Reports Server (NTRS)

Storaasli, Olaf O.; Nguyen, Duc T.; Agarwal, Tarun K.

1990-01-01

A fast, accurate Choleski method for the solution of symmetric systems of linear equations is presented. This direct method is based on a variable-band storage scheme and takes advantage of column heights to reduce the number of operations in the Choleski factorization. The method employs parallel computation in the outermost DO-loop and vector computation via the 'loop unrolling' technique in the innermost DO-loop. The method avoids computations with zeros outside the column heights, and as an option, zeros inside the band. The close relationship between Choleski and Gauss elimination methods is examined. The minor changes required to convert the Choleski code to a Gauss code to solve non-positive-definite symmetric systems of equations are identified. The results for two large-scale structural analyses performed on supercomputers, demonstrate the accuracy and speed of the method.

A parallel-vector algorithm for rapid structural analysis on high-performance computers

NASA Technical Reports Server (NTRS)

Storaasli, Olaf O.; Nguyen, Duc T.; Agarwal, Tarun K.

1990-01-01

A fast, accurate Choleski method for the solution of symmetric systems of linear equations is presented. This direct method is based on a variable-band storage scheme and takes advantage of column heights to reduce the number of operations in the Choleski factorization. The method employs parallel computation in the outermost DO-loop and vector computation via the loop unrolling technique in the innermost DO-loop. The method avoids computations with zeros outside the column heights, and as an option, zeros inside the band. The close relationship between Choleski and Gauss elimination methods is examined. The minor changes required to convert the Choleski code to a Gauss code to solve non-positive-definite symmetric systems of equations are identified. The results for two large scale structural analyses performed on supercomputers, demonstrate the accuracy and speed of the method.

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

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.

How Robust Is Your Project? From Local Failures to Global Catastrophes: A Complex Networks Approach to Project Systemic Risk.

PubMed

Ellinas, Christos; Allan, Neil; Durugbo, Christopher; Johansson, Anders

2015-01-01

Current societal requirements necessitate the effective delivery of complex projects that can do more while using less. Yet, recent large-scale project failures suggest that our ability to successfully deliver them is still at its infancy. Such failures can be seen to arise through various failure mechanisms; this work focuses on one such mechanism. Specifically, it examines the likelihood of a project sustaining a large-scale catastrophe, as triggered by single task failure and delivered via a cascading process. To do so, an analytical model was developed and tested on an empirical dataset by the means of numerical simulation. This paper makes three main contributions. First, it provides a methodology to identify the tasks most capable of impacting a project. In doing so, it is noted that a significant number of tasks induce no cascades, while a handful are capable of triggering surprisingly large ones. Secondly, it illustrates that crude task characteristics cannot aid in identifying them, highlighting the complexity of the underlying process and the utility of this approach. Thirdly, it draws parallels with systems encountered within the natural sciences by noting the emergence of self-organised criticality, commonly found within natural systems. These findings strengthen the need to account for structural intricacies of a project's underlying task precedence structure as they can provide the conditions upon which large-scale catastrophes materialise.

Scaling Up Coordinate Descent Algorithms for Large ℓ1 Regularization Problems

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

Scherrer, Chad; Halappanavar, Mahantesh; Tewari, Ambuj

2012-07-03

We present a generic framework for parallel coordinate descent (CD) algorithms that has as special cases the original sequential algorithms of Cyclic CD and Stochastic CD, as well as the recent parallel Shotgun algorithm of Bradley et al. We introduce two novel parallel algorithms that are also special cases---Thread-Greedy CD and Coloring-Based CD---and give performance measurements for an OpenMP implementation of these.

A new parallel-vector finite element analysis software on distributed-memory computers

NASA Technical Reports Server (NTRS)

Qin, Jiangning; Nguyen, Duc T.

1993-01-01

A new parallel-vector finite element analysis software package MPFEA (Massively Parallel-vector Finite Element Analysis) is developed for large-scale structural analysis on massively parallel computers with distributed-memory. MPFEA is designed for parallel generation and assembly of the global finite element stiffness matrices as well as parallel solution of the simultaneous linear equations, since these are often the major time-consuming parts of a finite element analysis. Block-skyline storage scheme along with vector-unrolling techniques are used to enhance the vector performance. Communications among processors are carried out concurrently with arithmetic operations to reduce the total execution time. Numerical results on the Intel iPSC/860 computers (such as the Intel Gamma with 128 processors and the Intel Touchstone Delta with 512 processors) are presented, including an aircraft structure and some very large truss structures, to demonstrate the efficiency and accuracy of MPFEA.

Computational Issues in Damping Identification for Large Scale Problems

NASA Technical Reports Server (NTRS)

Pilkey, Deborah L.; Roe, Kevin P.; Inman, Daniel J.

1997-01-01

Two damping identification methods are tested for efficiency in large-scale applications. One is an iterative routine, and the other a least squares method. Numerical simulations have been performed on multiple degree-of-freedom models to test the effectiveness of the algorithm and the usefulness of parallel computation for the problems. High Performance Fortran is used to parallelize the algorithm. Tests were performed using the IBM-SP2 at NASA Ames Research Center. The least squares method tested incurs high communication costs, which reduces the benefit of high performance computing. This method's memory requirement grows at a very rapid rate meaning that larger problems can quickly exceed available computer memory. The iterative method's memory requirement grows at a much slower pace and is able to handle problems with 500+ degrees of freedom on a single processor. This method benefits from parallelization, and significant speedup can he seen for problems of 100+ degrees-of-freedom.

Scalable parallel distance field construction for large-scale applications

DOE PAGES

Yu, Hongfeng; Xie, Jinrong; Ma, Kwan -Liu; ...

2015-10-01

Computing distance fields is fundamental to many scientific and engineering applications. Distance fields can be used to direct analysis and reduce data. In this paper, we present a highly scalable method for computing 3D distance fields on massively parallel distributed-memory machines. Anew distributed spatial data structure, named parallel distance tree, is introduced to manage the level sets of data and facilitate surface tracking overtime, resulting in significantly reduced computation and communication costs for calculating the distance to the surface of interest from any spatial locations. Our method supports several data types and distance metrics from real-world applications. We demonstrate itsmore » efficiency and scalability on state-of-the-art supercomputers using both large-scale volume datasets and surface models. We also demonstrate in-situ distance field computation on dynamic turbulent flame surfaces for a petascale combustion simulation. In conclusion, our work greatly extends the usability of distance fields for demanding applications.« less

Scalable Parallel Distance Field Construction for Large-Scale Applications.

PubMed

Yu, Hongfeng; Xie, Jinrong; Ma, Kwan-Liu; Kolla, Hemanth; Chen, Jacqueline H

2015-10-01

Computing distance fields is fundamental to many scientific and engineering applications. Distance fields can be used to direct analysis and reduce data. In this paper, we present a highly scalable method for computing 3D distance fields on massively parallel distributed-memory machines. A new distributed spatial data structure, named parallel distance tree, is introduced to manage the level sets of data and facilitate surface tracking over time, resulting in significantly reduced computation and communication costs for calculating the distance to the surface of interest from any spatial locations. Our method supports several data types and distance metrics from real-world applications. We demonstrate its efficiency and scalability on state-of-the-art supercomputers using both large-scale volume datasets and surface models. We also demonstrate in-situ distance field computation on dynamic turbulent flame surfaces for a petascale combustion simulation. Our work greatly extends the usability of distance fields for demanding applications.

A fully coupled method for massively parallel simulation of hydraulically driven fractures in 3-dimensions: FULLY COUPLED PARALLEL SIMULATION OF HYDRAULIC FRACTURES IN 3-D

DOE PAGES

Settgast, Randolph R.; Fu, Pengcheng; Walsh, Stuart D. C.; ...

2016-09-18

This study describes a fully coupled finite element/finite volume approach for simulating field-scale hydraulically driven fractures in three dimensions, using massively parallel computing platforms. The proposed method is capable of capturing realistic representations of local heterogeneities, layering and natural fracture networks in a reservoir. A detailed description of the numerical implementation is provided, along with numerical studies comparing the model with both analytical solutions and experimental results. The results demonstrate the effectiveness of the proposed method for modeling large-scale problems involving hydraulically driven fractures in three dimensions.

A fully coupled method for massively parallel simulation of hydraulically driven fractures in 3-dimensions: FULLY COUPLED PARALLEL SIMULATION OF HYDRAULIC FRACTURES IN 3-D

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

Settgast, Randolph R.; Fu, Pengcheng; Walsh, Stuart D. C.

This study describes a fully coupled finite element/finite volume approach for simulating field-scale hydraulically driven fractures in three dimensions, using massively parallel computing platforms. The proposed method is capable of capturing realistic representations of local heterogeneities, layering and natural fracture networks in a reservoir. A detailed description of the numerical implementation is provided, along with numerical studies comparing the model with both analytical solutions and experimental results. The results demonstrate the effectiveness of the proposed method for modeling large-scale problems involving hydraulically driven fractures in three dimensions.

Performance of the OVERFLOW-MLP and LAURA-MLP CFD Codes on the NASA Ames 512 CPU Origin System

NASA Technical Reports Server (NTRS)

Taft, James R.

2000-01-01

The shared memory Multi-Level Parallelism (MLP) technique, developed last year at NASA Ames has been very successful in dramatically improving the performance of important NASA CFD codes. This new and very simple parallel programming technique was first inserted into the OVERFLOW production CFD code in FY 1998. The OVERFLOW-MLP code's parallel performance scaled linearly to 256 CPUs on the NASA Ames 256 CPU Origin 2000 system (steger). Overall performance exceeded 20.1 GFLOP/s, or about 4.5x the performance of a dedicated 16 CPU C90 system. All of this was achieved without any major modification to the original vector based code. The OVERFLOW-MLP code is now in production on the inhouse Origin systems as well as being used offsite at commercial aerospace companies. Partially as a result of this work, NASA Ames has purchased a new 512 CPU Origin 2000 system to further test the limits of parallel performance for NASA codes of interest. This paper presents the performance obtained from the latest optimization efforts on this machine for the LAURA-MLP and OVERFLOW-MLP codes. The Langley Aerothermodynamics Upwind Relaxation Algorithm (LAURA) code is a key simulation tool in the development of the next generation shuttle, interplanetary reentry vehicles, and nearly all "X" plane development. This code sustains about 4-5 GFLOP/s on a dedicated 16 CPU C90. At this rate, expected workloads would require over 100 C90 CPU years of computing over the next few calendar years. It is not feasible to expect that this would be affordable or available to the user community. Dramatic performance gains on cheaper systems are needed. This code is expected to be perhaps the largest consumer of NASA Ames compute cycles per run in the coming year.The OVERFLOW CFD code is extensively used in the government and commercial aerospace communities to evaluate new aircraft designs. It is one of the largest consumers of NASA supercomputing cycles and large simulations of highly resolved full aircraft are routinely undertaken. Typical large problems might require 100s of Cray C90 CPU hours to complete. The dramatic performance gains with the 256 CPU steger system are exciting. Obtaining results in hours instead of months is revolutionizing the way in which aircraft manufacturers are looking at future aircraft simulation work. Figure 2 below is a current state of the art plot of OVERFLOW-MLP performance on the 512 CPU Lomax system. As can be seen, the chart indicates that OVERFLOW-MLP continues to scale linearly with CPU count up to 512 CPUs on a large 35 million point full aircraft RANS simulation. At this point performance is such that a fully converged simulation of 2500 time steps is completed in less than 2 hours of elapsed time. Further work over the next few weeks will improve the performance of this code even further.The LAURA code has been converted to the MLP format as well. This code is currently being optimized for the 512 CPU system. Performance statistics indicate that the goal of 100 GFLOP/s will be achieved by year's end. This amounts to 20x the 16 CPU C90 result and strongly demonstrates the viability of the new parallel systems rapidly solving very large simulations in a production environment.

Parallel/Vector Integration Methods for Dynamical Astronomy

NASA Astrophysics Data System (ADS)

Fukushima, Toshio

1999-01-01

This paper reviews three recent works on the numerical methods to integrate ordinary differential equations (ODE), which are specially designed for parallel, vector, and/or multi-processor-unit(PU) computers. The first is the Picard-Chebyshev method (Fukushima, 1997a). It obtains a global solution of ODE in the form of Chebyshev polynomial of large (> 1000) degree by applying the Picard iteration repeatedly. The iteration converges for smooth problems and/or perturbed dynamics. The method runs around 100-1000 times faster in the vector mode than in the scalar mode of a certain computer with vector processors (Fukushima, 1997b). The second is a parallelization of a symplectic integrator (Saha et al., 1997). It regards the implicit midpoint rules covering thousands of timesteps as large-scale nonlinear equations and solves them by the fixed-point iteration. The method is applicable to Hamiltonian systems and is expected to lead an acceleration factor of around 50 in parallel computers with more than 1000 PUs. The last is a parallelization of the extrapolation method (Ito and Fukushima, 1997). It performs trial integrations in parallel. Also the trial integrations are further accelerated by balancing computational load among PUs by the technique of folding. The method is all-purpose and achieves an acceleration factor of around 3.5 by using several PUs. Finally, we give a perspective on the parallelization of some implicit integrators which require multiple corrections in solving implicit formulas like the implicit Hermitian integrators (Makino and Aarseth, 1992), (Hut et al., 1995) or the implicit symmetric multistep methods (Fukushima, 1998), (Fukushima, 1999).

Coarse-grained component concurrency in Earth system modeling: parallelizing atmospheric radiative transfer in the GFDL AM3 model using the Flexible Modeling System coupling framework

NASA Astrophysics Data System (ADS)

Balaji, V.; Benson, Rusty; Wyman, Bruce; Held, Isaac

2016-10-01

Climate models represent a large variety of processes on a variety of timescales and space scales, a canonical example of multi-physics multi-scale modeling. Current hardware trends, such as Graphical Processing Units (GPUs) and Many Integrated Core (MIC) chips, are based on, at best, marginal increases in clock speed, coupled with vast increases in concurrency, particularly at the fine grain. Multi-physics codes face particular challenges in achieving fine-grained concurrency, as different physics and dynamics components have different computational profiles, and universal solutions are hard to come by. We propose here one approach for multi-physics codes. These codes are typically structured as components interacting via software frameworks. The component structure of a typical Earth system model consists of a hierarchical and recursive tree of components, each representing a different climate process or dynamical system. This recursive structure generally encompasses a modest level of concurrency at the highest level (e.g., atmosphere and ocean on different processor sets) with serial organization underneath. We propose to extend concurrency much further by running more and more lower- and higher-level components in parallel with each other. Each component can further be parallelized on the fine grain, potentially offering a major increase in the scalability of Earth system models. We present here first results from this approach, called coarse-grained component concurrency, or CCC. Within the Geophysical Fluid Dynamics Laboratory (GFDL) Flexible Modeling System (FMS), the atmospheric radiative transfer component has been configured to run in parallel with a composite component consisting of every other atmospheric component, including the atmospheric dynamics and all other atmospheric physics components. We will explore the algorithmic challenges involved in such an approach, and present results from such simulations. Plans to achieve even greater levels of coarse-grained concurrency by extending this approach within other components, such as the ocean, will be discussed.

A Review of High-Performance Computational Strategies for Modeling and Imaging of Electromagnetic Induction Data

NASA Astrophysics Data System (ADS)

Newman, Gregory A.

2014-01-01

Many geoscientific applications exploit electrostatic and electromagnetic fields to interrogate and map subsurface electrical resistivity—an important geophysical attribute for characterizing mineral, energy, and water resources. In complex three-dimensional geologies, where many of these resources remain to be found, resistivity mapping requires large-scale modeling and imaging capabilities, as well as the ability to treat significant data volumes, which can easily overwhelm single-core and modest multicore computing hardware. To treat such problems requires large-scale parallel computational resources, necessary for reducing the time to solution to a time frame acceptable to the exploration process. The recognition that significant parallel computing processes must be brought to bear on these problems gives rise to choices that must be made in parallel computing hardware and software. In this review, some of these choices are presented, along with the resulting trade-offs. We also discuss future trends in high-performance computing and the anticipated impact on electromagnetic (EM) geophysics. Topics discussed in this review article include a survey of parallel computing platforms, graphics processing units to multicore CPUs with a fast interconnect, along with effective parallel solvers and associated solver libraries effective for inductive EM modeling and imaging.

Parallel distributed, reciprocal Monte Carlo radiation in coupled, large eddy combustion simulations

NASA Astrophysics Data System (ADS)

Hunsaker, Isaac L.

Radiation is the dominant mode of heat transfer in high temperature combustion environments. Radiative heat transfer affects the gas and particle phases, including all the associated combustion chemistry. The radiative properties are in turn affected by the turbulent flow field. This bi-directional coupling of radiation turbulence interactions poses a major challenge in creating parallel-capable, high-fidelity combustion simulations. In this work, a new model was developed in which reciprocal monte carlo radiation was coupled with a turbulent, large-eddy simulation combustion model. A technique wherein domain patches are stitched together was implemented to allow for scalable parallelism. The combustion model runs in parallel on a decomposed domain. The radiation model runs in parallel on a recomposed domain. The recomposed domain is stored on each processor after information sharing of the decomposed domain is handled via the message passing interface. Verification and validation testing of the new radiation model were favorable. Strong scaling analyses were performed on the Ember cluster and the Titan cluster for the CPU-radiation model and GPU-radiation model, respectively. The model demonstrated strong scaling to over 1,700 and 16,000 processing cores on Ember and Titan, respectively.

A Web-based Distributed Voluntary Computing Platform for Large Scale Hydrological Computations

NASA Astrophysics Data System (ADS)

Demir, I.; Agliamzanov, R.

2014-12-01

Distributed volunteer computing can enable researchers and scientist to form large parallel computing environments to utilize the computing power of the millions of computers on the Internet, and use them towards running large scale environmental simulations and models to serve the common good of local communities and the world. Recent developments in web technologies and standards allow client-side scripting languages to run at speeds close to native application, and utilize the power of Graphics Processing Units (GPU). Using a client-side scripting language like JavaScript, we have developed an open distributed computing framework that makes it easy for researchers to write their own hydrologic models, and run them on volunteer computers. Users will easily enable their websites for visitors to volunteer sharing their computer resources to contribute running advanced hydrological models and simulations. Using a web-based system allows users to start volunteering their computational resources within seconds without installing any software. The framework distributes the model simulation to thousands of nodes in small spatial and computational sizes. A relational database system is utilized for managing data connections and queue management for the distributed computing nodes. In this paper, we present a web-based distributed volunteer computing platform to enable large scale hydrological simulations and model runs in an open and integrated environment.

Efficiency of parallel direct optimization

NASA Technical Reports Server (NTRS)

Janies, D. A.; Wheeler, W. C.

2001-01-01

Tremendous progress has been made at the level of sequential computation in phylogenetics. However, little attention has been paid to parallel computation. Parallel computing is particularly suited to phylogenetics because of the many ways large computational problems can be broken into parts that can be analyzed concurrently. In this paper, we investigate the scaling factors and efficiency of random addition and tree refinement strategies using the direct optimization software, POY, on a small (10 slave processors) and a large (256 slave processors) cluster of networked PCs running LINUX. These algorithms were tested on several data sets composed of DNA and morphology ranging from 40 to 500 taxa. Various algorithms in POY show fundamentally different properties within and between clusters. All algorithms are efficient on the small cluster for the 40-taxon data set. On the large cluster, multibuilding exhibits excellent parallel efficiency, whereas parallel building is inefficient. These results are independent of data set size. Branch swapping in parallel shows excellent speed-up for 16 slave processors on the large cluster. However, there is no appreciable speed-up for branch swapping with the further addition of slave processors (>16). This result is independent of data set size. Ratcheting in parallel is efficient with the addition of up to 32 processors in the large cluster. This result is independent of data set size. c2001 The Willi Hennig Society.

Scaling Optimization of the SIESTA MHD Code

NASA Astrophysics Data System (ADS)

Seal, Sudip; Hirshman, Steven; Perumalla, Kalyan

2013-10-01

SIESTA is a parallel three-dimensional plasma equilibrium code capable of resolving magnetic islands at high spatial resolutions for toroidal plasmas. Originally designed to exploit small-scale parallelism, SIESTA has now been scaled to execute efficiently over several thousands of processors P. This scaling improvement was accomplished with minimal intrusion to the execution flow of the original version. First, the efficiency of the iterative solutions was improved by integrating the parallel tridiagonal block solver code BCYCLIC. Krylov-space generation in GMRES was then accelerated using a customized parallel matrix-vector multiplication algorithm. Novel parallel Hessian generation algorithms were integrated and memory access latencies were dramatically reduced through loop nest optimizations and data layout rearrangement. These optimizations sped up equilibria calculations by factors of 30-50. It is possible to compute solutions with granularity N/P near unity on extremely fine radial meshes (N > 1024 points). Grid separation in SIESTA, which manifests itself primarily in the resonant components of the pressure far from rational surfaces, is strongly suppressed by finer meshes. Large problem sizes of up to 300 K simultaneous non-linear coupled equations have been solved on the NERSC supercomputers. Work supported by U.S. DOE under Contract DE-AC05-00OR22725 with UT-Battelle, LLC.

DL_MG: A Parallel Multigrid Poisson and Poisson-Boltzmann Solver for Electronic Structure Calculations in Vacuum and Solution.

PubMed

Womack, James C; Anton, Lucian; Dziedzic, Jacek; Hasnip, Phil J; Probert, Matt I J; Skylaris, Chris-Kriton

2018-03-13

The solution of the Poisson equation is a crucial step in electronic structure calculations, yielding the electrostatic potential-a key component of the quantum mechanical Hamiltonian. In recent decades, theoretical advances and increases in computer performance have made it possible to simulate the electronic structure of extended systems in complex environments. This requires the solution of more complicated variants of the Poisson equation, featuring nonhomogeneous dielectric permittivities, ionic concentrations with nonlinear dependencies, and diverse boundary conditions. The analytic solutions generally used to solve the Poisson equation in vacuum (or with homogeneous permittivity) are not applicable in these circumstances, and numerical methods must be used. In this work, we present DL_MG, a flexible, scalable, and accurate solver library, developed specifically to tackle the challenges of solving the Poisson equation in modern large-scale electronic structure calculations on parallel computers. Our solver is based on the multigrid approach and uses an iterative high-order defect correction method to improve the accuracy of solutions. Using two chemically relevant model systems, we tested the accuracy and computational performance of DL_MG when solving the generalized Poisson and Poisson-Boltzmann equations, demonstrating excellent agreement with analytic solutions and efficient scaling to ∼10 9 unknowns and 100s of CPU cores. We also applied DL_MG in actual large-scale electronic structure calculations, using the ONETEP linear-scaling electronic structure package to study a 2615 atom protein-ligand complex with routinely available computational resources. In these calculations, the overall execution time with DL_MG was not significantly greater than the time required for calculations using a conventional FFT-based solver.

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

Chiang, Nai-Yuan; Zavala, Victor M.

We present a filter line-search algorithm that does not require inertia information of the linear system. This feature enables the use of a wide range of linear algebra strategies and libraries, which is essential to tackle large-scale problems on modern computing architectures. The proposed approach performs curvature tests along the search step to detect negative curvature and to trigger convexification. We prove that the approach is globally convergent and we implement the approach within a parallel interior-point framework to solve large-scale and highly nonlinear problems. Our numerical tests demonstrate that the inertia-free approach is as efficient as inertia detection viamore » symmetric indefinite factorizations. We also demonstrate that the inertia-free approach can lead to reductions in solution time because it reduces the amount of convexification needed.« less

Parallel algorithm for multiscale atomistic/continuum simulations using LAMMPS

NASA Astrophysics Data System (ADS)

Pavia, F.; Curtin, W. A.

2015-07-01

Deformation and fracture processes in engineering materials often require simultaneous descriptions over a range of length and time scales, with each scale using a different computational technique. Here we present a high-performance parallel 3D computing framework for executing large multiscale studies that couple an atomic domain, modeled using molecular dynamics and a continuum domain, modeled using explicit finite elements. We use the robust Coupled Atomistic/Discrete-Dislocation (CADD) displacement-coupling method, but without the transfer of dislocations between atoms and continuum. The main purpose of the work is to provide a multiscale implementation within an existing large-scale parallel molecular dynamics code (LAMMPS) that enables use of all the tools associated with this popular open-source code, while extending CADD-type coupling to 3D. Validation of the implementation includes the demonstration of (i) stability in finite-temperature dynamics using Langevin dynamics, (ii) elimination of wave reflections due to large dynamic events occurring in the MD region and (iii) the absence of spurious forces acting on dislocations due to the MD/FE coupling, for dislocations further than 10 Å from the coupling boundary. A first non-trivial example application of dislocation glide and bowing around obstacles is shown, for dislocation lengths of ∼50 nm using fewer than 1 000 000 atoms but reproducing results of extremely large atomistic simulations at much lower computational cost.

Neural Parallel Engine: A toolbox for massively parallel neural signal processing.

PubMed

Tam, Wing-Kin; Yang, Zhi

2018-05-01

Large-scale neural recordings provide detailed information on neuronal activities and can help elicit the underlying neural mechanisms of the brain. However, the computational burden is also formidable when we try to process the huge data stream generated by such recordings. In this study, we report the development of Neural Parallel Engine (NPE), a toolbox for massively parallel neural signal processing on graphical processing units (GPUs). It offers a selection of the most commonly used routines in neural signal processing such as spike detection and spike sorting, including advanced algorithms such as exponential-component-power-component (EC-PC) spike detection and binary pursuit spike sorting. We also propose a new method for detecting peaks in parallel through a parallel compact operation. Our toolbox is able to offer a 5× to 110× speedup compared with its CPU counterparts depending on the algorithms. A user-friendly MATLAB interface is provided to allow easy integration of the toolbox into existing workflows. Previous efforts on GPU neural signal processing only focus on a few rudimentary algorithms, are not well-optimized and often do not provide a user-friendly programming interface to fit into existing workflows. There is a strong need for a comprehensive toolbox for massively parallel neural signal processing. A new toolbox for massively parallel neural signal processing has been created. It can offer significant speedup in processing signals from large-scale recordings up to thousands of channels. Copyright © 2018 Elsevier B.V. All rights reserved.

Micro-Macro Simulation of Viscoelastic Fluids in Three Dimensions

NASA Astrophysics Data System (ADS)

Rüttgers, Alexander; Griebel, Michael

2012-11-01

The development of the chemical industry resulted in various complex fluids that cannot be correctly described by classical fluid mechanics. For instance, this includes paint, engine oils with polymeric additives and toothpaste. We currently perform multiscale viscoelastic flow simulations for which we have coupled our three-dimensional Navier-Stokes solver NaSt3dGPF with the stochastic Brownian configuration field method on the micro-scale. In this method, we represent a viscoelastic fluid as a dumbbell system immersed in a three-dimensional Newtonian liquid which leads to a six-dimensional problem in space. The approach requires large computational resources and therefore depends on an efficient parallelisation strategy. Our flow solver is parallelised with a domain decomposition approach using MPI. It shows excellent scale-up results for up to 128 processors. In this talk, we present simulation results for viscoelastic fluids in square-square contractions due to their relevance for many engineering applications such as extrusion. Another aspect of the talk is the parallel implementation in NaSt3dGPF and the parallel scale-up and speed-up behaviour.

Scalable multi-objective control for large scale water resources systems under uncertainty

NASA Astrophysics Data System (ADS)

Giuliani, Matteo; Quinn, Julianne; Herman, Jonathan; Castelletti, Andrea; Reed, Patrick

2016-04-01

The use of mathematical models to support the optimal management of environmental systems is rapidly expanding over the last years due to advances in scientific knowledge of the natural processes, efficiency of the optimization techniques, and availability of computational resources. However, undergoing changes in climate and society introduce additional challenges for controlling these systems, ultimately motivating the emergence of complex models to explore key causal relationships and dependencies on uncontrolled sources of variability. In this work, we contribute a novel implementation of the evolutionary multi-objective direct policy search (EMODPS) method for controlling environmental systems under uncertainty. The proposed approach combines direct policy search (DPS) with hierarchical parallelization of multi-objective evolutionary algorithms (MOEAs) and offers a threefold advantage: the DPS simulation-based optimization can be combined with any simulation model and does not add any constraint on modeled information, allowing the use of exogenous information in conditioning the decisions. Moreover, the combination of DPS and MOEAs prompts the generation or Pareto approximate set of solutions for up to 10 objectives, thus overcoming the decision biases produced by cognitive myopia, where narrow or restrictive definitions of optimality strongly limit the discovery of decision relevant alternatives. Finally, the use of large-scale MOEAs parallelization improves the ability of the designed solutions in handling the uncertainty due to severe natural variability. The proposed approach is demonstrated on a challenging water resources management problem represented by the optimal control of a network of four multipurpose water reservoirs in the Red River basin (Vietnam). As part of the medium-long term energy and food security national strategy, four large reservoirs have been constructed on the Red River tributaries, which are mainly operated for hydropower production, flood control, and water supply. Numerical results under historical as well as synthetically generated hydrologic conditions show that our approach is able to discover key system tradeoffs in the operations of the system. The ability of the algorithm to find near-optimal solutions increases with the number of islands in the adopted hierarchical parallelization scheme. In addition, although significant performance degradation is observed when the solutions designed over history are re-evaluated over synthetically generated inflows, we successfully reduced these vulnerabilities by identifying alternative solutions that are more robust to hydrologic uncertainties, while also addressing the tradeoffs across the Red River multi-sector services.

Programming Probabilistic Structural Analysis for Parallel Processing Computer

NASA Technical Reports Server (NTRS)

Sues, Robert H.; Chen, Heh-Chyun; Twisdale, Lawrence A.; Chamis, Christos C.; Murthy, Pappu L. N.

1991-01-01

The ultimate goal of this research program is to make Probabilistic Structural Analysis (PSA) computationally efficient and hence practical for the design environment by achieving large scale parallelism. The paper identifies the multiple levels of parallelism in PSA, identifies methodologies for exploiting this parallelism, describes the development of a parallel stochastic finite element code, and presents results of two example applications. It is demonstrated that speeds within five percent of those theoretically possible can be achieved. A special-purpose numerical technique, the stochastic preconditioned conjugate gradient method, is also presented and demonstrated to be extremely efficient for certain classes of PSA problems.

Scaling Irregular Applications through Data Aggregation and Software Multithreading

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

Morari, Alessandro; Tumeo, Antonino; Chavarría-Miranda, Daniel

Bioinformatics, data analytics, semantic databases, knowledge discovery are emerging high performance application areas that exploit dynamic, linked data structures such as graphs, unbalanced trees or unstructured grids. These data structures usually are very large, requiring significantly more memory than available on single shared memory systems. Additionally, these data structures are difficult to partition on distributed memory systems. They also present poor spatial and temporal locality, thus generating unpredictable memory and network accesses. The Partitioned Global Address Space (PGAS) programming model seems suitable for these applications, because it allows using a shared memory abstraction across distributed-memory clusters. However, current PGAS languagesmore » and libraries are built to target regular remote data accesses and block transfers. Furthermore, they usually rely on the Single Program Multiple Data (SPMD) parallel control model, which is not well suited to the fine grained, dynamic and unbalanced parallelism of irregular applications. In this paper we present {\\bf GMT} (Global Memory and Threading library), a custom runtime library that enables efficient execution of irregular applications on commodity clusters. GMT integrates a PGAS data substrate with simple fork/join parallelism and provides automatic load balancing on a per node basis. It implements multi-level aggregation and lightweight multithreading to maximize memory and network bandwidth with fine-grained data accesses and tolerate long data access latencies. A key innovation in the GMT runtime is its thread specialization (workers, helpers and communication threads) that realize the overall functionality. We compare our approach with other PGAS models, such as UPC running using GASNet, and hand-optimized MPI code on a set of typical large-scale irregular applications, demonstrating speedups of an order of magnitude.« less

Divide and Conquer (DC) BLAST: fast and easy BLAST execution within HPC environments

DOE PAGES

Yim, Won Cheol; Cushman, John C.

2017-07-22

Bioinformatics is currently faced with very large-scale data sets that lead to computational jobs, especially sequence similarity searches, that can take absurdly long times to run. For example, the National Center for Biotechnology Information (NCBI) Basic Local Alignment Search Tool (BLAST and BLAST+) suite, which is by far the most widely used tool for rapid similarity searching among nucleic acid or amino acid sequences, is highly central processing unit (CPU) intensive. While the BLAST suite of programs perform searches very rapidly, they have the potential to be accelerated. In recent years, distributed computing environments have become more widely accessible andmore » used due to the increasing availability of high-performance computing (HPC) systems. Therefore, simple solutions for data parallelization are needed to expedite BLAST and other sequence analysis tools. However, existing software for parallel sequence similarity searches often requires extensive computational experience and skill on the part of the user. In order to accelerate BLAST and other sequence analysis tools, Divide and Conquer BLAST (DCBLAST) was developed to perform NCBI BLAST searches within a cluster, grid, or HPC environment by using a query sequence distribution approach. Scaling from one (1) to 256 CPU cores resulted in significant improvements in processing speed. Thus, DCBLAST dramatically accelerates the execution of BLAST searches using a simple, accessible, robust, and parallel approach. DCBLAST works across multiple nodes automatically and it overcomes the speed limitation of single-node BLAST programs. DCBLAST can be used on any HPC system, can take advantage of hundreds of nodes, and has no output limitations. Thus, this freely available tool simplifies distributed computation pipelines to facilitate the rapid discovery of sequence similarities between very large data sets.« less

Divide and Conquer (DC) BLAST: fast and easy BLAST execution within HPC environments

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

Yim, Won Cheol; Cushman, John C.

Bioinformatics is currently faced with very large-scale data sets that lead to computational jobs, especially sequence similarity searches, that can take absurdly long times to run. For example, the National Center for Biotechnology Information (NCBI) Basic Local Alignment Search Tool (BLAST and BLAST+) suite, which is by far the most widely used tool for rapid similarity searching among nucleic acid or amino acid sequences, is highly central processing unit (CPU) intensive. While the BLAST suite of programs perform searches very rapidly, they have the potential to be accelerated. In recent years, distributed computing environments have become more widely accessible andmore » used due to the increasing availability of high-performance computing (HPC) systems. Therefore, simple solutions for data parallelization are needed to expedite BLAST and other sequence analysis tools. However, existing software for parallel sequence similarity searches often requires extensive computational experience and skill on the part of the user. In order to accelerate BLAST and other sequence analysis tools, Divide and Conquer BLAST (DCBLAST) was developed to perform NCBI BLAST searches within a cluster, grid, or HPC environment by using a query sequence distribution approach. Scaling from one (1) to 256 CPU cores resulted in significant improvements in processing speed. Thus, DCBLAST dramatically accelerates the execution of BLAST searches using a simple, accessible, robust, and parallel approach. DCBLAST works across multiple nodes automatically and it overcomes the speed limitation of single-node BLAST programs. DCBLAST can be used on any HPC system, can take advantage of hundreds of nodes, and has no output limitations. Thus, this freely available tool simplifies distributed computation pipelines to facilitate the rapid discovery of sequence similarities between very large data sets.« less

Design and implementation of highly parallel pipelined VLSI systems

NASA Astrophysics Data System (ADS)

Delange, Alphonsus Anthonius Jozef

A methodology and its realization as a prototype CAD (Computer Aided Design) system for the design and analysis of complex multiprocessor systems is presented. The design is an iterative process in which the behavioral specifications of the system components are refined into structural descriptions consisting of interconnections and lower level components etc. A model for the representation and analysis of multiprocessor systems at several levels of abstraction and an implementation of a CAD system based on this model are described. A high level design language, an object oriented development kit for tool design, a design data management system, and design and analysis tools such as a high level simulator and graphics design interface which are integrated into the prototype system and graphics interface are described. Procedures for the synthesis of semiregular processor arrays, and to compute the switching of input/output signals, memory management and control of processor array, and sequencing and segmentation of input/output data streams due to partitioning and clustering of the processor array during the subsequent synthesis steps, are described. The architecture and control of a parallel system is designed and each component mapped to a module or module generator in a symbolic layout library, compacted for design rules of VLSI (Very Large Scale Integration) technology. An example of the design of a processor that is a useful building block for highly parallel pipelined systems in the signal/image processing domains is given.

Parallel stitching of 2D materials

DOE PAGES

Ling, Xi; Wu, Lijun; Lin, Yuxuan; ...

2016-01-27

Diverse parallel stitched 2D heterostructures, including metal–semiconductor, semiconductor–semiconductor, and insulator–semiconductor, are synthesized directly through selective “sowing” of aromatic molecules as the seeds in the chemical vapor deposition (CVD) method. Lastly, the methodology enables the large-scale fabrication of lateral heterostructures, which offers tremendous potential for its application in integrated circuits.

Disappearance of Anisotropic Intermittency in Large-amplitude MHD Turbulence and Its Comparison with Small-amplitude MHD Turbulence

NASA Astrophysics Data System (ADS)

Yang, Liping; Zhang, Lei; He, Jiansen; Tu, Chuanyi; Li, Shengtai; Wang, Xin; Wang, Linghua

2018-03-01

Multi-order structure functions in the solar wind are reported to display a monofractal scaling when sampled parallel to the local magnetic field and a multifractal scaling when measured perpendicularly. Whether and to what extent will the scaling anisotropy be weakened by the enhancement of turbulence amplitude relative to the background magnetic strength? In this study, based on two runs of the magnetohydrodynamic (MHD) turbulence simulation with different relative levels of turbulence amplitude, we investigate and compare the scaling of multi-order magnetic structure functions and magnetic probability distribution functions (PDFs) as well as their dependence on the direction of the local field. The numerical results show that for the case of large-amplitude MHD turbulence, the multi-order structure functions display a multifractal scaling at all angles to the local magnetic field, with PDFs deviating significantly from the Gaussian distribution and a flatness larger than 3 at all angles. In contrast, for the case of small-amplitude MHD turbulence, the multi-order structure functions and PDFs have different features in the quasi-parallel and quasi-perpendicular directions: a monofractal scaling and Gaussian-like distribution in the former, and a conversion of a monofractal scaling and Gaussian-like distribution into a multifractal scaling and non-Gaussian tail distribution in the latter. These results hint that when intermittencies are abundant and intense, the multifractal scaling in the structure functions can appear even if it is in the quasi-parallel direction; otherwise, the monofractal scaling in the structure functions remains even if it is in the quasi-perpendicular direction.

Self-Scheduling Parallel Methods for Multiple Serial Codes with Application to WOPWOP

NASA Technical Reports Server (NTRS)

Long, Lyle N.; Brentner, Kenneth S.

2000-01-01

This paper presents a scheme for efficiently running a large number of serial jobs on parallel computers. Two examples are given of computer programs that run relatively quickly, but often they must be run numerous times to obtain all the results needed. It is very common in science and engineering to have codes that are not massive computing challenges in themselves, but due to the number of instances that must be run, they do become large-scale computing problems. The two examples given here represent common problems in aerospace engineering: aerodynamic panel methods and aeroacoustic integral methods. The first example simply solves many systems of linear equations. This is representative of an aerodynamic panel code where someone would like to solve for numerous angles of attack. The complete code for this first example is included in the appendix so that it can be readily used by others as a template. The second example is an aeroacoustics code (WOPWOP) that solves the Ffowcs Williams Hawkings equation to predict the far-field sound due to rotating blades. In this example, one quite often needs to compute the sound at numerous observer locations, hence parallelization is utilized to automate the noise computation for a large number of observers.

StagBL : A Scalable, Portable, High-Performance Discretization and Solver Layer for Geodynamic Simulation

NASA Astrophysics Data System (ADS)

Sanan, P.; Tackley, P. J.; Gerya, T.; Kaus, B. J. P.; May, D.

2017-12-01

StagBL is an open-source parallel solver and discretization library for geodynamic simulation,encapsulating and optimizing operations essential to staggered-grid finite volume Stokes flow solvers.It provides a parallel staggered-grid abstraction with a high-level interface in C and Fortran.On top of this abstraction, tools are available to define boundary conditions and interact with particle systems.Tools and examples to efficiently solve Stokes systems defined on the grid are provided in small (direct solver), medium (simple preconditioners), and large (block factorization and multigrid) model regimes.By working directly with leading application codes (StagYY, I3ELVIS, and LaMEM) and providing an API and examples to integrate with others, StagBL aims to become a community tool supplying scalable, portable, reproducible performance toward novel science in regional- and planet-scale geodynamics and planetary science.By implementing kernels used by many research groups beneath a uniform abstraction layer, the library will enable optimization for modern hardware, thus reducing community barriers to large- or extreme-scale parallel simulation on modern architectures. In particular, the library will include CPU-, Manycore-, and GPU-optimized variants of matrix-free operators and multigrid components.The common layer provides a framework upon which to introduce innovative new tools.StagBL will leverage p4est to provide distributed adaptive meshes, and incorporate a multigrid convergence analysis tool.These options, in addition to a wealth of solver options provided by an interface to PETSc, will make the most modern solution techniques available from a common interface. StagBL in turn provides a PETSc interface, DMStag, to its central staggered grid abstraction.We present public version 0.5 of StagBL, including preliminary integration with application codes and demonstrations with its own demonstration application, StagBLDemo. Central to StagBL is the notion of an uninterrupted pipeline from toy/teaching codes to high-performance, extreme-scale solves. StagBLDemo replicates the functionality of an advanced MATLAB-style regional geodynamics code, thus providing users with a concrete procedure to exceed the performance and scalability limitations of smaller-scale tools.

A parallel solver for huge dense linear systems

NASA Astrophysics Data System (ADS)

Badia, J. M.; Movilla, J. L.; Climente, J. I.; Castillo, M.; Marqués, M.; Mayo, R.; Quintana-Ortí, E. S.; Planelles, J.

2011-11-01

HDSS (Huge Dense Linear System Solver) is a Fortran Application Programming Interface (API) to facilitate the parallel solution of very large dense systems to scientists and engineers. The API makes use of parallelism to yield an efficient solution of the systems on a wide range of parallel platforms, from clusters of processors to massively parallel multiprocessors. It exploits out-of-core strategies to leverage the secondary memory in order to solve huge linear systems O(100.000). The API is based on the parallel linear algebra library PLAPACK, and on its Out-Of-Core (OOC) extension POOCLAPACK. Both PLAPACK and POOCLAPACK use the Message Passing Interface (MPI) as the communication layer and BLAS to perform the local matrix operations. The API provides a friendly interface to the users, hiding almost all the technical aspects related to the parallel execution of the code and the use of the secondary memory to solve the systems. In particular, the API can automatically select the best way to store and solve the systems, depending of the dimension of the system, the number of processes and the main memory of the platform. Experimental results on several parallel platforms report high performance, reaching more than 1 TFLOP with 64 cores to solve a system with more than 200 000 equations and more than 10 000 right-hand side vectors. New version program summaryProgram title: Huge Dense System Solver (HDSS) Catalogue identifier: AEHU_v1_1 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEHU_v1_1.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.: 87 062 No. of bytes in distributed program, including test data, etc.: 1 069 110 Distribution format: tar.gz Programming language: Fortran90, C Computer: Parallel architectures: multiprocessors, computer clusters Operating system: Linux/Unix Has the code been vectorized or parallelized?: Yes, includes MPI primitives. RAM: Tested for up to 190 GB Classification: 6.5 External routines: MPI ( http://www.mpi-forum.org/), BLAS ( http://www.netlib.org/blas/), PLAPACK ( http://www.cs.utexas.edu/~plapack/), POOCLAPACK ( ftp://ftp.cs.utexas.edu/pub/rvdg/PLAPACK/pooclapack.ps) (code for PLAPACK and POOCLAPACK is included in the distribution). Catalogue identifier of previous version: AEHU_v1_0 Journal reference of previous version: Comput. Phys. Comm. 182 (2011) 533 Does the new version supersede the previous version?: Yes Nature of problem: Huge scale dense systems of linear equations, Ax=B, beyond standard LAPACK capabilities. Solution method: The linear systems are solved by means of parallelized routines based on the LU factorization, using efficient secondary storage algorithms when the available main memory is insufficient. Reasons for new version: In many applications we need to guarantee a high accuracy in the solution of very large linear systems and we can do it by using double-precision arithmetic. Summary of revisions: Version 1.1 Can be used to solve linear systems using double-precision arithmetic. New version of the initialization routine. The user can choose the kind of arithmetic and the values of several parameters of the environment. Running time: About 5 hours to solve a system with more than 200 000 equations and more than 10 000 right-hand side vectors using double-precision arithmetic on an eight-node commodity cluster with a total of 64 Intel cores.

The Center for Optimized Structural Studies (COSS) platform for automation in cloning, expression, and purification of single proteins and protein-protein complexes.

PubMed

Mlynek, Georg; Lehner, Anita; Neuhold, Jana; Leeb, Sarah; Kostan, Julius; Charnagalov, Alexej; Stolt-Bergner, Peggy; Djinović-Carugo, Kristina; Pinotsis, Nikos

2014-06-01

Expression in Escherichia coli represents the simplest and most cost effective means for the production of recombinant proteins. This is a routine task in structural biology and biochemistry where milligrams of the target protein are required in high purity and monodispersity. To achieve these criteria, the user often needs to screen several constructs in different expression and purification conditions in parallel. We describe a pipeline, implemented in the Center for Optimized Structural Studies, that enables the systematic screening of expression and purification conditions for recombinant proteins and relies on a series of logical decisions. We first use bioinformatics tools to design a series of protein fragments, which we clone in parallel, and subsequently screen in small scale for optimal expression and purification conditions. Based on a scoring system that assesses soluble expression, we then select the top ranking targets for large-scale purification. In the establishment of our pipeline, emphasis was put on streamlining the processes such that it can be easily but not necessarily automatized. In a typical run of about 2 weeks, we are able to prepare and perform small-scale expression screens for 20-100 different constructs followed by large-scale purification of at least 4-6 proteins. The major advantage of our approach is its flexibility, which allows for easy adoption, either partially or entirely, by any average hypothesis driven laboratory in a manual or robot-assisted manner.

Study of a hybrid multispectral processor

NASA Technical Reports Server (NTRS)

Marshall, R. E.; Kriegler, F. J.

1973-01-01

A hybrid processor is described offering enough handling capacity and speed to process efficiently the large quantities of multispectral data that can be gathered by scanner systems such as MSDS, SKYLAB, ERTS, and ERIM M-7. Combinations of general-purpose and special-purpose hybrid computers were examined to include both analog and digital types as well as all-digital configurations. The current trend toward lower costs for medium-scale digital circuitry suggests that the all-digital approach may offer the better solution within the time frame of the next few years. The study recommends and defines such a hybrid digital computing system in which both special-purpose and general-purpose digital computers would be employed. The tasks of recognizing surface objects would be performed in a parallel, pipeline digital system while the tasks of control and monitoring would be handled by a medium-scale minicomputer system. A program to design and construct a small, prototype, all-digital system has been started.

OpenMP Parallelization and Optimization of Graph-Based Machine Learning Algorithms

DOE PAGES

Meng, Zhaoyi; Koniges, Alice; He, Yun Helen; ...

2016-09-21

In this paper, we investigate the OpenMP parallelization and optimization of two novel data classification algorithms. The new algorithms are based on graph and PDE solution techniques and provide significant accuracy and performance advantages over traditional data classification algorithms in serial mode. The methods leverage the Nystrom extension to calculate eigenvalue/eigenvectors of the graph Laplacian and this is a self-contained module that can be used in conjunction with other graph-Laplacian based methods such as spectral clustering. We use performance tools to collect the hotspots and memory access of the serial codes and use OpenMP as the parallelization language to parallelizemore » the most time-consuming parts. Where possible, we also use library routines. We then optimize the OpenMP implementations and detail the performance on traditional supercomputer nodes (in our case a Cray XC30), and test the optimization steps on emerging testbed systems based on Intel’s Knights Corner and Landing processors. We show both performance improvement and strong scaling behavior. Finally, a large number of optimization techniques and analyses are necessary before the algorithm reaches almost ideal scaling.« less

WImpiBLAST: web interface for mpiBLAST to help biologists perform large-scale annotation using high performance computing.

PubMed

Sharma, Parichit; Mantri, Shrikant S

2014-01-01

The function of a newly sequenced gene can be discovered by determining its sequence homology with known proteins. BLAST is the most extensively used sequence analysis program for sequence similarity search in large databases of sequences. With the advent of next generation sequencing technologies it has now become possible to study genes and their expression at a genome-wide scale through RNA-seq and metagenome sequencing experiments. Functional annotation of all the genes is done by sequence similarity search against multiple protein databases. This annotation task is computationally very intensive and can take days to obtain complete results. The program mpiBLAST, an open-source parallelization of BLAST that achieves superlinear speedup, can be used to accelerate large-scale annotation by using supercomputers and high performance computing (HPC) clusters. Although many parallel bioinformatics applications using the Message Passing Interface (MPI) are available in the public domain, researchers are reluctant to use them due to lack of expertise in the Linux command line and relevant programming experience. With these limitations, it becomes difficult for biologists to use mpiBLAST for accelerating annotation. No web interface is available in the open-source domain for mpiBLAST. We have developed WImpiBLAST, a user-friendly open-source web interface for parallel BLAST searches. It is implemented in Struts 1.3 using a Java backbone and runs atop the open-source Apache Tomcat Server. WImpiBLAST supports script creation and job submission features and also provides a robust job management interface for system administrators. It combines script creation and modification features with job monitoring and management through the Torque resource manager on a Linux-based HPC cluster. Use case information highlights the acceleration of annotation analysis achieved by using WImpiBLAST. Here, we describe the WImpiBLAST web interface features and architecture, explain design decisions, describe workflows and provide a detailed analysis.

WImpiBLAST: Web Interface for mpiBLAST to Help Biologists Perform Large-Scale Annotation Using High Performance Computing

PubMed Central

Sharma, Parichit; Mantri, Shrikant S.

2014-01-01

The function of a newly sequenced gene can be discovered by determining its sequence homology with known proteins. BLAST is the most extensively used sequence analysis program for sequence similarity search in large databases of sequences. With the advent of next generation sequencing technologies it has now become possible to study genes and their expression at a genome-wide scale through RNA-seq and metagenome sequencing experiments. Functional annotation of all the genes is done by sequence similarity search against multiple protein databases. This annotation task is computationally very intensive and can take days to obtain complete results. The program mpiBLAST, an open-source parallelization of BLAST that achieves superlinear speedup, can be used to accelerate large-scale annotation by using supercomputers and high performance computing (HPC) clusters. Although many parallel bioinformatics applications using the Message Passing Interface (MPI) are available in the public domain, researchers are reluctant to use them due to lack of expertise in the Linux command line and relevant programming experience. With these limitations, it becomes difficult for biologists to use mpiBLAST for accelerating annotation. No web interface is available in the open-source domain for mpiBLAST. We have developed WImpiBLAST, a user-friendly open-source web interface for parallel BLAST searches. It is implemented in Struts 1.3 using a Java backbone and runs atop the open-source Apache Tomcat Server. WImpiBLAST supports script creation and job submission features and also provides a robust job management interface for system administrators. It combines script creation and modification features with job monitoring and management through the Torque resource manager on a Linux-based HPC cluster. Use case information highlights the acceleration of annotation analysis achieved by using WImpiBLAST. Here, we describe the WImpiBLAST web interface features and architecture, explain design decisions, describe workflows and provide a detailed analysis. PMID:24979410

A General-purpose Framework for Parallel Processing of Large-scale LiDAR Data

NASA Astrophysics Data System (ADS)

Li, Z.; Hodgson, M.; Li, W.

2016-12-01

Light detection and ranging (LiDAR) technologies have proven efficiency to quickly obtain very detailed Earth surface data for a large spatial extent. Such data is important for scientific discoveries such as Earth and ecological sciences and natural disasters and environmental applications. However, handling LiDAR data poses grand geoprocessing challenges due to data intensity and computational intensity. Previous studies received notable success on parallel processing of LiDAR data to these challenges. However, these studies either relied on high performance computers and specialized hardware (GPUs) or focused mostly on finding customized solutions for some specific algorithms. We developed a general-purpose scalable framework coupled with sophisticated data decomposition and parallelization strategy to efficiently handle big LiDAR data. Specifically, 1) a tile-based spatial index is proposed to manage big LiDAR data in the scalable and fault-tolerable Hadoop distributed file system, 2) two spatial decomposition techniques are developed to enable efficient parallelization of different types of LiDAR processing tasks, and 3) by coupling existing LiDAR processing tools with Hadoop, this framework is able to conduct a variety of LiDAR data processing tasks in parallel in a highly scalable distributed computing environment. The performance and scalability of the framework is evaluated with a series of experiments conducted on a real LiDAR dataset using a proof-of-concept prototype system. The results show that the proposed framework 1) is able to handle massive LiDAR data more efficiently than standalone tools; and 2) provides almost linear scalability in terms of either increased workload (data volume) or increased computing nodes with both spatial decomposition strategies. We believe that the proposed framework provides valuable references on developing a collaborative cyberinfrastructure for processing big earth science data in a highly scalable environment.

Parallel Multiscale Algorithms for Astrophysical Fluid Dynamics Simulations

NASA Technical Reports Server (NTRS)

Norman, Michael L.

1997-01-01

Our goal is to develop software libraries and applications for astrophysical fluid dynamics simulations in multidimensions that will enable us to resolve the large spatial and temporal variations that inevitably arise due to gravity, fronts and microphysical phenomena. The software must run efficiently on parallel computers and be general enough to allow the incorporation of a wide variety of physics. Cosmological structure formation with realistic gas physics is the primary application driver in this work. Accurate simulations of e.g. galaxy formation require a spatial dynamic range (i.e., ratio of system scale to smallest resolved feature) of 104 or more in three dimensions in arbitrary topologies. We take this as our technical requirement. We have achieved, and in fact, surpassed these goals.

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

Rajbhandari, Samyam; NIkam, Akshay; Lai, Pai-Wei

Tensor contractions represent the most compute-intensive core kernels in ab initio computational quantum chemistry and nuclear physics. Symmetries in these tensor contractions makes them difficult to load balance and scale to large distributed systems. In this paper, we develop an efficient and scalable algorithm to contract symmetric tensors. We introduce a novel approach that avoids data redistribution in contracting symmetric tensors while also avoiding redundant storage and maintaining load balance. We present experimental results on two parallel supercomputers for several symmetric contractions that appear in the CCSD quantum chemistry method. We also present a novel approach to tensor redistribution thatmore » can take advantage of parallel hyperplanes when the initial distribution has replicated dimensions, and use collective broadcast when the final distribution has replicated dimensions, making the algorithm very efficient.« less

Parallel group independent component analysis for massive fMRI data sets.

PubMed

Chen, Shaojie; Huang, Lei; Qiu, Huitong; Nebel, Mary Beth; Mostofsky, Stewart H; Pekar, James J; Lindquist, Martin A; Eloyan, Ani; Caffo, Brian S

2017-01-01

Independent component analysis (ICA) is widely used in the field of functional neuroimaging to decompose data into spatio-temporal patterns of co-activation. In particular, ICA has found wide usage in the analysis of resting state fMRI (rs-fMRI) data. Recently, a number of large-scale data sets have become publicly available that consist of rs-fMRI scans from thousands of subjects. As a result, efficient ICA algorithms that scale well to the increased number of subjects are required. To address this problem, we propose a two-stage likelihood-based algorithm for performing group ICA, which we denote Parallel Group Independent Component Analysis (PGICA). By utilizing the sequential nature of the algorithm and parallel computing techniques, we are able to efficiently analyze data sets from large numbers of subjects. We illustrate the efficacy of PGICA, which has been implemented in R and is freely available through the Comprehensive R Archive Network, through simulation studies and application to rs-fMRI data from two large multi-subject data sets, consisting of 301 and 779 subjects respectively.

Iterative algorithms for large sparse linear systems on parallel computers

NASA Technical Reports Server (NTRS)

Adams, L. M.

1982-01-01

Algorithms for assembling in parallel the sparse system of linear equations that result from finite difference or finite element discretizations of elliptic partial differential equations, such as those that arise in structural engineering are developed. Parallel linear stationary iterative algorithms and parallel preconditioned conjugate gradient algorithms are developed for solving these systems. In addition, a model for comparing parallel algorithms on array architectures is developed and results of this model for the algorithms are given.

Trend Switching Processes in Financial Markets

NASA Astrophysics Data System (ADS)

Preis, Tobias; Stanley, H. Eugene

For an intriguing variety of switching processes in nature, the underlying complex system abruptly changes at a specific point from one state to another in a highly discontinuous fashion. Financial market fluctuations are characterized by many abrupt switchings creating increasing trends ("bubble formation") and decreasing trends ("bubble collapse"), on time scales ranging from macroscopic bubbles persisting for hundreds of days to microscopic bubbles persisting only for very short time scales. Our analysis is based on a German DAX Future data base containing 13,991,275 transactions recorded with a time resolution of 10- 2 s. For a parallel analysis, we use a data base of all S&P500 stocks providing 2,592,531 daily closing prices. We ask whether these ubiquitous switching processes have quantifiable features independent of the time horizon studied. We find striking scale-free behavior of the volatility after each switching occurs. We interpret our findings as being consistent with time-dependent collective behavior of financial market participants. We test the possible universality of our result by performing a parallel analysis of fluctuations in transaction volume and time intervals between trades. We show that these financial market switching processes have features similar to those present in phase transitions. We find that the well-known catastrophic bubbles that occur on large time scales - such as the most recent financial crisis - are no outliers but in fact single dramatic representatives caused by the formation of upward and downward trends on time scales varying over nine orders of magnitude from the very large down to the very small.

Performance of a parallel algebraic multilevel preconditioner for stabilized finite element semiconductor device modeling

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

Lin, Paul T.; Shadid, John N.; Sala, Marzio

In this study results are presented for the large-scale parallel performance of an algebraic multilevel preconditioner for solution of the drift-diffusion model for semiconductor devices. The preconditioner is the key numerical procedure determining the robustness, efficiency and scalability of the fully-coupled Newton-Krylov based, nonlinear solution method that is employed for this system of equations. The coupled system is comprised of a source term dominated Poisson equation for the electric potential, and two convection-diffusion-reaction type equations for the electron and hole concentration. The governing PDEs are discretized in space by a stabilized finite element method. Solution of the discrete system ismore » obtained through a fully-implicit time integrator, a fully-coupled Newton-based nonlinear solver, and a restarted GMRES Krylov linear system solver. The algebraic multilevel preconditioner is based on an aggressive coarsening graph partitioning of the nonzero block structure of the Jacobian matrix. Representative performance results are presented for various choices of multigrid V-cycles and W-cycles and parameter variations for smoothers based on incomplete factorizations. Parallel scalability results are presented for solution of up to 10{sup 8} unknowns on 4096 processors of a Cray XT3/4 and an IBM POWER eServer system.« less

Evaluation of parallel milliliter-scale stirred-tank bioreactors for the study of biphasic whole-cell biocatalysis with ionic liquids.

PubMed

Dennewald, Danielle; Hortsch, Ralf; Weuster-Botz, Dirk

2012-01-01

As clear structure-activity relationships are still rare for ionic liquids, preliminary experiments are necessary for the process development of biphasic whole-cell processes involving these solvents. To reduce the time investment and the material costs, the process development of such biphasic reaction systems would profit from a small-scale high-throughput platform. Exemplarily, the reduction of 2-octanone to (R)-2-octanol by a recombinant Escherichia coli in a biphasic ionic liquid/water system was studied in a miniaturized stirred-tank bioreactor system allowing the parallel operation of up to 48 reactors at the mL-scale. The results were compared to those obtained in a 20-fold larger stirred-tank reactor. The maximum local energy dissipation was evaluated at the larger scale and compared to the data available for the small-scale reactors, to verify if similar mass transfer could be obtained at both scales. Thereafter, the reaction kinetics and final conversions reached in different reactions setups were analysed. The results were in good agreement between both scales for varying ionic liquids and for ionic liquid volume fractions up to 40%. The parallel bioreactor system can thus be used for the process development of the majority of biphasic reaction systems involving ionic liquids, reducing the time and resource investment during the process development of this type of applications. Copyright © 2011. Published by Elsevier B.V.

Digital hydraulic drive for microfluidics and miniaturized cell culture devices based on shape memory alloy actuators

NASA Astrophysics Data System (ADS)

Tsai, Cheng-Han; Wu, Xuanye; Kuan, Da-Han; Zimmermann, Stefan; Zengerle, Roland; Koltay, Peter

2018-08-01

In order to culture and analyze individual living cells, microfluidic cultivation and manipulation of cells become an increasingly important topic. Such microfluidic systems allow for exploring the phenotypic differences between thousands of genetically identical cells or pharmacological tests in parallel, which is impossible to achieve by traditional macroscopic cell culture methods. Therefore, plenty of microfluidic systems and devices have been developed for cell biological studies like cell culture, cell sorting, and cell lysis in the past. However, these microfluidic systems are still limited by the external pressure sources which most of the time are large in size and have to be connected by fluidic tubing leading to complex and delicate systems. In order to provide a miniaturized, more robust actuation system a novel, compact and low power consumption digital hydraulic drive (DHD) has been developed that is intended for use in portable and automated microfluidic systems for various applications. The DHD considered in this work consists of a shape memory alloy (SMA) actuator and a pneumatic cylinder. The switching time of the digital modes (pressure ON versus OFF) can be adjusted from 1 s to min. Thus, the DHDs might have many applications for driving microfluidic devices. In this work, different implementations of DHDs are presented and their performance is characterized by experiments. In particular, it will be shown that DHDs can be used for microfluidic large-scale integration (mLSI) valve control (256 valves in parallel) as well as potentially for droplet-based microfluidic systems. As further application example, high-throughput mixing of cell cultures (96 wells in parallel) is demonstrated employing the DHD to drive a so-called ‘functional lid’ (FL), to enable a miniaturized micro bioreactor in a regular 96-well micro well plate.

Nongyrotropic Electrons in Guide Field Reconnection

NASA Technical Reports Server (NTRS)

Wendel, D. E.; Hesse, M.; Bessho, N.; Adrian, M. L.; Kuznetsova, M.

2016-01-01

We apply a scalar measure of nongyrotropy to the electron pressure tensor in a 2D particle-in-cell simulation of guide field reconnection and assess the corresponding electron distributions and the forces that account for the nongyrotropy. The scalar measure reveals that the nongyrotropy lies in bands that straddle the electron diffusion region and the separatrices, in the same regions where there are parallel electric fields. Analysis of electron distributions and fields shows that the nongyrotropy along the inflow and outflow separatrices emerges as a result of multiple populations of electrons influenced differently by large and small-scale parallel electric fields and by gradients in the electric field. The relevant parallel electric fields include large-scale potential ramps emanating from the x-line and sub-ion inertial scale bipolar electron holes. Gradients in the perpendicular electric field modify electrons differently depending on their phase, thus producing nongyrotropy. Magnetic flux violation occurs along portions of the separatrices that coincide with the parallel electric fields. An inductive electric field in the electron EB drift frame thus develops, which has the effect of enhancing nongyrotropies already produced by other mechanisms and under certain conditions producing their own nongyrotropy. Particle tracing of electrons from nongyrotropic populations along the inflows and outflows shows that the striated structure of nongyrotropy corresponds to electrons arriving from different source regions. We also show that the relevant parallel electric fields receive important contributions not only from the nongyrotropic portion of the electron pressure tensor but from electron spatial and temporal inertial terms as well.

Architectural Implications for Spatial Object Association Algorithms

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

Kumar, V S; Kurc, T; Saltz, J

2009-01-29

Spatial object association, also referred to as cross-match of spatial datasets, is the problem of identifying and comparing objects in two or more datasets based on their positions in a common spatial coordinate system. In this work, we evaluate two crossmatch algorithms that are used for astronomical sky surveys, on the following database system architecture configurations: (1) Netezza Performance Server R, a parallel database system with active disk style processing capabilities, (2) MySQL Cluster, a high-throughput network database system, and (3) a hybrid configuration consisting of a collection of independent database system instances with data replication support. Our evaluation providesmore » insights about how architectural characteristics of these systems affect the performance of the spatial crossmatch algorithms. We conducted our study using real use-case scenarios borrowed from a large-scale astronomy application known as the Large Synoptic Survey Telescope (LSST).« less

Integrating the Apache Big Data Stack with HPC for Big Data

NASA Astrophysics Data System (ADS)

Fox, G. C.; Qiu, J.; Jha, S.

2014-12-01

There is perhaps a broad consensus as to important issues in practical parallel computing as applied to large scale simulations; this is reflected in supercomputer architectures, algorithms, libraries, languages, compilers and best practice for application development. However, the same is not so true for data intensive computing, even though commercially clouds devote much more resources to data analytics than supercomputers devote to simulations. We look at a sample of over 50 big data applications to identify characteristics of data intensive applications and to deduce needed runtime and architectures. We suggest a big data version of the famous Berkeley dwarfs and NAS parallel benchmarks and use these to identify a few key classes of hardware/software architectures. Our analysis builds on combining HPC and ABDS the Apache big data software stack that is well used in modern cloud computing. Initial results on clouds and HPC systems are encouraging. We propose the development of SPIDAL - Scalable Parallel Interoperable Data Analytics Library -- built on system aand data abstractions suggested by the HPC-ABDS architecture. We discuss how it can be used in several application areas including Polar Science.

Hadoop-GIS: A High Performance Spatial Data Warehousing System over MapReduce.

PubMed

Aji, Ablimit; Wang, Fusheng; Vo, Hoang; Lee, Rubao; Liu, Qiaoling; Zhang, Xiaodong; Saltz, Joel

2013-08-01

Support of high performance queries on large volumes of spatial data becomes increasingly important in many application domains, including geospatial problems in numerous fields, location based services, and emerging scientific applications that are increasingly data- and compute-intensive. The emergence of massive scale spatial data is due to the proliferation of cost effective and ubiquitous positioning technologies, development of high resolution imaging technologies, and contribution from a large number of community users. There are two major challenges for managing and querying massive spatial data to support spatial queries: the explosion of spatial data, and the high computational complexity of spatial queries. In this paper, we present Hadoop-GIS - a scalable and high performance spatial data warehousing system for running large scale spatial queries on Hadoop. Hadoop-GIS supports multiple types of spatial queries on MapReduce through spatial partitioning, customizable spatial query engine RESQUE, implicit parallel spatial query execution on MapReduce, and effective methods for amending query results through handling boundary objects. Hadoop-GIS utilizes global partition indexing and customizable on demand local spatial indexing to achieve efficient query processing. Hadoop-GIS is integrated into Hive to support declarative spatial queries with an integrated architecture. Our experiments have demonstrated the high efficiency of Hadoop-GIS on query response and high scalability to run on commodity clusters. Our comparative experiments have showed that performance of Hadoop-GIS is on par with parallel SDBMS and outperforms SDBMS for compute-intensive queries. Hadoop-GIS is available as a set of library for processing spatial queries, and as an integrated software package in Hive.

Hadoop-GIS: A High Performance Spatial Data Warehousing System over MapReduce

PubMed Central

Aji, Ablimit; Wang, Fusheng; Vo, Hoang; Lee, Rubao; Liu, Qiaoling; Zhang, Xiaodong; Saltz, Joel

2013-01-01

Support of high performance queries on large volumes of spatial data becomes increasingly important in many application domains, including geospatial problems in numerous fields, location based services, and emerging scientific applications that are increasingly data- and compute-intensive. The emergence of massive scale spatial data is due to the proliferation of cost effective and ubiquitous positioning technologies, development of high resolution imaging technologies, and contribution from a large number of community users. There are two major challenges for managing and querying massive spatial data to support spatial queries: the explosion of spatial data, and the high computational complexity of spatial queries. In this paper, we present Hadoop-GIS – a scalable and high performance spatial data warehousing system for running large scale spatial queries on Hadoop. Hadoop-GIS supports multiple types of spatial queries on MapReduce through spatial partitioning, customizable spatial query engine RESQUE, implicit parallel spatial query execution on MapReduce, and effective methods for amending query results through handling boundary objects. Hadoop-GIS utilizes global partition indexing and customizable on demand local spatial indexing to achieve efficient query processing. Hadoop-GIS is integrated into Hive to support declarative spatial queries with an integrated architecture. Our experiments have demonstrated the high efficiency of Hadoop-GIS on query response and high scalability to run on commodity clusters. Our comparative experiments have showed that performance of Hadoop-GIS is on par with parallel SDBMS and outperforms SDBMS for compute-intensive queries. Hadoop-GIS is available as a set of library for processing spatial queries, and as an integrated software package in Hive. PMID:24187650

The genetic architecture of novel trophic specialists: larger effect sizes are associated with exceptional oral jaw diversification in a pupfish adaptive radiation.

PubMed

Martin, Christopher H; Erickson, Priscilla A; Miller, Craig T

2017-01-01

The genetic architecture of adaptation is fundamental to understanding the mechanisms and constraints governing diversification. However, most case studies focus on loss of complex traits or parallel speciation in similar environments. It is still unclear how the genetic architecture of these local adaptive processes compares to the architecture of evolutionary transitions contributing to morphological and ecological novelty. Here, we identify quantitative trait loci (QTL) between two trophic specialists in an excellent case study for examining the origins of ecological novelty: a sympatric radiation of pupfishes endemic to San Salvador Island, Bahamas, containing a large-jawed scale-eater and a short-jawed molluscivore with a skeletal nasal protrusion. These specialized niches and trophic traits are unique among over 2000 related species. Measurements of the fitness landscape on San Salvador demonstrate multiple fitness peaks and a larger fitness valley isolating the scale-eater from the putative ancestral intermediate phenotype of the generalist, suggesting that more large-effect QTL should contribute to its unique phenotype. We evaluated this prediction using an F2 intercross between these specialists. We present the first linkage map for pupfishes and detect significant QTL for sex and eight skeletal traits. Large-effect QTL contributed more to enlarged scale-eater jaws than the molluscivore nasal protrusion, consistent with predictions from the adaptive landscape. The microevolutionary genetic architecture of large-effect QTL for oral jaws parallels the exceptional diversification rates of oral jaws within the San Salvador radiation observed over macroevolutionary timescales and may have facilitated exceptional trophic novelty in this system. © 2016 John Wiley & Sons Ltd.

Integrated nanoscale tools for interrogating living cells

NASA Astrophysics Data System (ADS)

Jorgolli, Marsela

The development of next-generation, nanoscale technologies that interface biological systems will pave the way towards new understanding of such complex systems. Nanowires -- one-dimensional nanoscale structures -- have shown unique potential as an ideal physical interface to biological systems. Herein, we focus on the development of nanowire-based devices that can enable a wide variety of biological studies. First, we built upon standard nanofabrication techniques to optimize nanowire devices, resulting in perfectly ordered arrays of both opaque (Silicon) and transparent (Silicon dioxide) nanowires with user defined structural profile, densities, and overall patterns, as well as high sample consistency and large scale production. The high-precision and well-controlled fabrication method in conjunction with additional technologies laid the foundation for the generation of highly specialized platforms for imaging, electrochemical interrogation, and molecular biology. Next, we utilized nanowires as the fundamental structure in the development of integrated nanoelectronic platforms to directly interrogate the electrical activity of biological systems. Initially, we generated a scalable intracellular electrode platform based on vertical nanowires that allows for parallel electrical interfacing to multiple mammalian neurons. Our prototype device consisted of 16 individually addressable stimulation/recording sites, each containing an array of 9 electrically active silicon nanowires. We showed that these vertical nanowire electrode arrays could intracellularly record and stimulate neuronal activity in dissociated cultures of rat cortical neurons similar to patch clamp electrodes. In addition, we used our intracellular electrode platform to measure multiple individual synaptic connections, which enables the reconstruction of the functional connectivity maps of neuronal circuits. In order to expand and improve the capability of this functional prototype device we designed and fabricated a new hybrid chip that combines a front-side nanowire-based interface for neuronal recording with backside complementary metal oxide semiconductor (CMOS) circuits for on-chip multiplexing, voltage control for stimulation, signal amplification, and signal processing. Individual chips contain 1024 stimulation/recording sites enabling large-scale interfacing of neuronal networks with single cell resolution. Through electrical and electrochemical characterization of the devices, we demonstrated their enhanced functionality at a massively parallel scale. In our initial cell experiments, we achieved intracellular stimulations and recordings of changes in the membrane potential in a variety of cells including: HEK293T, cardiomyocytes, and rat cortical neurons. This demonstrated the device capability for single-cell-resolution recording/stimulation which when extended to a large number of neurons in a massively parallel fashion will enable the functional mapping of a complex neuronal network.

Automatic differentiation for design sensitivity analysis of structural systems using multiple processors

NASA Technical Reports Server (NTRS)

Nguyen, Duc T.; Storaasli, Olaf O.; Qin, Jiangning; Qamar, Ramzi

1994-01-01

An automatic differentiation tool (ADIFOR) is incorporated into a finite element based structural analysis program for shape and non-shape design sensitivity analysis of structural systems. The entire analysis and sensitivity procedures are parallelized and vectorized for high performance computation. Small scale examples to verify the accuracy of the proposed program and a medium scale example to demonstrate the parallel vector performance on multiple CRAY C90 processors are included.

OWL: A scalable Monte Carlo simulation suite for finite-temperature study of materials

NASA Astrophysics Data System (ADS)

Li, Ying Wai; Yuk, Simuck F.; Cooper, Valentino R.; Eisenbach, Markus; Odbadrakh, Khorgolkhuu

The OWL suite is a simulation package for performing large-scale Monte Carlo simulations. Its object-oriented, modular design enables it to interface with various external packages for energy evaluations. It is therefore applicable to study the finite-temperature properties for a wide range of systems: from simple classical spin models to materials where the energy is evaluated by ab initio methods. This scheme not only allows for the study of thermodynamic properties based on first-principles statistical mechanics, it also provides a means for massive, multi-level parallelism to fully exploit the capacity of modern heterogeneous computer architectures. We will demonstrate how improved strong and weak scaling is achieved by employing novel, parallel and scalable Monte Carlo algorithms, as well as the applications of OWL to a few selected frontier materials research problems. This research was supported by the Office of Science of the Department of Energy under contract DE-AC05-00OR22725.

A hybrid algorithm for parallel molecular dynamics simulations

NASA Astrophysics Data System (ADS)

Mangiardi, Chris M.; Meyer, R.

2017-10-01

This article describes algorithms for the hybrid parallelization and SIMD vectorization of molecular dynamics simulations with short-range forces. The parallelization method combines domain decomposition with a thread-based parallelization approach. The goal of the work is to enable efficient simulations of very large (tens of millions of atoms) and inhomogeneous systems on many-core processors with hundreds or thousands of cores and SIMD units with large vector sizes. In order to test the efficiency of the method, simulations of a variety of configurations with up to 74 million atoms have been performed. Results are shown that were obtained on multi-core systems with Sandy Bridge and Haswell processors as well as systems with Xeon Phi many-core processors.

The Potsdam Parallel Ice Sheet Model (PISM-PIK) - Part 1: Model description

NASA Astrophysics Data System (ADS)

Winkelmann, R.; Martin, M. A.; Haseloff, M.; Albrecht, T.; Bueler, E.; Khroulev, C.; Levermann, A.

2011-09-01

We present the Potsdam Parallel Ice Sheet Model (PISM-PIK), developed at the Potsdam Institute for Climate Impact Research to be used for simulations of large-scale ice sheet-shelf systems. It is derived from the Parallel Ice Sheet Model (Bueler and Brown, 2009). Velocities are calculated by superposition of two shallow stress balance approximations within the entire ice covered region: the shallow ice approximation (SIA) is dominant in grounded regions and accounts for shear deformation parallel to the geoid. The plug-flow type shallow shelf approximation (SSA) dominates the velocity field in ice shelf regions and serves as a basal sliding velocity in grounded regions. Ice streams can be identified diagnostically as regions with a significant contribution of membrane stresses to the local momentum balance. All lateral boundaries in PISM-PIK are free to evolve, including the grounding line and ice fronts. Ice shelf margins in particular are modeled using Neumann boundary conditions for the SSA equations, reflecting a hydrostatic stress imbalance along the vertical calving face. The ice front position is modeled using a subgrid-scale representation of calving front motion (Albrecht et al., 2011) and a physically-motivated calving law based on horizontal spreading rates. The model is tested in experiments from the Marine Ice Sheet Model Intercomparison Project (MISMIP). A dynamic equilibrium simulation of Antarctica under present-day conditions is presented in Martin et al. (2011).

Conjugate-Gradient Algorithms For Dynamics Of Manipulators

NASA Technical Reports Server (NTRS)

Fijany, Amir; Scheid, Robert E.

1993-01-01

Algorithms for serial and parallel computation of forward dynamics of multiple-link robotic manipulators by conjugate-gradient method developed. Parallel algorithms have potential for speedup of computations on multiple linked, specialized processors implemented in very-large-scale integrated circuits. Such processors used to stimulate dynamics, possibly faster than in real time, for purposes of planning and control.

ParBiBit: Parallel tool for binary biclustering on modern distributed-memory systems

PubMed Central

Expósito, Roberto R.

2018-01-01

Biclustering techniques are gaining attention in the analysis of large-scale datasets as they identify two-dimensional submatrices where both rows and columns are correlated. In this work we present ParBiBit, a parallel tool to accelerate the search of interesting biclusters on binary datasets, which are very popular on different fields such as genetics, marketing or text mining. It is based on the state-of-the-art sequential Java tool BiBit, which has been proved accurate by several studies, especially on scenarios that result on many large biclusters. ParBiBit uses the same methodology as BiBit (grouping the binary information into patterns) and provides the same results. Nevertheless, our tool significantly improves performance thanks to an efficient implementation based on C++11 that includes support for threads and MPI processes in order to exploit the compute capabilities of modern distributed-memory systems, which provide several multicore CPU nodes interconnected through a network. Our performance evaluation with 18 representative input datasets on two different eight-node systems shows that our tool is significantly faster than the original BiBit. Source code in C++ and MPI running on Linux systems as well as a reference manual are available at https://sourceforge.net/projects/parbibit/. PMID:29608567

ParBiBit: Parallel tool for binary biclustering on modern distributed-memory systems.

PubMed

González-Domínguez, Jorge; Expósito, Roberto R

2018-01-01

Biclustering techniques are gaining attention in the analysis of large-scale datasets as they identify two-dimensional submatrices where both rows and columns are correlated. In this work we present ParBiBit, a parallel tool to accelerate the search of interesting biclusters on binary datasets, which are very popular on different fields such as genetics, marketing or text mining. It is based on the state-of-the-art sequential Java tool BiBit, which has been proved accurate by several studies, especially on scenarios that result on many large biclusters. ParBiBit uses the same methodology as BiBit (grouping the binary information into patterns) and provides the same results. Nevertheless, our tool significantly improves performance thanks to an efficient implementation based on C++11 that includes support for threads and MPI processes in order to exploit the compute capabilities of modern distributed-memory systems, which provide several multicore CPU nodes interconnected through a network. Our performance evaluation with 18 representative input datasets on two different eight-node systems shows that our tool is significantly faster than the original BiBit. Source code in C++ and MPI running on Linux systems as well as a reference manual are available at https://sourceforge.net/projects/parbibit/.

Final Technical Report - Center for Technology for Advanced Scientific Component Software (TASCS)

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

Sussman, Alan

2014-10-21

This is a final technical report for the University of Maryland work in the SciDAC Center for Technology for Advanced Scientific Component Software (TASCS). The Maryland work focused on software tools for coupling parallel software components built using the Common Component Architecture (CCA) APIs. Those tools are based on the Maryland InterComm software framework that has been used in multiple computational science applications to build large-scale simulations of complex physical systems that employ multiple separately developed codes.

Parallel processing and expert systems

NASA Technical Reports Server (NTRS)

Yan, Jerry C.; Lau, Sonie

1991-01-01

Whether it be monitoring the thermal subsystem of Space Station Freedom, or controlling the navigation of the autonomous rover on Mars, NASA missions in the 90's cannot enjoy an increased level of autonomy without the efficient use of expert systems. Merely increasing the computational speed of uniprocessors may not be able to guarantee that real time demands are met for large expert systems. Speed-up via parallel processing must be pursued alongside the optimization of sequential implementations. Prototypes of parallel expert systems have been built at universities and industrial labs in the U.S. and Japan. The state-of-the-art research in progress related to parallel execution of expert systems was surveyed. The survey is divided into three major sections: (1) multiprocessors for parallel expert systems; (2) parallel languages for symbolic computations; and (3) measurements of parallelism of expert system. Results to date indicate that the parallelism achieved for these systems is small. In order to obtain greater speed-ups, data parallelism and application parallelism must be exploited.

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.

THE EFFECT OF INTERMITTENT GYRO-SCALE SLAB TURBULENCE ON PARALLEL AND PERPENDICULAR COSMIC-RAY TRANSPORT

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

Le Roux, J. A.

Earlier work based on nonlinear guiding center (NLGC) theory suggested that perpendicular cosmic-ray transport is diffusive when cosmic rays encounter random three-dimensional magnetohydrodynamic turbulence dominated by uniform two-dimensional (2D) turbulence with a minor uniform slab turbulence component. In this approach large-scale perpendicular cosmic-ray transport is due to cosmic rays microscopically diffusing along the meandering magnetic field dominated by 2D turbulence because of gyroresonant interactions with slab turbulence. However, turbulence in the solar wind is intermittent and it has been suggested that intermittent turbulence might be responsible for the observation of 'dropout' events in solar energetic particle fluxes on small scales.more » In a previous paper le Roux et al. suggested, using NLGC theory as a basis, that if gyro-scale slab turbulence is intermittent, large-scale perpendicular cosmic-ray transport in weak uniform 2D turbulence will be superdiffusive or subdiffusive depending on the statistical characteristics of the intermittent slab turbulence. In this paper we expand and refine our previous work further by investigating how both parallel and perpendicular transport are affected by intermittent slab turbulence for weak as well as strong uniform 2D turbulence. The main new finding is that both parallel and perpendicular transport are the net effect of an interplay between diffusive and nondiffusive (superdiffusive or subdiffusive) transport effects as a consequence of this intermittency.« less

Parallel Scaling Characteristics of Selected NERSC User ProjectCodes

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

Skinner, David; Verdier, Francesca; Anand, Harsh

This report documents parallel scaling characteristics of NERSC user project codes between Fiscal Year 2003 and the first half of Fiscal Year 2004 (Oct 2002-March 2004). The codes analyzed cover 60% of all the CPU hours delivered during that time frame on seaborg, a 6080 CPU IBM SP and the largest parallel computer at NERSC. The scale in terms of concurrency and problem size of the workload is analyzed. Drawing on batch queue logs, performance data and feedback from researchers we detail the motivations, benefits, and challenges of implementing highly parallel scientific codes on current NERSC High Performance Computing systems.more » An evaluation and outlook of the NERSC workload for Allocation Year 2005 is presented.« less

High-Speed Interrogation for Large-Scale Fiber Bragg Grating Sensing

PubMed Central

Hu, Chenyuan; Bai, Wei

2018-01-01

A high-speed interrogation scheme for large-scale fiber Bragg grating (FBG) sensing arrays is presented. This technique employs parallel computing and pipeline control to modulate incident light and demodulate the reflected sensing signal. One Electro-optic modulator (EOM) and one semiconductor optical amplifier (SOA) were used to generate a phase delay to filter reflected spectrum form multiple candidate FBGs with the same optical path difference (OPD). Experimental results showed that the fastest interrogation delay time for the proposed method was only about 27.2 us for a single FBG interrogation, and the system scanning period was only limited by the optical transmission delay in the sensing fiber owing to the multiple simultaneous central wavelength calculations. Furthermore, the proposed FPGA-based technique had a verified FBG wavelength demodulation stability of ±1 pm without average processing. PMID:29495263

High-Speed Interrogation for Large-Scale Fiber Bragg Grating Sensing.

PubMed

Hu, Chenyuan; Bai, Wei

2018-02-24

A high-speed interrogation scheme for large-scale fiber Bragg grating (FBG) sensing arrays is presented. This technique employs parallel computing and pipeline control to modulate incident light and demodulate the reflected sensing signal. One Electro-optic modulator (EOM) and one semiconductor optical amplifier (SOA) were used to generate a phase delay to filter reflected spectrum form multiple candidate FBGs with the same optical path difference (OPD). Experimental results showed that the fastest interrogation delay time for the proposed method was only about 27.2 us for a single FBG interrogation, and the system scanning period was only limited by the optical transmission delay in the sensing fiber owing to the multiple simultaneous central wavelength calculations. Furthermore, the proposed FPGA-based technique had a verified FBG wavelength demodulation stability of ±1 pm without average processing.

Parallel implementation of the particle simulation method with dynamic load balancing: Toward realistic geodynamical simulation

NASA Astrophysics Data System (ADS)

Furuichi, M.; Nishiura, D.

2015-12-01

Fully Lagrangian methods such as Smoothed Particle Hydrodynamics (SPH) and Discrete Element Method (DEM) have been widely used to solve the continuum and particles motions in the computational geodynamics field. These mesh-free methods are suitable for the problems with the complex geometry and boundary. In addition, their Lagrangian nature allows non-diffusive advection useful for tracking history dependent properties (e.g. rheology) of the material. These potential advantages over the mesh-based methods offer effective numerical applications to the geophysical flow and tectonic processes, which are for example, tsunami with free surface and floating body, magma intrusion with fracture of rock, and shear zone pattern generation of granular deformation. In order to investigate such geodynamical problems with the particle based methods, over millions to billion particles are required for the realistic simulation. Parallel computing is therefore important for handling such huge computational cost. An efficient parallel implementation of SPH and DEM methods is however known to be difficult especially for the distributed-memory architecture. Lagrangian methods inherently show workload imbalance problem for parallelization with the fixed domain in space, because particles move around and workloads change during the simulation. Therefore dynamic load balance is key technique to perform the large scale SPH and DEM simulation. In this work, we present the parallel implementation technique of SPH and DEM method utilizing dynamic load balancing algorithms toward the high resolution simulation over large domain using the massively parallel super computer system. Our method utilizes the imbalances of the executed time of each MPI process as the nonlinear term of parallel domain decomposition and minimizes them with the Newton like iteration method. In order to perform flexible domain decomposition in space, the slice-grid algorithm is used. Numerical tests show that our approach is suitable for solving the particles with different calculation costs (e.g. boundary particles) as well as the heterogeneous computer architecture. We analyze the parallel efficiency and scalability on the super computer systems (K-computer, Earth simulator 3, etc.).

Improved treatment of exact exchange in Quantum ESPRESSO

DOE PAGES

Barnes, Taylor A.; Kurth, Thorsten; Carrier, Pierre; ...

2017-01-18

Here, we present an algorithm and implementation for the parallel computation of exact exchange in Quantum ESPRESSO (QE) that exhibits greatly improved strong scaling. QE is an open-source software package for electronic structure calculations using plane wave density functional theory, and supports the use of local, semi-local, and hybrid DFT functionals. Wider application of hybrid functionals is desirable for the improved simulation of electronic band energy alignments and thermodynamic properties, but the computational complexity of evaluating the exact exchange potential limits the practical application of hybrid functionals to large systems and requires efficient implementations. We demonstrate that existing implementations ofmore » hybrid DFT that utilize a single data structure for both the local and exact exchange regions of the code are significantly limited in the degree of parallelization achievable. We present a band-pair parallelization approach, in which the calculation of exact exchange is parallelized and evaluated independently from the parallelization of the remainder of the calculation, with the wavefunction data being efficiently transformed on-the-fly into a form that is optimal for each part of the calculation. For a 64 water molecule supercell, our new algorithm reduces the overall time to solution by nearly an order of magnitude.« less

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

Barnes, Taylor A.; Kurth, Thorsten; Carrier, Pierre

Here, we present an algorithm and implementation for the parallel computation of exact exchange in Quantum ESPRESSO (QE) that exhibits greatly improved strong scaling. QE is an open-source software package for electronic structure calculations using plane wave density functional theory, and supports the use of local, semi-local, and hybrid DFT functionals. Wider application of hybrid functionals is desirable for the improved simulation of electronic band energy alignments and thermodynamic properties, but the computational complexity of evaluating the exact exchange potential limits the practical application of hybrid functionals to large systems and requires efficient implementations. We demonstrate that existing implementations ofmore » hybrid DFT that utilize a single data structure for both the local and exact exchange regions of the code are significantly limited in the degree of parallelization achievable. We present a band-pair parallelization approach, in which the calculation of exact exchange is parallelized and evaluated independently from the parallelization of the remainder of the calculation, with the wavefunction data being efficiently transformed on-the-fly into a form that is optimal for each part of the calculation. For a 64 water molecule supercell, our new algorithm reduces the overall time to solution by nearly an order of magnitude.« less

Recent developments in user-job management with Ganga

NASA Astrophysics Data System (ADS)

Currie, R.; Elmsheuser, J.; Fay, R.; Owen, P. H.; Richards, A.; Slater, M.; Sutcliffe, W.; Williams, M.

2015-12-01

The Ganga project was originally developed for use by LHC experiments and has been used extensively throughout Run1 in both LHCb and ATLAS. This document describes some the most recent developments within the Ganga project. There have been improvements in the handling of large scale computational tasks in the form of a new GangaTasks infrastructure. Improvements in file handling through using a new IGangaFile interface makes handling files largely transparent to the end user. In addition to this the performance and usability of Ganga have both been addressed through the development of a new queues system allows for parallel processing of job related tasks.

Scalable nuclear density functional theory with Sky3D

NASA Astrophysics Data System (ADS)

Afibuzzaman, Md; Schuetrumpf, Bastian; Aktulga, Hasan Metin

2018-02-01

In nuclear astrophysics, quantum simulations of large inhomogeneous dense systems as they appear in the crusts of neutron stars present big challenges. The number of particles in a simulation with periodic boundary conditions is strongly limited due to the immense computational cost of the quantum methods. In this paper, we describe techniques for an efficient and scalable parallel implementation of Sky3D, a nuclear density functional theory solver that operates on an equidistant grid. Presented techniques allow Sky3D to achieve good scaling and high performance on a large number of cores, as demonstrated through detailed performance analysis on a Cray XC40 supercomputer.

Software environment for implementing engineering applications on MIMD computers

NASA Technical Reports Server (NTRS)

Lopez, L. A.; Valimohamed, K. A.; Schiff, S.

1990-01-01

In this paper the concept for a software environment for developing engineering application systems for multiprocessor hardware (MIMD) is presented. The philosophy employed is to solve the largest problems possible in a reasonable amount of time, rather than solve existing problems faster. In the proposed environment most of the problems concerning parallel computation and handling of large distributed data spaces are hidden from the application program developer, thereby facilitating the development of large-scale software applications. Applications developed under the environment can be executed on a variety of MIMD hardware; it protects the application software from the effects of a rapidly changing MIMD hardware technology.

RAMA: A file system for massively parallel computers

NASA Technical Reports Server (NTRS)

Miller, Ethan L.; Katz, Randy H.

1993-01-01

This paper describes a file system design for massively parallel computers which makes very efficient use of a few disks per processor. This overcomes the traditional I/O bottleneck of massively parallel machines by storing the data on disks within the high-speed interconnection network. In addition, the file system, called RAMA, requires little inter-node synchronization, removing another common bottleneck in parallel processor file systems. Support for a large tertiary storage system can easily be integrated in lo the file system; in fact, RAMA runs most efficiently when tertiary storage is used.

On Parallelizing Single Dynamic Simulation Using HPC Techniques and APIs of Commercial Software

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

Diao, Ruisheng; Jin, Shuangshuang; Howell, Frederic

Time-domain simulations are heavily used in today’s planning and operation practices to assess power system transient stability and post-transient voltage/frequency profiles following severe contingencies to comply with industry standards. Because of the increased modeling complexity, it is several times slower than real time for state-of-the-art commercial packages to complete a dynamic simulation for a large-scale model. With the growing stochastic behavior introduced by emerging technologies, power industry has seen a growing need for performing security assessment in real time. This paper presents a parallel implementation framework to speed up a single dynamic simulation by leveraging the existing stability model librarymore » in commercial tools through their application programming interfaces (APIs). Several high performance computing (HPC) techniques are explored such as parallelizing the calculation of generator current injection, identifying fast linear solvers for network solution, and parallelizing data outputs when interacting with APIs in the commercial package, TSAT. The proposed method has been tested on a WECC planning base case with detailed synchronous generator models and exhibits outstanding scalable performance with sufficient accuracy.« less

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

Steed, Chad Allen

EDENx is a multivariate data visualization tool that allows interactive user driven analysis of large-scale data sets with high dimensionality. EDENx builds on our earlier system, called EDEN to enable analysis of more dimensions and larger scale data sets. EDENx provides an initial overview of summary statistics for each variable in the data set under investigation. EDENx allows the user to interact with graphical summary plots of the data to investigate subsets and their statistical associations. These plots include histograms, binned scatterplots, binned parallel coordinate plots, timeline plots, and graphical correlation indicators. From the EDENx interface, a user can selectmore » a subsample of interest and launch a more detailed data visualization via the EDEN system. EDENx is best suited for high-level, aggregate analysis tasks while EDEN is more appropriate for detail data investigations.« less

Eigensolver for a Sparse, Large Hermitian Matrix

NASA Technical Reports Server (NTRS)

Tisdale, E. Robert; Oyafuso, Fabiano; Klimeck, Gerhard; Brown, R. Chris

2003-01-01

A parallel-processing computer program finds a few eigenvalues in a sparse Hermitian matrix that contains as many as 100 million diagonal elements. This program finds the eigenvalues faster, using less memory, than do other, comparable eigensolver programs. This program implements a Lanczos algorithm in the American National Standards Institute/ International Organization for Standardization (ANSI/ISO) C computing language, using the Message Passing Interface (MPI) standard to complement an eigensolver in PARPACK. [PARPACK (Parallel Arnoldi Package) is an extension, to parallel-processing computer architectures, of ARPACK (Arnoldi Package), which is a collection of Fortran 77 subroutines that solve large-scale eigenvalue problems.] The eigensolver runs on Beowulf clusters of computers at the Jet Propulsion Laboratory (JPL).

Hybrid-optimization strategy for the communication of large-scale Kinetic Monte Carlo simulation

NASA Astrophysics Data System (ADS)

Wu, Baodong; Li, Shigang; Zhang, Yunquan; Nie, Ningming

2017-02-01

The parallel Kinetic Monte Carlo (KMC) algorithm based on domain decomposition has been widely used in large-scale physical simulations. However, the communication overhead of the parallel KMC algorithm is critical, and severely degrades the overall performance and scalability. In this paper, we present a hybrid optimization strategy to reduce the communication overhead for the parallel KMC simulations. We first propose a communication aggregation algorithm to reduce the total number of messages and eliminate the communication redundancy. Then, we utilize the shared memory to reduce the memory copy overhead of the intra-node communication. Finally, we optimize the communication scheduling using the neighborhood collective operations. We demonstrate the scalability and high performance of our hybrid optimization strategy by both theoretical and experimental analysis. Results show that the optimized KMC algorithm exhibits better performance and scalability than the well-known open-source library-SPPARKS. On 32-node Xeon E5-2680 cluster (total 640 cores), the optimized algorithm reduces the communication time by 24.8% compared with SPPARKS.

Development of the Large-Scale Statistical Analysis System of Satellites Observations Data with Grid Datafarm Architecture

NASA Astrophysics Data System (ADS)

Yamamoto, K.; Murata, K.; Kimura, E.; Honda, R.

2006-12-01

In the Solar-Terrestrial Physics (STP) field, the amount of satellite observation data has been increasing every year. It is necessary to solve the following three problems to achieve large-scale statistical analyses of plenty of data. (i) More CPU power and larger memory and disk size are required. However, total powers of personal computers are not enough to analyze such amount of data. Super-computers provide a high performance CPU and rich memory area, but they are usually separated from the Internet or connected only for the purpose of programming or data file transfer. (ii) Most of the observation data files are managed at distributed data sites over the Internet. Users have to know where the data files are located. (iii) Since no common data format in the STP field is available now, users have to prepare reading program for each data by themselves. To overcome the problems (i) and (ii), we constructed a parallel and distributed data analysis environment based on the Gfarm reference implementation of the Grid Datafarm architecture. The Gfarm shares both computational resources and perform parallel distributed processings. In addition, the Gfarm provides the Gfarm filesystem which can be as virtual directory tree among nodes. The Gfarm environment is composed of three parts; a metadata server to manage distributed files information, filesystem nodes to provide computational resources and a client to throw a job into metadata server and manages data processing schedulings. In the present study, both data files and data processes are parallelized on the Gfarm with 6 file system nodes: CPU clock frequency of each node is Pentium V 1GHz, 256MB memory and40GB disk. To evaluate performances of the present Gfarm system, we scanned plenty of data files, the size of which is about 300MB for each, in three processing methods: sequential processing in one node, sequential processing by each node and parallel processing by each node. As a result, in comparison between the number of files and the elapsed time, parallel and distributed processing shorten the elapsed time to 1/5 than sequential processing. On the other hand, sequential processing times were shortened in another experiment, whose file size is smaller than 100KB. In this case, the elapsed time to scan one file is within one second. It implies that disk swap took place in case of parallel processing by each node. We note that the operation became unstable when the number of the files exceeded 1000. To overcome the problem (iii), we developed an original data class. This class supports our reading of data files with various data formats since it converts them into an original data format since it defines schemata for every type of data and encapsulates the structure of data files. In addition, since this class provides a function of time re-sampling, users can easily convert multiple data (array) with different time resolution into the same time resolution array. Finally, using the Gfarm, we achieved a high performance environment for large-scale statistical data analyses. It should be noted that the present method is effective only when one data file size is large enough. At present, we are restructuring the new Gfarm environment with 8 nodes: CPU is Athlon 64 x2 Dual Core 2GHz, 2GB memory and 1.2TB disk (using RAID0) for each node. Our original class is to be implemented on the new Gfarm environment. In the present talk, we show the latest results with applying the present system for data analyses with huge number of satellite observation data files.

On program restructuring, scheduling, and communication for parallel processor systems

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

Polychronopoulos, Constantine D.

1986-08-01

This dissertation discusses several software and hardware aspects of program execution on large-scale, high-performance parallel processor systems. The issues covered are program restructuring, partitioning, scheduling and interprocessor communication, synchronization, and hardware design issues of specialized units. All this work was performed focusing on a single goal: to maximize program speedup, or equivalently, to minimize parallel execution time. Parafrase, a Fortran restructuring compiler was used to transform programs in a parallel form and conduct experiments. Two new program restructuring techniques are presented, loop coalescing and subscript blocking. Compile-time and run-time scheduling schemes are covered extensively. Depending on the program construct, thesemore » algorithms generate optimal or near-optimal schedules. For the case of arbitrarily nested hybrid loops, two optimal scheduling algorithms for dynamic and static scheduling are presented. Simulation results are given for a new dynamic scheduling algorithm. The performance of this algorithm is compared to that of self-scheduling. Techniques for program partitioning and minimization of interprocessor communication for idealized program models and for real Fortran programs are also discussed. The close relationship between scheduling, interprocessor communication, and synchronization becomes apparent at several points in this work. Finally, the impact of various types of overhead on program speedup and experimental results are presented.« less

Concurrent Programming Using Actors: Exploiting Large-Scale Parallelism,

DTIC Science & Technology

1985-10-07

ORGANIZATION NAME AND ADDRESS 10. PROGRAM ELEMENT. PROJECT. TASK* Artificial Inteligence Laboratory AREA Is WORK UNIT NUMBERS 545 Technology Square...D-R162 422 CONCURRENT PROGRMMIZNG USING f"OS XL?ITP TEH l’ LARGE-SCALE PARALLELISH(U) NASI AC E Al CAMBRIDGE ARTIFICIAL INTELLIGENCE L. G AGHA ET AL...RESOLUTION TEST CHART N~ATIONAL BUREAU OF STANDA.RDS - -96 A -E. __ _ __ __’ .,*- - -- •. - MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL

Fine grained event processing on HPCs with the ATLAS Yoda system

NASA Astrophysics Data System (ADS)

Calafiura, Paolo; De, Kaushik; Guan, Wen; Maeno, Tadashi; Nilsson, Paul; Oleynik, Danila; Panitkin, Sergey; Tsulaia, Vakhtang; Van Gemmeren, Peter; Wenaus, Torre

2015-12-01

High performance computing facilities present unique challenges and opportunities for HEP event processing. The massive scale of many HPC systems means that fractionally small utilization can yield large returns in processing throughput. Parallel applications which can dynamically and efficiently fill any scheduling opportunities the resource presents benefit both the facility (maximal utilization) and the (compute-limited) science. The ATLAS Yoda system provides this capability to HEP-like event processing applications by implementing event-level processing in an MPI-based master-client model that integrates seamlessly with the more broadly scoped ATLAS Event Service. Fine grained, event level work assignments are intelligently dispatched to parallel workers to sustain full utilization on all cores, with outputs streamed off to destination object stores in near real time with similarly fine granularity, such that processing can proceed until termination with full utilization. The system offers the efficiency and scheduling flexibility of preemption without requiring the application actually support or employ check-pointing. We will present the new Yoda system, its motivations, architecture, implementation, and applications in ATLAS data processing at several US HPC centers.

BioPig: a Hadoop-based analytic toolkit for large-scale sequence data.

PubMed

Nordberg, Henrik; Bhatia, Karan; Wang, Kai; Wang, Zhong

2013-12-01

The recent revolution in sequencing technologies has led to an exponential growth of sequence data. As a result, most of the current bioinformatics tools become obsolete as they fail to scale with data. To tackle this 'data deluge', here we introduce the BioPig sequence analysis toolkit as one of the solutions that scale to data and computation. We built BioPig on the Apache's Hadoop MapReduce system and the Pig data flow language. Compared with traditional serial and MPI-based algorithms, BioPig has three major advantages: first, BioPig's programmability greatly reduces development time for parallel bioinformatics applications; second, testing BioPig with up to 500 Gb sequences demonstrates that it scales automatically with size of data; and finally, BioPig can be ported without modification on many Hadoop infrastructures, as tested with Magellan system at National Energy Research Scientific Computing Center and the Amazon Elastic Compute Cloud. In summary, BioPig represents a novel program framework with the potential to greatly accelerate data-intensive bioinformatics analysis.

Large-scale transportation network congestion evolution prediction using deep learning theory.

PubMed

Ma, Xiaolei; Yu, Haiyang; Wang, Yunpeng; Wang, Yinhai

2015-01-01

Understanding how congestion at one location can cause ripples throughout large-scale transportation network is vital for transportation researchers and practitioners to pinpoint traffic bottlenecks for congestion mitigation. Traditional studies rely on either mathematical equations or simulation techniques to model traffic congestion dynamics. However, most of the approaches have limitations, largely due to unrealistic assumptions and cumbersome parameter calibration process. With the development of Intelligent Transportation Systems (ITS) and Internet of Things (IoT), transportation data become more and more ubiquitous. This triggers a series of data-driven research to investigate transportation phenomena. Among them, deep learning theory is considered one of the most promising techniques to tackle tremendous high-dimensional data. This study attempts to extend deep learning theory into large-scale transportation network analysis. A deep Restricted Boltzmann Machine and Recurrent Neural Network architecture is utilized to model and predict traffic congestion evolution based on Global Positioning System (GPS) data from taxi. A numerical study in Ningbo, China is conducted to validate the effectiveness and efficiency of the proposed method. Results show that the prediction accuracy can achieve as high as 88% within less than 6 minutes when the model is implemented in a Graphic Processing Unit (GPU)-based parallel computing environment. The predicted congestion evolution patterns can be visualized temporally and spatially through a map-based platform to identify the vulnerable links for proactive congestion mitigation.

Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory

PubMed Central

Ma, Xiaolei; Yu, Haiyang; Wang, Yunpeng; Wang, Yinhai

2015-01-01

Understanding how congestion at one location can cause ripples throughout large-scale transportation network is vital for transportation researchers and practitioners to pinpoint traffic bottlenecks for congestion mitigation. Traditional studies rely on either mathematical equations or simulation techniques to model traffic congestion dynamics. However, most of the approaches have limitations, largely due to unrealistic assumptions and cumbersome parameter calibration process. With the development of Intelligent Transportation Systems (ITS) and Internet of Things (IoT), transportation data become more and more ubiquitous. This triggers a series of data-driven research to investigate transportation phenomena. Among them, deep learning theory is considered one of the most promising techniques to tackle tremendous high-dimensional data. This study attempts to extend deep learning theory into large-scale transportation network analysis. A deep Restricted Boltzmann Machine and Recurrent Neural Network architecture is utilized to model and predict traffic congestion evolution based on Global Positioning System (GPS) data from taxi. A numerical study in Ningbo, China is conducted to validate the effectiveness and efficiency of the proposed method. Results show that the prediction accuracy can achieve as high as 88% within less than 6 minutes when the model is implemented in a Graphic Processing Unit (GPU)-based parallel computing environment. The predicted congestion evolution patterns can be visualized temporally and spatially through a map-based platform to identify the vulnerable links for proactive congestion mitigation. PMID:25780910

Parallel-vector out-of-core equation solver for computational mechanics

NASA Technical Reports Server (NTRS)

Qin, J.; Agarwal, T. K.; Storaasli, O. O.; Nguyen, D. T.; Baddourah, M. A.

1993-01-01

A parallel/vector out-of-core equation solver is developed for shared-memory computers, such as the Cray Y-MP machine. The input/ output (I/O) time is reduced by using the a synchronous BUFFER IN and BUFFER OUT, which can be executed simultaneously with the CPU instructions. The parallel and vector capability provided by the supercomputers is also exploited to enhance the performance. Numerical applications in large-scale structural analysis are given to demonstrate the efficiency of the present out-of-core solver.

Modification in drag of turbulent boundary layers resulting from manipulation of large-scale structures

NASA Technical Reports Server (NTRS)

Corke, T. C.; Guezennec, Y.; Nagib, H. M.

1981-01-01

The effects of placing a parallel-plate turbulence manipulator in a boundary layer are documented through flow visualization and hot wire measurements. The boundary layer manipulator was designed to manage the large scale structures of turbulence leading to a reduction in surface drag. The differences in the turbulent structure of the boundary layer are summarized to demonstrate differences in various flow properties. The manipulator inhibited the intermittent large scale structure of the turbulent boundary layer for at least 70 boundary layer thicknesses downstream. With the removal of the large scale, the streamwise turbulence intensity levels near the wall were reduced. The downstream distribution of the skin friction was also altered by the introduction of the manipulator.

On the performance of exponential integrators for problems in magnetohydrodynamics

NASA Astrophysics Data System (ADS)

Einkemmer, Lukas; Tokman, Mayya; Loffeld, John

2017-02-01

Exponential integrators have been introduced as an efficient alternative to explicit and implicit methods for integrating large stiff systems of differential equations. Over the past decades these methods have been studied theoretically and their performance was evaluated using a range of test problems. While the results of these investigations showed that exponential integrators can provide significant computational savings, the research on validating this hypothesis for large scale systems and understanding what classes of problems can particularly benefit from the use of the new techniques is in its initial stages. Resistive magnetohydrodynamic (MHD) modeling is widely used in studying large scale behavior of laboratory and astrophysical plasmas. In many problems numerical solution of MHD equations is a challenging task due to the temporal stiffness of this system in the parameter regimes of interest. In this paper we evaluate the performance of exponential integrators on large MHD problems and compare them to a state-of-the-art implicit time integrator. Both the variable and constant time step exponential methods of EPIRK-type are used to simulate magnetic reconnection and the Kevin-Helmholtz instability in plasma. Performance of these methods, which are part of the EPIC software package, is compared to the variable time step variable order BDF scheme included in the CVODE (part of SUNDIALS) library. We study performance of the methods on parallel architectures and with respect to magnitudes of important parameters such as Reynolds, Lundquist, and Prandtl numbers. We find that the exponential integrators provide superior or equal performance in most circumstances and conclude that further development of exponential methods for MHD problems is warranted and can lead to significant computational advantages for large scale stiff systems of differential equations such as MHD.

Optimal processor assignment for pipeline computations

NASA Technical Reports Server (NTRS)

Nicol, David M.; Simha, Rahul; Choudhury, Alok N.; Narahari, Bhagirath

1991-01-01

The availability of large scale multitasked parallel architectures introduces the following processor assignment problem for pipelined computations. Given a set of tasks and their precedence constraints, along with their experimentally determined individual responses times for different processor sizes, find an assignment of processor to tasks. Two objectives are of interest: minimal response given a throughput requirement, and maximal throughput given a response time requirement. These assignment problems differ considerably from the classical mapping problem in which several tasks share a processor; instead, it is assumed that a large number of processors are to be assigned to a relatively small number of tasks. Efficient assignment algorithms were developed for different classes of task structures. For a p processor system and a series parallel precedence graph with n constituent tasks, an O(np2) algorithm is provided that finds the optimal assignment for the response time optimization problem; it was found that the assignment optimizing the constrained throughput in O(np2log p) time. Special cases of linear, independent, and tree graphs are also considered.

Fault Tolerant Frequent Pattern Mining

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

Shohdy, Sameh; Vishnu, Abhinav; Agrawal, Gagan

FP-Growth algorithm is a Frequent Pattern Mining (FPM) algorithm that has been extensively used to study correlations and patterns in large scale datasets. While several researchers have designed distributed memory FP-Growth algorithms, it is pivotal to consider fault tolerant FP-Growth, which can address the increasing fault rates in large scale systems. In this work, we propose a novel parallel, algorithm-level fault-tolerant FP-Growth algorithm. We leverage algorithmic properties and MPI advanced features to guarantee an O(1) space complexity, achieved by using the dataset memory space itself for checkpointing. We also propose a recovery algorithm that can use in-memory and disk-based checkpointing,more » though in many cases the recovery can be completed without any disk access, and incurring no memory overhead for checkpointing. We evaluate our FT algorithm on a large scale InfiniBand cluster with several large datasets using up to 2K cores. Our evaluation demonstrates excellent efficiency for checkpointing and recovery in comparison to the disk-based approach. We have also observed 20x average speed-up in comparison to Spark, establishing that a well designed algorithm can easily outperform a solution based on a general fault-tolerant programming model.« less

An approach to enhance pnetCDF performance in ...

EPA Pesticide Factsheets

Data intensive simulations are often limited by their I/O (input/output) performance, and "novel" techniques need to be developed in order to overcome this limitation. The software package pnetCDF (parallel network Common Data Form), which works with parallel file systems, was developed to address this issue by providing parallel I/O capability. This study examines the performance of an application-level data aggregation approach which performs data aggregation along either row or column dimension of MPI (Message Passing Interface) processes on a spatially decomposed domain, and then applies the pnetCDF parallel I/O paradigm. The test was done with three different domain sizes which represent small, moderately large, and large data domains, using a small-scale Community Multiscale Air Quality model (CMAQ) mock-up code. The examination includes comparing I/O performance with traditional serial I/O technique, straight application of pnetCDF, and the data aggregation along row and column dimension before applying pnetCDF. After the comparison, "optimal" I/O configurations of this application-level data aggregation approach were quantified. Data aggregation along the row dimension (pnetCDFcr) works better than along the column dimension (pnetCDFcc) although it may perform slightly worse than the straight pnetCDF method with a small number of processors. When the number of processors becomes larger, pnetCDFcr outperforms pnetCDF significantly. If the number of proces

Seismic anisotropy of the lithosphere/asthenosphere system beneath the Rwenzori region of the Albertine Rift

NASA Astrophysics Data System (ADS)

Homuth, B.; Löbl, U.; Batte, A. G.; Link, K.; Kasereka, C. M.; Rümpker, G.

2016-09-01

Shear-wave splitting measurements from local and teleseismic earthquakes are used to investigate the seismic anisotropy in the upper mantle beneath the Rwenzori region of the East African Rift system. At most stations, shear-wave splitting parameters obtained from individual earthquakes exhibit only minor variations with backazimuth. We therefore employ a joint inversion of SKS waveforms to derive hypothetical one-layer parameters. The corresponding fast polarizations are generally rift parallel and the average delay time is about 1 s. Shear phases from local events within the crust are characterized by an average delay time of 0.04 s. Delay times from local mantle earthquakes are in the range of 0.2 s. This observation suggests that the dominant source region for seismic anisotropy beneath the rift is located within the mantle. We use finite-frequency waveform modeling to test different models of anisotropy within the lithosphere/asthenosphere system of the rift. The results show that the rift-parallel fast polarizations are consistent with horizontal transverse isotropy (HTI anisotropy) caused by rift-parallel magmatic intrusions or lenses located within the lithospheric mantle—as it would be expected during the early stages of continental rifting. Furthermore, the short-scale spatial variations in the fast polarizations observed in the southern part of the study area can be explained by effects due to sedimentary basins of low isotropic velocity in combination with a shift in the orientation of anisotropic fabrics in the upper mantle. A uniform anisotropic layer in relation to large-scale asthenospheric mantle flow is less consistent with the observed splitting parameters.

Development of a parallel FE simulator for modeling the whole trans-scale failure process of rock from meso- to engineering-scale

NASA Astrophysics Data System (ADS)

Li, Gen; Tang, Chun-An; Liang, Zheng-Zhao

2017-01-01

Multi-scale high-resolution modeling of rock failure process is a powerful means in modern rock mechanics studies to reveal the complex failure mechanism and to evaluate engineering risks. However, multi-scale continuous modeling of rock, from deformation, damage to failure, has raised high requirements on the design, implementation scheme and computation capacity of the numerical software system. This study is aimed at developing the parallel finite element procedure, a parallel rock failure process analysis (RFPA) simulator that is capable of modeling the whole trans-scale failure process of rock. Based on the statistical meso-damage mechanical method, the RFPA simulator is able to construct heterogeneous rock models with multiple mechanical properties, deal with and represent the trans-scale propagation of cracks, in which the stress and strain fields are solved for the damage evolution analysis of representative volume element by the parallel finite element method (FEM) solver. This paper describes the theoretical basis of the approach and provides the details of the parallel implementation on a Windows - Linux interactive platform. A numerical model is built to test the parallel performance of FEM solver. Numerical simulations are then carried out on a laboratory-scale uniaxial compression test, and field-scale net fracture spacing and engineering-scale rock slope examples, respectively. The simulation results indicate that relatively high speedup and computation efficiency can be achieved by the parallel FEM solver with a reasonable boot process. In laboratory-scale simulation, the well-known physical phenomena, such as the macroscopic fracture pattern and stress-strain responses, can be reproduced. In field-scale simulation, the formation process of net fracture spacing from initiation, propagation to saturation can be revealed completely. In engineering-scale simulation, the whole progressive failure process of the rock slope can be well modeled. It is shown that the parallel FE simulator developed in this study is an efficient tool for modeling the whole trans-scale failure process of rock from meso- to engineering-scale.

Large-scale virtual screening on public cloud resources with Apache Spark.

PubMed

Capuccini, Marco; Ahmed, Laeeq; Schaal, Wesley; Laure, Erwin; Spjuth, Ola

2017-01-01

Structure-based virtual screening is an in-silico method to screen a target receptor against a virtual molecular library. Applying docking-based screening to large molecular libraries can be computationally expensive, however it constitutes a trivially parallelizable task. Most of the available parallel implementations are based on message passing interface, relying on low failure rate hardware and fast network connection. Google's MapReduce revolutionized large-scale analysis, enabling the processing of massive datasets on commodity hardware and cloud resources, providing transparent scalability and fault tolerance at the software level. Open source implementations of MapReduce include Apache Hadoop and the more recent Apache Spark. We developed a method to run existing docking-based screening software on distributed cloud resources, utilizing the MapReduce approach. We benchmarked our method, which is implemented in Apache Spark, docking a publicly available target receptor against [Formula: see text]2.2 M compounds. The performance experiments show a good parallel efficiency (87%) when running in a public cloud environment. Our method enables parallel Structure-based virtual screening on public cloud resources or commodity computer clusters. The degree of scalability that we achieve allows for trying out our method on relatively small libraries first and then to scale to larger libraries. Our implementation is named Spark-VS and it is freely available as open source from GitHub (https://github.com/mcapuccini/spark-vs).Graphical abstract.

The high performance parallel algorithm for Unified Gas-Kinetic Scheme

NASA Astrophysics Data System (ADS)

Li, Shiyi; Li, Qibing; Fu, Song; Xu, Jinxiu

2016-11-01

A high performance parallel algorithm for UGKS is developed to simulate three-dimensional flows internal and external on arbitrary grid system. The physical domain and velocity domain are divided into different blocks and distributed according to the two-dimensional Cartesian topology with intra-communicators in physical domain for data exchange and other intra-communicators in velocity domain for sum reduction to moment integrals. Numerical results of three-dimensional cavity flow and flow past a sphere agree well with the results from the existing studies and validate the applicability of the algorithm. The scalability of the algorithm is tested both on small (1-16) and large (729-5832) scale processors. The tested speed-up ratio is near linear ashind thus the efficiency is around 1, which reveals the good scalability of the present algorithm.

Epidemic failure detection and consensus for extreme parallelism

DOE PAGES

Katti, Amogh; Di Fatta, Giuseppe; Naughton, Thomas; ...

2017-02-01

Future extreme-scale high-performance computing systems will be required to work under frequent component failures. The MPI Forum s User Level Failure Mitigation proposal has introduced an operation, MPI Comm shrink, to synchronize the alive processes on the list of failed processes, so that applications can continue to execute even in the presence of failures by adopting algorithm-based fault tolerance techniques. This MPI Comm shrink operation requires a failure detection and consensus algorithm. This paper presents three novel failure detection and consensus algorithms using Gossiping. The proposed algorithms were implemented and tested using the Extreme-scale Simulator. The results show that inmore » all algorithms the number of Gossip cycles to achieve global consensus scales logarithmically with system size. The second algorithm also shows better scalability in terms of memory and network bandwidth usage and a perfect synchronization in achieving global consensus. The third approach is a three-phase distributed failure detection and consensus algorithm and provides consistency guarantees even in very large and extreme-scale systems while at the same time being memory and bandwidth efficient.« less

Block Preconditioning to Enable Physics-Compatible Implicit Multifluid Plasma Simulations

NASA Astrophysics Data System (ADS)

Phillips, Edward; Shadid, John; Cyr, Eric; Miller, Sean

2017-10-01

Multifluid plasma simulations involve large systems of partial differential equations in which many time-scales ranging over many orders of magnitude arise. Since the fastest of these time-scales may set a restrictively small time-step limit for explicit methods, the use of implicit or implicit-explicit time integrators can be more tractable for obtaining dynamics at time-scales of interest. Furthermore, to enforce properties such as charge conservation and divergence-free magnetic field, mixed discretizations using volume, nodal, edge-based, and face-based degrees of freedom are often employed in some form. Together with the presence of stiff modes due to integrating over fast time-scales, the mixed discretization makes the required linear solves for implicit methods particularly difficult for black box and monolithic solvers. This work presents a block preconditioning strategy for multifluid plasma systems that segregates the linear system based on discretization type and approximates off-diagonal coupling in block diagonal Schur complement operators. By employing multilevel methods for the block diagonal subsolves, this strategy yields algorithmic and parallel scalability which we demonstrate on a range of problems.

A gyrofluid description of Alfvenic turbulence and its parallel electric field

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

Bian, N. H.; Kontar, E. P.

2010-06-15

Anisotropic Alfvenic fluctuations with k{sub ||}/k{sub perpendicular}<<1 remain at frequencies much smaller than the ion cyclotron frequency in the presence of a strong background magnetic field. Based on the simplest truncation of the electromagnetic gyrofluid equations in a homogeneous plasma, a model for the energy cascade produced by Alfvenic turbulence is constructed, which smoothly connects the large magnetohydrodynamics scales and the small 'kinetic' scales. Scaling relations are obtained for the electromagnetic fluctuations, as a function of k{sub perpendicular} and k{sub ||}. Moreover, a particular attention is paid to the spectral structure of the parallel electric field which is produced bymore » Alfvenic turbulence. The reason is the potential implication of this parallel electric field in turbulent acceleration and transport of particles. For electromagnetic turbulence, this issue was raised some time ago in Hasegawa and Mima [J. Geophys. Res. 83, 1117 (1978)].« less

Parallel-Processing Test Bed For Simulation Software

NASA Technical Reports Server (NTRS)

Blech, Richard; Cole, Gary; Townsend, Scott

1996-01-01

Second-generation Hypercluster computing system is multiprocessor test bed for research on parallel algorithms for simulation in fluid dynamics, electromagnetics, chemistry, and other fields with large computational requirements but relatively low input/output requirements. Built from standard, off-shelf hardware readily upgraded as improved technology becomes available. System used for experiments with such parallel-processing concepts as message-passing algorithms, debugging software tools, and computational steering. First-generation Hypercluster system described in "Hypercluster Parallel Processor" (LEW-15283).

Quaternary Morphodynamics of Fluvial Dispersal Systems Revealed: The Fly River, PNG, and the Sunda Shelf, SE Asia, simulated with the Massively Parallel GPU-based Model 'GULLEM'

NASA Astrophysics Data System (ADS)

Aalto, R. E.; Lauer, J. W.; Darby, S. E.; Best, J.; Dietrich, W. E.

2015-12-01

During glacial-marine transgressions vast volumes of sediment are deposited due to the infilling of lowland fluvial systems and shallow shelves, material that is removed during ensuing regressions. Modelling these processes would illuminate system morphodynamics, fluxes, and 'complexity' in response to base level change, yet such problems are computationally formidable. Environmental systems are characterized by strong interconnectivity, yet traditional supercomputers have slow inter-node communication -- whereas rapidly advancing Graphics Processing Unit (GPU) technology offers vastly higher (>100x) bandwidths. GULLEM (GpU-accelerated Lowland Landscape Evolution Model) employs massively parallel code to simulate coupled fluvial-landscape evolution for complex lowland river systems over large temporal and spatial scales. GULLEM models the accommodation space carved/infilled by representing a range of geomorphic processes, including: river & tributary incision within a multi-directional flow regime, non-linear diffusion, glacial-isostatic flexure, hydraulic geometry, tectonic deformation, sediment production, transport & deposition, and full 3D tracking of all resulting stratigraphy. Model results concur with the Holocene dynamics of the Fly River, PNG -- as documented with dated cores, sonar imaging of floodbasin stratigraphy, and the observations of topographic remnants from LGM conditions. Other supporting research was conducted along the Mekong River, the largest fluvial system of the Sunda Shelf. These and other field data provide tantalizing empirical glimpses into the lowland landscapes of large rivers during glacial-interglacial transitions, observations that can be explored with this powerful numerical model. GULLEM affords estimates for the timing and flux budgets within the Fly and Sunda Systems, illustrating complex internal system responses to the external forcing of sea level and climate. Furthermore, GULLEM can be applied to most ANY fluvial system to explore processes across a wide range of temporal and spatial scales. The presentation will provide insights (& many animations) illustrating river morphodynamics & resulting landscapes formed as a result of sea level oscillations. [Image: The incised 3.2e6 km^2 Sundaland domain @ 431ka

Parallel high-performance grid computing: capabilities and opportunities of a novel demanding service and business class allowing highest resource efficiency.

PubMed

Kepper, Nick; Ettig, Ramona; Dickmann, Frank; Stehr, Rene; Grosveld, Frank G; Wedemann, Gero; Knoch, Tobias A

2010-01-01

Especially in the life-science and the health-care sectors the huge IT requirements are imminent due to the large and complex systems to be analysed and simulated. Grid infrastructures play here a rapidly increasing role for research, diagnostics, and treatment, since they provide the necessary large-scale resources efficiently. Whereas grids were first used for huge number crunching of trivially parallelizable problems, increasingly parallel high-performance computing is required. Here, we show for the prime example of molecular dynamic simulations how the presence of large grid clusters including very fast network interconnects within grid infrastructures allows now parallel high-performance grid computing efficiently and thus combines the benefits of dedicated super-computing centres and grid infrastructures. The demands for this service class are the highest since the user group has very heterogeneous requirements: i) two to many thousands of CPUs, ii) different memory architectures, iii) huge storage capabilities, and iv) fast communication via network interconnects, are all needed in different combinations and must be considered in a highly dedicated manner to reach highest performance efficiency. Beyond, advanced and dedicated i) interaction with users, ii) the management of jobs, iii) accounting, and iv) billing, not only combines classic with parallel high-performance grid usage, but more importantly is also able to increase the efficiency of IT resource providers. Consequently, the mere "yes-we-can" becomes a huge opportunity like e.g. the life-science and health-care sectors as well as grid infrastructures by reaching higher level of resource efficiency.

Divide-and-conquer density functional theory on hierarchical real-space grids: Parallel implementation and applications

NASA Astrophysics Data System (ADS)

Shimojo, Fuyuki; Kalia, Rajiv K.; Nakano, Aiichiro; Vashishta, Priya

2008-02-01

A linear-scaling algorithm based on a divide-and-conquer (DC) scheme has been designed to perform large-scale molecular-dynamics (MD) simulations, in which interatomic forces are computed quantum mechanically in the framework of the density functional theory (DFT). Electronic wave functions are represented on a real-space grid, which is augmented with a coarse multigrid to accelerate the convergence of iterative solutions and with adaptive fine grids around atoms to accurately calculate ionic pseudopotentials. Spatial decomposition is employed to implement the hierarchical-grid DC-DFT algorithm on massively parallel computers. The largest benchmark tests include 11.8×106 -atom ( 1.04×1012 electronic degrees of freedom) calculation on 131 072 IBM BlueGene/L processors. The DC-DFT algorithm has well-defined parameters to control the data locality, with which the solutions converge rapidly. Also, the total energy is well conserved during the MD simulation. We perform first-principles MD simulations based on the DC-DFT algorithm, in which large system sizes bring in excellent agreement with x-ray scattering measurements for the pair-distribution function of liquid Rb and allow the description of low-frequency vibrational modes of graphene. The band gap of a CdSe nanorod calculated by the DC-DFT algorithm agrees well with the available conventional DFT results. With the DC-DFT algorithm, the band gap is calculated for larger system sizes until the result reaches the asymptotic value.

Molcas 8: New capabilities for multiconfigurational quantum chemical calculations across the periodic table.

PubMed

Aquilante, Francesco; Autschbach, Jochen; Carlson, Rebecca K; Chibotaru, Liviu F; Delcey, Mickaël G; De Vico, Luca; Fdez Galván, Ignacio; Ferré, Nicolas; Frutos, Luis Manuel; Gagliardi, Laura; Garavelli, Marco; Giussani, Angelo; Hoyer, Chad E; Li Manni, Giovanni; Lischka, Hans; Ma, Dongxia; Malmqvist, Per Åke; Müller, Thomas; Nenov, Artur; Olivucci, Massimo; Pedersen, Thomas Bondo; Peng, Daoling; Plasser, Felix; Pritchard, Ben; Reiher, Markus; Rivalta, Ivan; Schapiro, Igor; Segarra-Martí, Javier; Stenrup, Michael; Truhlar, Donald G; Ungur, Liviu; Valentini, Alessio; Vancoillie, Steven; Veryazov, Valera; Vysotskiy, Victor P; Weingart, Oliver; Zapata, Felipe; Lindh, Roland

2016-02-15

In this report, we summarize and describe the recent unique updates and additions to the Molcas quantum chemistry program suite as contained in release version 8. These updates include natural and spin orbitals for studies of magnetic properties, local and linear scaling methods for the Douglas-Kroll-Hess transformation, the generalized active space concept in MCSCF methods, a combination of multiconfigurational wave functions with density functional theory in the MC-PDFT method, additional methods for computation of magnetic properties, methods for diabatization, analytical gradients of state average complete active space SCF in association with density fitting, methods for constrained fragment optimization, large-scale parallel multireference configuration interaction including analytic gradients via the interface to the Columbus package, and approximations of the CASPT2 method to be used for computations of large systems. In addition, the report includes the description of a computational machinery for nonlinear optical spectroscopy through an interface to the QM/MM package Cobramm. Further, a module to run molecular dynamics simulations is added, two surface hopping algorithms are included to enable nonadiabatic calculations, and the DQ method for diabatization is added. Finally, we report on the subject of improvements with respects to alternative file options and parallelization. © 2015 Wiley Periodicals, Inc.

Converging evolution leads to near maximal junction diversity through parallel mechanisms in B and T cell receptors

NASA Astrophysics Data System (ADS)

Benichou, Jennifer I. C.; van Heijst, Jeroen W. J.; Glanville, Jacob; Louzoun, Yoram

2017-08-01

T and B cell receptor (TCR and BCR) complementarity determining region 3 (CDR3) genetic diversity is produced through multiple diversification and selection stages. Potential holes in the CDR3 repertoire were argued to be linked to immunodeficiencies and diseases. In contrast with BCRs, TCRs have practically no Dβ germline genetic diversity, and the question emerges as to whether they can produce a diverse CDR3 repertoire. In order to address the genetic diversity of the adaptive immune system, appropriate quantitative measures for diversity and large-scale sequencing are required. Such a diversity method should incorporate the complex diversification mechanisms of the adaptive immune response and the BCR and TCR loci structure. We combined large-scale sequencing and diversity measures to show that TCRs have a near maximal CDR3 genetic diversity. Specifically, TCR have a larger junctional and V germline diversity, which starts more 5‧ in Vβ than BCRs. Selection decreases the TCR repertoire diversity, but does not affect BCR repertoire. As a result, TCR is as diverse as BCR repertoire, with a biased CDR3 length toward short TCRs and long BCRs. These differences suggest parallel converging evolutionary tracks to reach the required diversity to avoid holes in the CDR3 repertoire.

Onset of a Large Ejective Solar Eruption from a Typical Coronal-jet-base Field Configuration

NASA Astrophysics Data System (ADS)

Joshi, Navin Chandra; Sterling, Alphonse C.; Moore, Ronald L.; Magara, Tetsuya; Moon, Yong-Jae

2017-08-01

Utilizing multiwavelength observations and magnetic field data from the Solar Dynamics Observatory (SDO)/Atmospheric Imaging Assembly (AIA), SDO/Helioseismic and Magnetic Imager (HMI), the Geostationary Operational Environmental Satellite (GOES), and RHESSI, we investigate a large-scale ejective solar eruption of 2014 December 18 from active region NOAA 12241. This event produced a distinctive “three-ribbon” flare, having two parallel ribbons corresponding to the ribbons of a standard two-ribbon flare, and a larger-scale third quasi-circular ribbon offset from the other two. There are two components to this eruptive event. First, a flux rope forms above a strong-field polarity inversion line and erupts and grows as the parallel ribbons turn on, grow, and spread apart from that polarity inversion line; this evolution is consistent with the mechanism of tether-cutting reconnection for eruptions. Second, the eruption of the arcade that has the erupting flux rope in its core undergoes magnetic reconnection at the null point of a fan dome that envelops the erupting arcade, resulting in formation of the quasi-circular ribbon; this is consistent with the breakout reconnection mechanism for eruptions. We find that the parallel ribbons begin well before (˜12 minutes) the onset of the circular ribbon, indicating that tether-cutting reconnection (or a non-ideal MHD instability) initiated this event, rather than breakout reconnection. The overall setup for this large-scale eruption (diameter of the circular ribbon ˜105 km) is analogous to that of coronal jets (base size ˜104 km), many of which, according to recent findings, result from eruptions of small-scale “minifilaments.” Thus these findings confirm that eruptions of sheared-core magnetic arcades seated in fan-spine null-point magnetic topology happen on a wide range of size scales on the Sun.

Population Annealing Monte Carlo for Frustrated Systems

NASA Astrophysics Data System (ADS)

Amey, Christopher; Machta, Jonathan

Population annealing is a sequential Monte Carlo algorithm that efficiently simulates equilibrium systems with rough free energy landscapes such as spin glasses and glassy fluids. A large population of configurations is initially thermalized at high temperature and then cooled to low temperature according to an annealing schedule. The population is kept in thermal equilibrium at every annealing step via resampling configurations according to their Boltzmann weights. Population annealing is comparable to parallel tempering in terms of efficiency, but has several distinct and useful features. In this talk I will give an introduction to population annealing and present recent progress in understanding its equilibration properties and optimizing it for spin glasses. Results from large-scale population annealing simulations for the Ising spin glass in 3D and 4D will be presented. NSF Grant DMR-1507506.

Comparing multi-module connections in membrane chromatography scale-up.

PubMed

Yu, Zhou; Karkaria, Tishtar; Espina, Marianela; Hunjun, Manjeet; Surendran, Abera; Luu, Tina; Telychko, Julia; Yang, Yan-Ping

2015-07-20

Membrane chromatography is increasingly used for protein purification in the biopharmaceutical industry. Membrane adsorbers are often pre-assembled by manufacturers as ready-to-use modules. In large-scale protein manufacturing settings, the use of multiple membrane modules for a single batch is often required due to the large quantity of feed material. The question as to how multiple modules can be connected to achieve optimum separation and productivity has been previously approached using model proteins and mass transport theories. In this study, we compare the performance of multiple membrane modules in series and in parallel in the production of a protein antigen. Series connection was shown to provide superior separation compared to parallel connection in the context of competitive adsorption. Copyright © 2015 Elsevier B.V. All rights reserved.

Scalable Performance Measurement and Analysis

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

Gamblin, Todd

2009-01-01

Concurrency levels in large-scale, distributed-memory supercomputers are rising exponentially. Modern machines may contain 100,000 or more microprocessor cores, and the largest of these, IBM's Blue Gene/L, contains over 200,000 cores. Future systems are expected to support millions of concurrent tasks. In this dissertation, we focus on efficient techniques for measuring and analyzing the performance of applications running on very large parallel machines. Tuning the performance of large-scale applications can be a subtle and time-consuming task because application developers must measure and interpret data from many independent processes. While the volume of the raw data scales linearly with the number ofmore » tasks in the running system, the number of tasks is growing exponentially, and data for even small systems quickly becomes unmanageable. Transporting performance data from so many processes over a network can perturb application performance and make measurements inaccurate, and storing such data would require a prohibitive amount of space. Moreover, even if it were stored, analyzing the data would be extremely time-consuming. In this dissertation, we present novel methods for reducing performance data volume. The first draws on multi-scale wavelet techniques from signal processing to compress systemwide, time-varying load-balance data. The second uses statistical sampling to select a small subset of running processes to generate low-volume traces. A third approach combines sampling and wavelet compression to stratify performance data adaptively at run-time and to reduce further the cost of sampled tracing. We have integrated these approaches into Libra, a toolset for scalable load-balance analysis. We present Libra and show how it can be used to analyze data from large scientific applications scalably.« less

MOLNs: A CLOUD PLATFORM FOR INTERACTIVE, REPRODUCIBLE, AND SCALABLE SPATIAL STOCHASTIC COMPUTATIONAL EXPERIMENTS IN SYSTEMS BIOLOGY USING PyURDME.

PubMed

Drawert, Brian; Trogdon, Michael; Toor, Salman; Petzold, Linda; Hellander, Andreas

2016-01-01

Computational experiments using spatial stochastic simulations have led to important new biological insights, but they require specialized tools and a complex software stack, as well as large and scalable compute and data analysis resources due to the large computational cost associated with Monte Carlo computational workflows. The complexity of setting up and managing a large-scale distributed computation environment to support productive and reproducible modeling can be prohibitive for practitioners in systems biology. This results in a barrier to the adoption of spatial stochastic simulation tools, effectively limiting the type of biological questions addressed by quantitative modeling. In this paper, we present PyURDME, a new, user-friendly spatial modeling and simulation package, and MOLNs, a cloud computing appliance for distributed simulation of stochastic reaction-diffusion models. MOLNs is based on IPython and provides an interactive programming platform for development of sharable and reproducible distributed parallel computational experiments.

A cooperative strategy for parameter estimation in large scale systems biology models.

PubMed

Villaverde, Alejandro F; Egea, Jose A; Banga, Julio R

2012-06-22

Mathematical models play a key role in systems biology: they summarize the currently available knowledge in a way that allows to make experimentally verifiable predictions. Model calibration consists of finding the parameters that give the best fit to a set of experimental data, which entails minimizing a cost function that measures the goodness of this fit. Most mathematical models in systems biology present three characteristics which make this problem very difficult to solve: they are highly non-linear, they have a large number of parameters to be estimated, and the information content of the available experimental data is frequently scarce. Hence, there is a need for global optimization methods capable of solving this problem efficiently. A new approach for parameter estimation of large scale models, called Cooperative Enhanced Scatter Search (CeSS), is presented. Its key feature is the cooperation between different programs ("threads") that run in parallel in different processors. Each thread implements a state of the art metaheuristic, the enhanced Scatter Search algorithm (eSS). Cooperation, meaning information sharing between threads, modifies the systemic properties of the algorithm and allows to speed up performance. Two parameter estimation problems involving models related with the central carbon metabolism of E. coli which include different regulatory levels (metabolic and transcriptional) are used as case studies. The performance and capabilities of the method are also evaluated using benchmark problems of large-scale global optimization, with excellent results. The cooperative CeSS strategy is a general purpose technique that can be applied to any model calibration problem. Its capability has been demonstrated by calibrating two large-scale models of different characteristics, improving the performance of previously existing methods in both cases. The cooperative metaheuristic presented here can be easily extended to incorporate other global and local search solvers and specific structural information for particular classes of problems.

A cooperative strategy for parameter estimation in large scale systems biology models

PubMed Central

2012-01-01

Background Mathematical models play a key role in systems biology: they summarize the currently available knowledge in a way that allows to make experimentally verifiable predictions. Model calibration consists of finding the parameters that give the best fit to a set of experimental data, which entails minimizing a cost function that measures the goodness of this fit. Most mathematical models in systems biology present three characteristics which make this problem very difficult to solve: they are highly non-linear, they have a large number of parameters to be estimated, and the information content of the available experimental data is frequently scarce. Hence, there is a need for global optimization methods capable of solving this problem efficiently. Results A new approach for parameter estimation of large scale models, called Cooperative Enhanced Scatter Search (CeSS), is presented. Its key feature is the cooperation between different programs (“threads”) that run in parallel in different processors. Each thread implements a state of the art metaheuristic, the enhanced Scatter Search algorithm (eSS). Cooperation, meaning information sharing between threads, modifies the systemic properties of the algorithm and allows to speed up performance. Two parameter estimation problems involving models related with the central carbon metabolism of E. coli which include different regulatory levels (metabolic and transcriptional) are used as case studies. The performance and capabilities of the method are also evaluated using benchmark problems of large-scale global optimization, with excellent results. Conclusions The cooperative CeSS strategy is a general purpose technique that can be applied to any model calibration problem. Its capability has been demonstrated by calibrating two large-scale models of different characteristics, improving the performance of previously existing methods in both cases. The cooperative metaheuristic presented here can be easily extended to incorporate other global and local search solvers and specific structural information for particular classes of problems. PMID:22727112

A Screen Space GPGPU Surface LIC Algorithm for Distributed Memory Data Parallel Sort Last Rendering Infrastructures

NASA Astrophysics Data System (ADS)

Loring, B.; Karimabadi, H.; Rortershteyn, V.

2015-10-01

The surface line integral convolution(LIC) visualization technique produces dense visualization of vector fields on arbitrary surfaces. We present a screen space surface LIC algorithm for use in distributed memory data parallel sort last rendering infrastructures. The motivations for our work are to support analysis of datasets that are too large to fit in the main memory of a single computer and compatibility with prevalent parallel scientific visualization tools such as ParaView and VisIt. By working in screen space using OpenGL we can leverage the computational power of GPUs when they are available and run without them when they are not. We address efficiency and performance issues that arise from the transformation of data from physical to screen space by selecting an alternate screen space domain decomposition. We analyze the algorithm's scaling behavior with and without GPUs on two high performance computing systems using data from turbulent plasma simulations.

A Screen Space GPGPU Surface LIC Algorithm for Distributed Memory Data Parallel Sort Last Rendering Infrastructures

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

Loring, Burlen; Karimabadi, Homa; Rortershteyn, Vadim

2014-07-01

The surface line integral convolution(LIC) visualization technique produces dense visualization of vector fields on arbitrary surfaces. We present a screen space surface LIC algorithm for use in distributed memory data parallel sort last rendering infrastructures. The motivations for our work are to support analysis of datasets that are too large to fit in the main memory of a single computer and compatibility with prevalent parallel scientific visualization tools such as ParaView and VisIt. By working in screen space using OpenGL we can leverage the computational power of GPUs when they are available and run without them when they are not.more » We address efficiency and performance issues that arise from the transformation of data from physical to screen space by selecting an alternate screen space domain decomposition. We analyze the algorithm's scaling behavior with and without GPUs on two high performance computing systems using data from turbulent plasma simulations.« less

The linearly scaling 3D fragment method for large scale electronic structure calculations

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

Zhao, Zhengji; Meza, Juan; Lee, Byounghak

2009-07-28

The Linearly Scaling three-dimensional fragment (LS3DF) method is an O(N) ab initio electronic structure method for large-scale nano material simulations. It is a divide-and-conquer approach with a novel patching scheme that effectively cancels out the artificial boundary effects, which exist in all divide-and-conquer schemes. This method has made ab initio simulations of thousand-atom nanosystems feasible in a couple of hours, while retaining essentially the same accuracy as the direct calculation methods. The LS3DF method won the 2008 ACM Gordon Bell Prize for algorithm innovation. Our code has reached 442 Tflop/s running on 147,456 processors on the Cray XT5 (Jaguar) atmore » OLCF, and has been run on 163,840 processors on the Blue Gene/P (Intrepid) at ALCF, and has been applied to a system containing 36,000 atoms. In this paper, we will present the recent parallel performance results of this code, and will apply the method to asymmetric CdSe/CdS core/shell nanorods, which have potential applications in electronic devices and solar cells.« less

The Linearly Scaling 3D Fragment Method for Large Scale Electronic Structure Calculations

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

Zhao, Zhengji; Meza, Juan; Lee, Byounghak

2009-06-26

The Linearly Scaling three-dimensional fragment (LS3DF) method is an O(N) ab initio electronic structure method for large-scale nano material simulations. It is a divide-and-conquer approach with a novel patching scheme that effectively cancels out the artificial boundary effects, which exist in all divide-and-conquer schemes. This method has made ab initio simulations of thousand-atom nanosystems feasible in a couple of hours, while retaining essentially the same accuracy as the direct calculation methods. The LS3DF method won the 2008 ACM Gordon Bell Prize for algorithm innovation. Our code has reached 442 Tflop/s running on 147,456 processors on the Cray XT5 (Jaguar) atmore » OLCF, and has been run on 163,840 processors on the Blue Gene/P (Intrepid) at ALCF, and has been applied to a system containing 36,000 atoms. In this paper, we will present the recent parallel performance results of this code, and will apply the method to asymmetric CdSe/CdS core/shell nanorods, which have potential applications in electronic devices and solar cells.« less

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

Lehtola, Susi; Parkhill, John; Head-Gordon, Martin

Novel implementations based on dense tensor storage are presented here for the singlet-reference perfect quadruples (PQ) [J. A. Parkhill et al., J. Chem. Phys. 130, 084101 (2009)] and perfect hextuples (PH) [J. A. Parkhill and M. Head-Gordon, J. Chem. Phys. 133, 024103 (2010)] models. The methods are obtained as block decompositions of conventional coupled-cluster theory that are exact for four electrons in four orbitals (PQ) and six electrons in six orbitals (PH), but that can also be applied to much larger systems. PQ and PH have storage requirements that scale as the square, and as the cube of the numbermore » of active electrons, respectively, and exhibit quartic scaling of the computational effort for large systems. Applications of the new implementations are presented for full-valence calculations on linear polyenes (C nH n+2), which highlight the excellent computational scaling of the present implementations that can routinely handle active spaces of hundreds of electrons. The accuracy of the models is studied in the π space of the polyenes, in hydrogen chains (H 50), and in the π space of polyacene molecules. In all cases, the results compare favorably to density matrix renormalization group values. With the novel implementation of PQ, active spaces of 140 electrons in 140 orbitals can be solved in a matter of minutes on a single core workstation, and the relatively low polynomial scaling means that very large systems are also accessible using parallel computing.« less

Studying an Eulerian Computer Model on Different High-performance Computer Platforms and Some Applications

NASA Astrophysics Data System (ADS)

Georgiev, K.; Zlatev, Z.

2010-11-01

The Danish Eulerian Model (DEM) is an Eulerian model for studying the transport of air pollutants on large scale. Originally, the model was developed at the National Environmental Research Institute of Denmark. The model computational domain covers Europe and some neighbour parts belong to the Atlantic Ocean, Asia and Africa. If DEM model is to be applied by using fine grids, then its discretization leads to a huge computational problem. This implies that such a model as DEM must be run only on high-performance computer architectures. The implementation and tuning of such a complex large-scale model on each different computer is a non-trivial task. Here, some comparison results of running of this model on different kind of vector (CRAY C92A, Fujitsu, etc.), parallel computers with distributed memory (IBM SP, CRAY T3E, Beowulf clusters, Macintosh G4 clusters, etc.), parallel computers with shared memory (SGI Origin, SUN, etc.) and parallel computers with two levels of parallelism (IBM SMP, IBM BlueGene/P, clusters of multiprocessor nodes, etc.) will be presented. The main idea in the parallel version of DEM is domain partitioning approach. Discussions according to the effective use of the cache and hierarchical memories of the modern computers as well as the performance, speed-ups and efficiency achieved will be done. The parallel code of DEM, created by using MPI standard library, appears to be highly portable and shows good efficiency and scalability on different kind of vector and parallel computers. Some important applications of the computer model output are presented in short.

Development and Application of the Collaborative Optimization Architecture in a Multidisciplinary Design Environment

NASA Technical Reports Server (NTRS)

Braun, R. D.; Kroo, I. M.

1995-01-01

Collaborative optimization is a design architecture applicable in any multidisciplinary analysis environment but specifically intended for large-scale distributed analysis applications. In this approach, a complex problem is hierarchically de- composed along disciplinary boundaries into a number of subproblems which are brought into multidisciplinary agreement by a system-level coordination process. When applied to problems in a multidisciplinary design environment, this scheme has several advantages over traditional solution strategies. These advantageous features include reducing the amount of information transferred between disciplines, the removal of large iteration-loops, allowing the use of different subspace optimizers among the various analysis groups, an analysis framework which is easily parallelized and can operate on heterogenous equipment, and a structural framework that is well-suited for conventional disciplinary organizations. In this article, the collaborative architecture is developed and its mathematical foundation is presented. An example application is also presented which highlights the potential of this method for use in large-scale design applications.

Designing for Peta-Scale in the LSST Database

NASA Astrophysics Data System (ADS)

Kantor, J.; Axelrod, T.; Becla, J.; Cook, K.; Nikolaev, S.; Gray, J.; Plante, R.; Nieto-Santisteban, M.; Szalay, A.; Thakar, A.

2007-10-01

The Large Synoptic Survey Telescope (LSST), a proposed ground-based 8.4 m telescope with a 10 deg^2 field of view, will generate 15 TB of raw images every observing night. When calibration and processed data are added, the image archive, catalogs, and meta-data will grow 15 PB yr^{-1} on average. The LSST Data Management System (DMS) must capture, process, store, index, replicate, and provide open access to this data. Alerts must be triggered within 30 s of data acquisition. To do this in real-time at these data volumes will require advances in data management, database, and file system techniques. This paper describes the design of the LSST DMS and emphasizes features for peta-scale data. The LSST DMS will employ a combination of distributed database and file systems, with schema, partitioning, and indexing oriented for parallel operations. Image files are stored in a distributed file system with references to, and meta-data from, each file stored in the databases. The schema design supports pipeline processing, rapid ingest, and efficient query. Vertical partitioning reduces disk input/output requirements, horizontal partitioning allows parallel data access using arrays of servers and disks. Indexing is extensive, utilizing both conventional RAM-resident indexes and column-narrow, row-deep tag tables/covering indices that are extracted from tables that contain many more attributes. The DMS Data Access Framework is encapsulated in a middleware framework to provide a uniform service interface to all framework capabilities. This framework will provide the automated work-flow, replication, and data analysis capabilities necessary to make data processing and data quality analysis feasible at this scale.

Personality Assessment Inventory scale characteristics and factor structure in the assessment of alcohol dependency.

PubMed

Schinka, J A

1995-02-01

Individual scale characteristics and the inventory structure of the Personality Assessment Inventory (PAI; Morey, 1991) were examined by conducting internal consistency and factor analyses of item and scale score data from a large group (N = 301) of alcohol-dependent patients. Alpha coefficients, mean inter-item correlations, and corrected item-total scale correlations for the sample paralleled values reported by Morey for a large clinical sample. Minor differences in the scale factor structure of the inventory from Morey's clinical sample were found. Overall, the findings support the use of the PAI in the assessment of personality and psychopathology of alcohol-dependent patients.

MSAProbs-MPI: parallel multiple sequence aligner for distributed-memory systems.

PubMed

González-Domínguez, Jorge; Liu, Yongchao; Touriño, Juan; Schmidt, Bertil

2016-12-15

MSAProbs is a state-of-the-art protein multiple sequence alignment tool based on hidden Markov models. It can achieve high alignment accuracy at the expense of relatively long runtimes for large-scale input datasets. In this work we present MSAProbs-MPI, a distributed-memory parallel version of the multithreaded MSAProbs tool that is able to reduce runtimes by exploiting the compute capabilities of common multicore CPU clusters. Our performance evaluation on a cluster with 32 nodes (each containing two Intel Haswell processors) shows reductions in execution time of over one order of magnitude for typical input datasets. Furthermore, MSAProbs-MPI using eight nodes is faster than the GPU-accelerated QuickProbs running on a Tesla K20. Another strong point is that MSAProbs-MPI can deal with large datasets for which MSAProbs and QuickProbs might fail due to time and memory constraints, respectively. Source code in C ++ and MPI running on Linux systems as well as a reference manual are available at http://msaprobs.sourceforge.net CONTACT: jgonzalezd@udc.esSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Accelerating large scale Kohn-Sham density functional theory calculations with semi-local functionals and hybrid functionals

NASA Astrophysics Data System (ADS)

Lin, Lin

The computational cost of standard Kohn-Sham density functional theory (KSDFT) calculations scale cubically with respect to the system size, which limits its use in large scale applications. In recent years, we have developed an alternative procedure called the pole expansion and selected inversion (PEXSI) method. The PEXSI method solves KSDFT without solving any eigenvalue and eigenvector, and directly evaluates physical quantities including electron density, energy, atomic force, density of states, and local density of states. The overall algorithm scales as at most quadratically for all materials including insulators, semiconductors and the difficult metallic systems. The PEXSI method can be efficiently parallelized over 10,000 - 100,000 processors on high performance machines. The PEXSI method has been integrated into a number of community electronic structure software packages such as ATK, BigDFT, CP2K, DGDFT, FHI-aims and SIESTA, and has been used in a number of applications with 2D materials beyond 10,000 atoms. The PEXSI method works for LDA, GGA and meta-GGA functionals. The mathematical structure for hybrid functional KSDFT calculations is significantly different. I will also discuss recent progress on using adaptive compressed exchange method for accelerating hybrid functional calculations. DOE SciDAC Program, DOE CAMERA Program, LBNL LDRD, Sloan Fellowship.

Pragmatic clinical trials embedded in healthcare systems: generalizable lessons from the NIH Collaboratory.

PubMed

Weinfurt, Kevin P; Hernandez, Adrian F; Coronado, Gloria D; DeBar, Lynn L; Dember, Laura M; Green, Beverly B; Heagerty, Patrick J; Huang, Susan S; James, Kathryn T; Jarvik, Jeffrey G; Larson, Eric B; Mor, Vincent; Platt, Richard; Rosenthal, Gary E; Septimus, Edward J; Simon, Gregory E; Staman, Karen L; Sugarman, Jeremy; Vazquez, Miguel; Zatzick, Douglas; Curtis, Lesley H

2017-09-18

The clinical research enterprise is not producing the evidence decision makers arguably need in a timely and cost effective manner; research currently involves the use of labor-intensive parallel systems that are separate from clinical care. The emergence of pragmatic clinical trials (PCTs) poses a possible solution: these large-scale trials are embedded within routine clinical care and often involve cluster randomization of hospitals, clinics, primary care providers, etc. Interventions can be implemented by health system personnel through usual communication channels and quality improvement infrastructure, and data collected as part of routine clinical care. However, experience with these trials is nascent and best practices regarding design operational, analytic, and reporting methodologies are undeveloped. To strengthen the national capacity to implement cost-effective, large-scale PCTs, the Common Fund of the National Institutes of Health created the Health Care Systems Research Collaboratory (Collaboratory) to support the design, execution, and dissemination of a series of demonstration projects using a pragmatic research design. In this article, we will describe the Collaboratory, highlight some of the challenges encountered and solutions developed thus far, and discuss remaining barriers and opportunities for large-scale evidence generation using PCTs. A planning phase is critical, and even with careful planning, new challenges arise during execution; comparisons between arms can be complicated by unanticipated changes. Early and ongoing engagement with both health care system leaders and front-line clinicians is critical for success. There is also marked uncertainty when applying existing ethical and regulatory frameworks to PCTS, and using existing electronic health records for data capture adds complexity.

Large Scale Software Building with CMake in ATLAS

NASA Astrophysics Data System (ADS)

Elmsheuser, J.; Krasznahorkay, A.; Obreshkov, E.; Undrus, A.; ATLAS Collaboration

2017-10-01

The offline software of the ATLAS experiment at the Large Hadron Collider (LHC) serves as the platform for detector data reconstruction, simulation and analysis. It is also used in the detector’s trigger system to select LHC collision events during data taking. The ATLAS offline software consists of several million lines of C++ and Python code organized in a modular design of more than 2000 specialized packages. Because of different workflows, many stable numbered releases are in parallel production use. To accommodate specific workflow requests, software patches with modified libraries are distributed on top of existing software releases on a daily basis. The different ATLAS software applications also require a flexible build system that strongly supports unit and integration tests. Within the last year this build system was migrated to CMake. A CMake configuration has been developed that allows one to easily set up and build the above mentioned software packages. This also makes it possible to develop and test new and modified packages on top of existing releases. The system also allows one to detect and execute partial rebuilds of the release based on single package changes. The build system makes use of CPack for building RPM packages out of the software releases, and CTest for running unit and integration tests. We report on the migration and integration of the ATLAS software to CMake and show working examples of this large scale project in production.

Algorithm for solving the linear Cauchy problem for large systems of ordinary differential equations with the use of parallel computations

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

Moryakov, A. V., E-mail: sailor@orc.ru

2016-12-15

An algorithm for solving the linear Cauchy problem for large systems of ordinary differential equations is presented. The algorithm for systems of first-order differential equations is implemented in the EDELWEISS code with the possibility of parallel computations on supercomputers employing the MPI (Message Passing Interface) standard for the data exchange between parallel processes. The solution is represented by a series of orthogonal polynomials on the interval [0, 1]. The algorithm is characterized by simplicity and the possibility to solve nonlinear problems with a correction of the operator in accordance with the solution obtained in the previous iterative process.

Building brains for bodies

NASA Technical Reports Server (NTRS)

Brooks, Rodney Allen; Stein, Lynn Andrea

1994-01-01

We describe a project to capitalize on newly available levels of computational resources in order to understand human cognition. We will build an integrated physical system including vision, sound input and output, and dextrous manipulation, all controlled by a continuously operating large scale parallel MIMD computer. The resulting system will learn to 'think' by building on its bodily experiences to accomplish progressively more abstract tasks. Past experience suggests that in attempting to build such an integrated system we will have to fundamentally change the way artificial intelligence, cognitive science, linguistics, and philosophy think about the organization of intelligence. We expect to be able to better reconcile the theories that will be developed with current work in neuroscience.

The Potsdam Parallel Ice Sheet Model (PISM-PIK) - Part 1: Model description

NASA Astrophysics Data System (ADS)

Winkelmann, R.; Martin, M. A.; Haseloff, M.; Albrecht, T.; Bueler, E.; Khroulev, C.; Levermann, A.

2010-08-01

We present the Potsdam Parallel Ice Sheet Model (PISM-PIK), developed at the Potsdam Institute for Climate Impact Research to be used for simulations of large-scale ice sheet-shelf systems. It is derived from the Parallel Ice Sheet Model (Bueler and Brown, 2009). Velocities are calculated by superposition of two shallow stress balance approximations within the entire ice covered region: the shallow ice approximation (SIA) is dominant in grounded regions and accounts for shear deformation parallel to the geoid. The plug-flow type shallow shelf approximation (SSA) dominates the velocity field in ice shelf regions and serves as a basal sliding velocity in grounded regions. Ice streams naturally emerge through this approach and can be identified diagnostically as regions with a significant contribution of membrane stresses to the local momentum balance. All lateral boundaries in PISM-PIK are free to evolve, including the grounding line and ice fronts. Ice shelf margins in particular are modeled using Neumann boundary conditions for the SSA equations, reflecting a hydrostatic stress imbalance along the vertical calving face. The ice front position is modeled using a subgrid scale representation of calving front motion (Albrecht et al., 2010) and a physically motivated dynamic calving law based on horizontal spreading rates. The model is validated within the Marine Ice Sheet Model Intercomparison Project (MISMIP) and is used for a dynamic equilibrium simulation of Antarctica under present-day conditions in the second part of this paper (Martin et al., 2010).

Airbreathing Propulsion System Analysis Using Multithreaded Parallel Processing

NASA Technical Reports Server (NTRS)

Schunk, Richard Gregory; Chung, T. J.; Rodriguez, Pete (Technical Monitor)

2000-01-01

In this paper, parallel processing is used to analyze the mixing, and combustion behavior of hypersonic flow. Preliminary work for a sonic transverse hydrogen jet injected from a slot into a Mach 4 airstream in a two-dimensional duct combustor has been completed [Moon and Chung, 1996]. Our aim is to extend this work to three-dimensional domain using multithreaded domain decomposition parallel processing based on the flowfield-dependent variation theory. Numerical simulations of chemically reacting flows are difficult because of the strong interactions between the turbulent hydrodynamic and chemical processes. The algorithm must provide an accurate representation of the flowfield, since unphysical flowfield calculations will lead to the faulty loss or creation of species mass fraction, or even premature ignition, which in turn alters the flowfield information. Another difficulty arises from the disparity in time scales between the flowfield and chemical reactions, which may require the use of finite rate chemistry. The situations are more complex when there is a disparity in length scales involved in turbulence. In order to cope with these complicated physical phenomena, it is our plan to utilize the flowfield-dependent variation theory mentioned above, facilitated by large eddy simulation. Undoubtedly, the proposed computation requires the most sophisticated computational strategies. The multithreaded domain decomposition parallel processing will be necessary in order to reduce both computational time and storage. Without special treatments involved in computer engineering, our attempt to analyze the airbreathing combustion appears to be difficult, if not impossible.

Solving very large, sparse linear systems on mesh-connected parallel computers

NASA Technical Reports Server (NTRS)

Opsahl, Torstein; Reif, John

1987-01-01

The implementation of Pan and Reif's Parallel Nested Dissection (PND) algorithm on mesh connected parallel computers is described. This is the first known algorithm that allows very large, sparse linear systems of equations to be solved efficiently in polylog time using a small number of processors. How the processor bound of PND can be matched to the number of processors available on a given parallel computer by slowing down the algorithm by constant factors is described. Also, for the important class of problems where G(A) is a grid graph, a unique memory mapping that reduces the inter-processor communication requirements of PND to those that can be executed on mesh connected parallel machines is detailed. A description of an implementation on the Goodyear Massively Parallel Processor (MPP), located at Goddard is given. Also, a detailed discussion of data mappings and performance issues is given.

Decadal opportunities for space architects

NASA Astrophysics Data System (ADS)

Sherwood, Brent

2012-12-01

A significant challenge for the new field of space architecture is the dearth of project opportunities. Yet every year more young professionals express interest to enter the field. This paper derives projections that bound the number, type, and range of global development opportunities that may be reasonably expected over the next few decades for human space flight (HSF) systems so those interested in the field can benchmark their goals. Four categories of HSF activity are described: human Exploration of solar system bodies; human Servicing of space-based assets; large-scale development of space Resources; and Breakout of self-sustaining human societies into the solar system. A progressive sequence of capabilities for each category starts with its earliest feasible missions and leads toward its full expression. The four sequences are compared in scale, distance from Earth, and readiness. Scenarios hybridize the most synergistic features from the four sequences for comparison to status quo, government-funded HSF program plans. Finally qualitative, decadal, order-of-magnitude estimates are derived for system development needs, and hence opportunities for space architects. Government investment towards human planetary exploration is the weakest generator of space architecture work. Conversely, the strongest generator is a combination of three market drivers: (1) commercial passenger travel in low Earth orbit; (2) in parallel, government extension of HSF capability to GEO; both followed by (3) scale-up demonstration of end-to-end solar power satellites in GEO. The rich end of this scale affords space architecture opportunities which are more diverse, complex, large-scale, and sociologically challenging than traditional exploration vehicle cabins and habitats.

MapReduce Based Parallel Neural Networks in Enabling Large Scale Machine Learning

PubMed Central

Yang, Jie; Huang, Yuan; Xu, Lixiong; Li, Siguang; Qi, Man

2015-01-01

Artificial neural networks (ANNs) have been widely used in pattern recognition and classification applications. However, ANNs are notably slow in computation especially when the size of data is large. Nowadays, big data has received a momentum from both industry and academia. To fulfill the potentials of ANNs for big data applications, the computation process must be speeded up. For this purpose, this paper parallelizes neural networks based on MapReduce, which has become a major computing model to facilitate data intensive applications. Three data intensive scenarios are considered in the parallelization process in terms of the volume of classification data, the size of the training data, and the number of neurons in the neural network. The performance of the parallelized neural networks is evaluated in an experimental MapReduce computer cluster from the aspects of accuracy in classification and efficiency in computation. PMID:26681933

MapReduce Based Parallel Neural Networks in Enabling Large Scale Machine Learning.

PubMed

Liu, Yang; Yang, Jie; Huang, Yuan; Xu, Lixiong; Li, Siguang; Qi, Man

2015-01-01

Artificial neural networks (ANNs) have been widely used in pattern recognition and classification applications. However, ANNs are notably slow in computation especially when the size of data is large. Nowadays, big data has received a momentum from both industry and academia. To fulfill the potentials of ANNs for big data applications, the computation process must be speeded up. For this purpose, this paper parallelizes neural networks based on MapReduce, which has become a major computing model to facilitate data intensive applications. Three data intensive scenarios are considered in the parallelization process in terms of the volume of classification data, the size of the training data, and the number of neurons in the neural network. The performance of the parallelized neural networks is evaluated in an experimental MapReduce computer cluster from the aspects of accuracy in classification and efficiency in computation.

Program Correctness, Verification and Testing for Exascale (Corvette)

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

Sen, Koushik; Iancu, Costin; Demmel, James W

The goal of this project is to provide tools to assess the correctness of parallel programs written using hybrid parallelism. There is a dire lack of both theoretical and engineering know-how in the area of finding bugs in hybrid or large scale parallel programs, which our research aims to change. In the project we have demonstrated novel approaches in several areas: 1. Low overhead automated and precise detection of concurrency bugs at scale. 2. Using low overhead bug detection tools to guide speculative program transformations for performance. 3. Techniques to reduce the concurrency required to reproduce a bug using partialmore » program restart/replay. 4. Techniques to provide reproducible execution of floating point programs. 5. Techniques for tuning the floating point precision used in codes.« less

Advanced Electric Distribution, Switching, and Conversion Technology for Power Control

NASA Technical Reports Server (NTRS)

Soltis, James V.

1998-01-01

The Electrical Power Control Unit currently under development by Sundstrand Aerospace for use on the Fluids Combustion Facility of the International Space Station is the precursor of modular power distribution and conversion concepts for future spacecraft and aircraft applications. This unit combines modular current-limiting flexible remote power controllers and paralleled power converters into one package. Each unit includes three 1-kW, current-limiting power converter modules designed for a variable-ratio load sharing capability. The flexible remote power controllers can be used in parallel to match load requirements and can be programmed for an initial ON or OFF state on powerup. The unit contains an integral cold plate. The modularity and hybridization of the Electrical Power Control Unit sets the course for future spacecraft electrical power systems, both large and small. In such systems, the basic hybridized converter and flexible remote power controller building blocks could be configured to match power distribution and conversion capabilities to load requirements. In addition, the flexible remote power controllers could be configured in assemblies to feed multiple individual loads and could be used in parallel to meet the specific current requirements of each of those loads. Ultimately, the Electrical Power Control Unit design concept could evolve to a common switch module hybrid, or family of hybrids, for both converter and switchgear applications. By assembling hybrids of a common current rating and voltage class in parallel, researchers could readily adapt these units for multiple applications. The Electrical Power Control Unit concept has the potential to be scaled to larger and smaller ratings for both small and large spacecraft and for aircraft where high-power density, remote power controllers or power converters are required and a common replacement part is desired for multiples of a base current rating.

Parallel-vector computation for structural analysis and nonlinear unconstrained optimization problems

NASA Technical Reports Server (NTRS)

Nguyen, Duc T.

1990-01-01

Practical engineering application can often be formulated in the form of a constrained optimization problem. There are several solution algorithms for solving a constrained optimization problem. One approach is to convert a constrained problem into a series of unconstrained problems. Furthermore, unconstrained solution algorithms can be used as part of the constrained solution algorithms. Structural optimization is an iterative process where one starts with an initial design, a finite element structure analysis is then performed to calculate the response of the system (such as displacements, stresses, eigenvalues, etc.). Based upon the sensitivity information on the objective and constraint functions, an optimizer such as ADS or IDESIGN, can be used to find the new, improved design. For the structural analysis phase, the equation solver for the system of simultaneous, linear equations plays a key role since it is needed for either static, or eigenvalue, or dynamic analysis. For practical, large-scale structural analysis-synthesis applications, computational time can be excessively large. Thus, it is necessary to have a new structural analysis-synthesis code which employs new solution algorithms to exploit both parallel and vector capabilities offered by modern, high performance computers such as the Convex, Cray-2 and Cray-YMP computers. The objective of this research project is, therefore, to incorporate the latest development in the parallel-vector equation solver, PVSOLVE into the widely popular finite-element production code, such as the SAP-4. Furthermore, several nonlinear unconstrained optimization subroutines have also been developed and tested under a parallel computer environment. The unconstrained optimization subroutines are not only useful in their own right, but they can also be incorporated into a more popular constrained optimization code, such as ADS.

Development of the US3D Code for Advanced Compressible and Reacting Flow Simulations

NASA Technical Reports Server (NTRS)

Candler, Graham V.; Johnson, Heath B.; Nompelis, Ioannis; Subbareddy, Pramod K.; Drayna, Travis W.; Gidzak, Vladimyr; Barnhardt, Michael D.

2015-01-01

Aerothermodynamics and hypersonic flows involve complex multi-disciplinary physics, including finite-rate gas-phase kinetics, finite-rate internal energy relaxation, gas-surface interactions with finite-rate oxidation and sublimation, transition to turbulence, large-scale unsteadiness, shock-boundary layer interactions, fluid-structure interactions, and thermal protection system ablation and thermal response. Many of the flows have a large range of length and time scales, requiring large computational grids, implicit time integration, and large solution run times. The University of Minnesota NASA US3D code was designed for the simulation of these complex, highly-coupled flows. It has many of the features of the well-established DPLR code, but uses unstructured grids and has many advanced numerical capabilities and physical models for multi-physics problems. The main capabilities of the code are described, the physical modeling approaches are discussed, the different types of numerical flux functions and time integration approaches are outlined, and the parallelization strategy is overviewed. Comparisons between US3D and the NASA DPLR code are presented, and several advanced simulations are presented to illustrate some of novel features of the code.

Micro injector sample delivery system for charged molecules

DOEpatents

Davidson, James C.; Balch, Joseph W.

1999-11-09

A micro injector sample delivery system for charged molecules. The injector is used for collecting and delivering controlled amounts of charged molecule samples for subsequent analysis. The injector delivery system can be scaled to large numbers (>96) for sample delivery to massively parallel high throughput analysis systems. The essence of the injector system is an electric field controllable loading tip including a section of porous material. By applying the appropriate polarity bias potential to the injector tip, charged molecules will migrate into porous material, and by reversing the polarity bias potential the molecules are ejected or forced away from the tip. The invention has application for uptake of charged biological molecules (e.g. proteins, nucleic acids, polymers, etc.) for delivery to analytical systems, and can be used in automated sample delivery systems.

High performance computing in biology: multimillion atom simulations of nanoscale systems

PubMed Central

Sanbonmatsu, K. Y.; Tung, C.-S.

2007-01-01

Computational methods have been used in biology for sequence analysis (bioinformatics), all-atom simulation (molecular dynamics and quantum calculations), and more recently for modeling biological networks (systems biology). Of these three techniques, all-atom simulation is currently the most computationally demanding, in terms of compute load, communication speed, and memory load. Breakthroughs in electrostatic force calculation and dynamic load balancing have enabled molecular dynamics simulations of large biomolecular complexes. Here, we report simulation results for the ribosome, using approximately 2.64 million atoms, the largest all-atom biomolecular simulation published to date. Several other nanoscale systems with different numbers of atoms were studied to measure the performance of the NAMD molecular dynamics simulation program on the Los Alamos National Laboratory Q Machine. We demonstrate that multimillion atom systems represent a 'sweet spot' for the NAMD code on large supercomputers. NAMD displays an unprecedented 85% parallel scaling efficiency for the ribosome system on 1024 CPUs. We also review recent targeted molecular dynamics simulations of the ribosome that prove useful for studying conformational changes of this large biomolecular complex in atomic detail. PMID:17187988

Operation of the Institute for Computer Applications in Science and Engineering

NASA Technical Reports Server (NTRS)

1975-01-01

The ICASE research program is described in detail; it consists of four major categories: (1) efficient use of vector and parallel computers, with particular emphasis on the CDC STAR-100; (2) numerical analysis, with particular emphasis on the development and analysis of basic numerical algorithms; (3) analysis and planning of large-scale software systems; and (4) computational research in engineering and the natural sciences, with particular emphasis on fluid dynamics. The work in each of these areas is described in detail; other activities are discussed, a prognosis of future activities are included.

Traffic Flow Management and Optimization

NASA Technical Reports Server (NTRS)

Rios, Joseph Lucio

2014-01-01

This talk will present an overview of Traffic Flow Management (TFM) research at NASA Ames Research Center. Dr. Rios will focus on his work developing a large-scale, parallel approach to solving traffic flow management problems in the national airspace. In support of this talk, Dr. Rios will provide some background on operational aspects of TFM as well a discussion of some of the tools needed to perform such work including a high-fidelity airspace simulator. Current, on-going research related to TFM data services in the national airspace system and general aviation will also be presented.

The Center for Multiscale Plasma Dynamics, Final Report

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

Gombosi, Tamas I.

The University of Michigan participated in the joint UCLA/Maryland fusion science center focused on plasma physics problems for which the traditional separation of the dynamics into microscale and macroscale processes breaks down. These processes involve large scale flows and magnetic fields tightly coupled to the small scale, kinetic dynamics of turbulence, particle acceleration and energy cascade. The interaction between these vastly disparate scales controls the evolution of the system. The enormous range of temporal and spatial scales associated with these problems renders direct simulation intractable even in computations that use the largest existing parallel computers. Our efforts focused on twomore » main problems: the development of Hall MHD solvers on solution adaptive grids and the development of solution adaptive grids using generalized coordinates so that the proper geometry of inertial confinement can be taken into account and efficient refinement strategies can be obtained.« less

Frog: Asynchronous Graph Processing on GPU with Hybrid Coloring Model

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

Shi, Xuanhua; Luo, Xuan; Liang, Junling

GPUs have been increasingly used to accelerate graph processing for complicated computational problems regarding graph theory. Many parallel graph algorithms adopt the asynchronous computing model to accelerate the iterative convergence. Unfortunately, the consistent asynchronous computing requires locking or atomic operations, leading to significant penalties/overheads when implemented on GPUs. As such, coloring algorithm is adopted to separate the vertices with potential updating conflicts, guaranteeing the consistency/correctness of the parallel processing. Common coloring algorithms, however, may suffer from low parallelism because of a large number of colors generally required for processing a large-scale graph with billions of vertices. We propose a light-weightmore » asynchronous processing framework called Frog with a preprocessing/hybrid coloring model. The fundamental idea is based on Pareto principle (or 80-20 rule) about coloring algorithms as we observed through masses of realworld graph coloring cases. We find that a majority of vertices (about 80%) are colored with only a few colors, such that they can be read and updated in a very high degree of parallelism without violating the sequential consistency. Accordingly, our solution separates the processing of the vertices based on the distribution of colors. In this work, we mainly answer three questions: (1) how to partition the vertices in a sparse graph with maximized parallelism, (2) how to process large-scale graphs that cannot fit into GPU memory, and (3) how to reduce the overhead of data transfers on PCIe while processing each partition. We conduct experiments on real-world data (Amazon, DBLP, YouTube, RoadNet-CA, WikiTalk and Twitter) to evaluate our approach and make comparisons with well-known non-preprocessed (such as Totem, Medusa, MapGraph and Gunrock) and preprocessed (Cusha) approaches, by testing four classical algorithms (BFS, PageRank, SSSP and CC). On all the tested applications and datasets, Frog is able to significantly outperform existing GPU-based graph processing systems except Gunrock and MapGraph. MapGraph gets better performance than Frog when running BFS on RoadNet-CA. The comparison between Gunrock and Frog is inconclusive. Frog can outperform Gunrock more than 1.04X when running PageRank and SSSP, while the advantage of Frog is not obvious when running BFS and CC on some datasets especially for RoadNet-CA.« less

Integral Images: Efficient Algorithms for Their Computation and Storage in Resource-Constrained Embedded Vision Systems

PubMed Central

Ehsan, Shoaib; Clark, Adrian F.; ur Rehman, Naveed; McDonald-Maier, Klaus D.

2015-01-01

The integral image, an intermediate image representation, has found extensive use in multi-scale local feature detection algorithms, such as Speeded-Up Robust Features (SURF), allowing fast computation of rectangular features at constant speed, independent of filter size. For resource-constrained real-time embedded vision systems, computation and storage of integral image presents several design challenges due to strict timing and hardware limitations. Although calculation of the integral image only consists of simple addition operations, the total number of operations is large owing to the generally large size of image data. Recursive equations allow substantial decrease in the number of operations but require calculation in a serial fashion. This paper presents two new hardware algorithms that are based on the decomposition of these recursive equations, allowing calculation of up to four integral image values in a row-parallel way without significantly increasing the number of operations. An efficient design strategy is also proposed for a parallel integral image computation unit to reduce the size of the required internal memory (nearly 35% for common HD video). Addressing the storage problem of integral image in embedded vision systems, the paper presents two algorithms which allow substantial decrease (at least 44.44%) in the memory requirements. Finally, the paper provides a case study that highlights the utility of the proposed architectures in embedded vision systems. PMID:26184211

Integral Images: Efficient Algorithms for Their Computation and Storage in Resource-Constrained Embedded Vision Systems.

PubMed

Ehsan, Shoaib; Clark, Adrian F; Naveed ur Rehman; McDonald-Maier, Klaus D

2015-07-10

The integral image, an intermediate image representation, has found extensive use in multi-scale local feature detection algorithms, such as Speeded-Up Robust Features (SURF), allowing fast computation of rectangular features at constant speed, independent of filter size. For resource-constrained real-time embedded vision systems, computation and storage of integral image presents several design challenges due to strict timing and hardware limitations. Although calculation of the integral image only consists of simple addition operations, the total number of operations is large owing to the generally large size of image data. Recursive equations allow substantial decrease in the number of operations but require calculation in a serial fashion. This paper presents two new hardware algorithms that are based on the decomposition of these recursive equations, allowing calculation of up to four integral image values in a row-parallel way without significantly increasing the number of operations. An efficient design strategy is also proposed for a parallel integral image computation unit to reduce the size of the required internal memory (nearly 35% for common HD video). Addressing the storage problem of integral image in embedded vision systems, the paper presents two algorithms which allow substantial decrease (at least 44.44%) in the memory requirements. Finally, the paper provides a case study that highlights the utility of the proposed architectures in embedded vision systems.

Parallel Adjective High-Order CFD Simulations Characterizing SOFIA Cavity Acoustics

NASA Technical Reports Server (NTRS)

Barad, Michael F.; Brehm, Christoph; Kiris, Cetin C.; Biswas, Rupak

2016-01-01

This paper presents large-scale MPI-parallel computational uid dynamics simulations for the Stratospheric Observatory for Infrared Astronomy (SOFIA). SOFIA is an airborne, 2.5-meter infrared telescope mounted in an open cavity in the aft fuselage of a Boeing 747SP. These simulations focus on how the unsteady ow eld inside and over the cavity interferes with the optical path and mounting structure of the telescope. A temporally fourth-order accurate Runge-Kutta, and spatially fth-order accurate WENO- 5Z scheme was used to perform implicit large eddy simulations. An immersed boundary method provides automated gridding for complex geometries and natural coupling to a block-structured Cartesian adaptive mesh re nement framework. Strong scaling studies using NASA's Pleiades supercomputer with up to 32k CPU cores and 4 billion compu- tational cells shows excellent scaling. Dynamic load balancing based on execution time on individual AMR blocks addresses irregular numerical cost associated with blocks con- taining boundaries. Limits to scaling beyond 32k cores are identi ed, and targeted code optimizations are discussed.

Parallel Adaptive High-Order CFD Simulations Characterizing SOFIA Cavitiy Acoustics

NASA Technical Reports Server (NTRS)

Barad, Michael F.; Brehm, Christoph; Kiris, Cetin C.; Biswas, Rupak

2015-01-01

This paper presents large-scale MPI-parallel computational uid dynamics simulations for the Stratospheric Observatory for Infrared Astronomy (SOFIA). SOFIA is an airborne, 2.5-meter infrared telescope mounted in an open cavity in the aft fuselage of a Boeing 747SP. These simulations focus on how the unsteady ow eld inside and over the cavity interferes with the optical path and mounting structure of the telescope. A tempo- rally fourth-order accurate Runge-Kutta, and a spatially fth-order accurate WENO-5Z scheme were used to perform implicit large eddy simulations. An immersed boundary method provides automated gridding for complex geometries and natural coupling to a block-structured Cartesian adaptive mesh re nement framework. Strong scaling studies using NASA's Pleiades supercomputer with up to 32k CPU cores and 4 billion compu- tational cells shows excellent scaling. Dynamic load balancing based on execution time on individual AMR blocks addresses irregular numerical cost associated with blocks con- taining boundaries. Limits to scaling beyond 32k cores are identi ed, and targeted code optimizations are discussed.

Eddy diffusivity of quasi-neutrally-buoyant inertial particles

NASA Astrophysics Data System (ADS)

Martins Afonso, Marco; Muratore-Ginanneschi, Paolo; Gama, Sílvio M. A.; Mazzino, Andrea

2018-04-01

We investigate the large-scale transport properties of quasi-neutrally-buoyant inertial particles carried by incompressible zero-mean periodic or steady ergodic flows. We show how to compute large-scale indicators such as the inertial-particle terminal velocity and eddy diffusivity from first principles in a perturbative expansion around the limit of added-mass factor close to unity. Physically, this limit corresponds to the case where the mass density of the particles is constant and close in value to the mass density of the fluid, which is also constant. Our approach differs from the usual over-damped expansion inasmuch as we do not assume a separation of time scales between thermalization and small-scale convection effects. For a general flow in the class of incompressible zero-mean periodic velocity fields, we derive closed-form cell equations for the auxiliary quantities determining the terminal velocity and effective diffusivity. In the special case of parallel flows these equations admit explicit analytic solution. We use parallel flows to show that our approach sheds light onto the behavior of terminal velocity and effective diffusivity for Stokes numbers of the order of unity.

Computational methods and software systems for dynamics and control of large space structures

NASA Technical Reports Server (NTRS)

Park, K. C.; Felippa, C. A.; Farhat, C.; Pramono, E.

1990-01-01

Two key areas of crucial importance to the computer-based simulation of large space structures are discussed. The first area involves multibody dynamics (MBD) of flexible space structures, with applications directed to deployment, construction, and maneuvering. The second area deals with advanced software systems, with emphasis on parallel processing. The latest research thrust in the second area involves massively parallel computers.

The build up of the correlation between halo spin and the large-scale structure

NASA Astrophysics Data System (ADS)

Wang, Peng; Kang, Xi

2018-01-01

Both simulations and observations have confirmed that the spin of haloes/galaxies is correlated with the large-scale structure (LSS) with a mass dependence such that the spin of low-mass haloes/galaxies tend to be parallel with the LSS, while that of massive haloes/galaxies tend to be perpendicular with the LSS. It is still unclear how this mass dependence is built up over time. We use N-body simulations to trace the evolution of the halo spin-LSS correlation and find that at early times the spin of all halo progenitors is parallel with the LSS. As time goes on, mass collapsing around massive halo is more isotropic, especially the recent mass accretion along the slowest collapsing direction is significant and it brings the halo spin to be perpendicular with the LSS. Adopting the fractional anisotropy (FA) parameter to describe the degree of anisotropy of the large-scale environment, we find that the spin-LSS correlation is a strong function of the environment such that a higher FA (more anisotropic environment) leads to an aligned signal, and a lower anisotropy leads to a misaligned signal. In general, our results show that the spin-LSS correlation is a combined consequence of mass flow and halo growth within the cosmic web. Our predicted environmental dependence between spin and large-scale structure can be further tested using galaxy surveys.

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

Chrisochoides, N.; Sukup, F.

In this paper we present a parallel implementation of the Bowyer-Watson (BW) algorithm using the task-parallel programming model. The BW algorithm constitutes an ideal mesh refinement strategy for implementing a large class of unstructured mesh generation techniques on both sequential and parallel computers, by preventing the need for global mesh refinement. Its implementation on distributed memory multicomputes using the traditional data-parallel model has been proven very inefficient due to excessive synchronization needed among processors. In this paper we demonstrate that with the task-parallel model we can tolerate synchronization costs inherent to data-parallel methods by exploring concurrency in the processor level.more » Our preliminary performance data indicate that the task- parallel approach: (i) is almost four times faster than the existing data-parallel methods, (ii) scales linearly, and (iii) introduces minimum overheads compared to the {open_quotes}best{close_quotes} sequential implementation of the BW algorithm.« less

Two degrees of freedom parallel linkageto track solarthermal platforms installed on ships

NASA Astrophysics Data System (ADS)

Visa, I.; Cotorcea, A.; Moldovan, M.; Neagoe, M.

2016-08-01

Transportation is responsible at global level for one third of the total energy consumption. Solutions to reduce conventional fuel consumption are under research, to improve the systems’ efficiency and to replace the current fossil fuels. There already are several applications, usually onsmall maritime vehicles, using photovoltaic systems to cover the electric energy demand on-board andto support the owners’ commitment towards sustainability. In most cases, these systems are fixed, parallely aligned with the deck; thus, the amount of solar energy received is heavily reduced (down to 50%) as compared to the available irradiance. Large scale, feasible applications require to maximize the energy output of the solar convertors implemented on ships; using solar tracking systems is an obvious path, allowing a gain up to 35...40% in the output energy, as compared to fixed systems. Spatial limitations, continuous movement of the ship and harsh navigation condition are the main barriers in implementation. This paper proposes a solar tracking system with two degrees of freedom, for a solar thermal platform, based on a parallel linkage with sphericaljoints, considered as Multibody System. The analytical model for mobile platform position, pressure angles and a numerical example are given in the paper.

A parallel finite element simulator for ion transport through three-dimensional ion channel systems.

PubMed

Tu, Bin; Chen, Minxin; Xie, Yan; Zhang, Linbo; Eisenberg, Bob; Lu, Benzhuo

2013-09-15

A parallel finite element simulator, ichannel, is developed for ion transport through three-dimensional ion channel systems that consist of protein and membrane. The coordinates of heavy atoms of the protein are taken from the Protein Data Bank and the membrane is represented as a slab. The simulator contains two components: a parallel adaptive finite element solver for a set of Poisson-Nernst-Planck (PNP) equations that describe the electrodiffusion process of ion transport, and a mesh generation tool chain for ion channel systems, which is an essential component for the finite element computations. The finite element method has advantages in modeling irregular geometries and complex boundary conditions. We have built a tool chain to get the surface and volume mesh for ion channel systems, which consists of a set of mesh generation tools. The adaptive finite element solver in our simulator is implemented using the parallel adaptive finite element package Parallel Hierarchical Grid (PHG) developed by one of the authors, which provides the capability of doing large scale parallel computations with high parallel efficiency and the flexibility of choosing high order elements to achieve high order accuracy. The simulator is applied to a real transmembrane protein, the gramicidin A (gA) channel protein, to calculate the electrostatic potential, ion concentrations and I - V curve, with which both primitive and transformed PNP equations are studied and their numerical performances are compared. To further validate the method, we also apply the simulator to two other ion channel systems, the voltage dependent anion channel (VDAC) and α-Hemolysin (α-HL). The simulation results agree well with Brownian dynamics (BD) simulation results and experimental results. Moreover, because ionic finite size effects can be included in PNP model now, we also perform simulations using a size-modified PNP (SMPNP) model on VDAC and α-HL. It is shown that the size effects in SMPNP can effectively lead to reduced current in the channel, and the results are closer to BD simulation results. Copyright © 2013 Wiley Periodicals, Inc.

Large-Scale Parallel Viscous Flow Computations using an Unstructured Multigrid Algorithm

NASA Technical Reports Server (NTRS)

Mavriplis, Dimitri J.

1999-01-01

The development and testing of a parallel unstructured agglomeration multigrid algorithm for steady-state aerodynamic flows is discussed. The agglomeration multigrid strategy uses a graph algorithm to construct the coarse multigrid levels from the given fine grid, similar to an algebraic multigrid approach, but operates directly on the non-linear system using the FAS (Full Approximation Scheme) approach. The scalability and convergence rate of the multigrid algorithm are examined on the SGI Origin 2000 and the Cray T3E. An argument is given which indicates that the asymptotic scalability of the multigrid algorithm should be similar to that of its underlying single grid smoothing scheme. For medium size problems involving several million grid points, near perfect scalability is obtained for the single grid algorithm, while only a slight drop-off in parallel efficiency is observed for the multigrid V- and W-cycles, using up to 128 processors on the SGI Origin 2000, and up to 512 processors on the Cray T3E. For a large problem using 25 million grid points, good scalability is observed for the multigrid algorithm using up to 1450 processors on a Cray T3E, even when the coarsest grid level contains fewer points than the total number of processors.

A Computationally Efficient Parallel Levenberg-Marquardt Algorithm for Large-Scale Big-Data Inversion

NASA Astrophysics Data System (ADS)

Lin, Y.; O'Malley, D.; Vesselinov, V. V.

2015-12-01

Inverse modeling seeks model parameters given a set of observed state variables. However, for many practical problems due to the facts that the observed data sets are often large and model parameters are often numerous, conventional methods for solving the inverse modeling can be computationally expensive. We have developed a new, computationally-efficient Levenberg-Marquardt method for solving large-scale inverse modeling. Levenberg-Marquardt methods require the solution of a dense linear system of equations which can be prohibitively expensive to compute for large-scale inverse problems. Our novel method projects the original large-scale linear problem down to a Krylov subspace, such that the dimensionality of the measurements can be significantly reduced. Furthermore, instead of solving the linear system for every Levenberg-Marquardt damping parameter, we store the Krylov subspace computed when solving the first damping parameter and recycle it for all the following damping parameters. The efficiency of our new inverse modeling algorithm is significantly improved by using these computational techniques. We apply this new inverse modeling method to invert for a random transitivity field. Our algorithm is fast enough to solve for the distributed model parameters (transitivity) at each computational node in the model domain. The inversion is also aided by the use regularization techniques. The algorithm is coded in Julia and implemented in the MADS computational framework (http://mads.lanl.gov). Julia is an advanced high-level scientific programing language that allows for efficient memory management and utilization of high-performance computational resources. By comparing with a Levenberg-Marquardt method using standard linear inversion techniques, our Levenberg-Marquardt method yields speed-up ratio of 15 in a multi-core computational environment and a speed-up ratio of 45 in a single-core computational environment. Therefore, our new inverse modeling method is a powerful tool for large-scale applications.

Building up the spin - orbit alignment of interacting galaxy pairs

NASA Astrophysics Data System (ADS)

Moon, Jun-Sung; Yoon, Suk-Jin

2018-01-01

Galaxies are not just randomly distributed throughout space. Instead, they are in alignment over a wide range of scales from the cosmic web down to a pair of galaxies. Motivated by recent findings that the spin and the orbital angular momentum vectors of galaxy pairs tend to be parallel, we here investigate the spin - orbit orientation in close pairs using the Illustris cosmological simulation. We find that since z ~ 1, the parallel alignment has become progressively stronger with time through repetitive encounters. The pair Interactions are preferentially in prograde at z = 0 (over 5 sigma significance). The prograde fraction at z = 0 is larger for the pairs influenced more heavily by each other during their evolution. We find no correlation between the spin - orbit orientation and the surrounding large-scale structure. Our results favor the scenario in which the alignment in close pairs is caused by tidal interactions later on, rather than the primordial torquing by the large-scale structures.

Optimized distributed systems achieve significant performance improvement on sorted merging of massive VCF files.

PubMed

Sun, Xiaobo; Gao, Jingjing; Jin, Peng; Eng, Celeste; Burchard, Esteban G; Beaty, Terri H; Ruczinski, Ingo; Mathias, Rasika A; Barnes, Kathleen; Wang, Fusheng; Qin, Zhaohui S

2018-06-01

Sorted merging of genomic data is a common data operation necessary in many sequencing-based studies. It involves sorting and merging genomic data from different subjects by their genomic locations. In particular, merging a large number of variant call format (VCF) files is frequently required in large-scale whole-genome sequencing or whole-exome sequencing projects. Traditional single-machine based methods become increasingly inefficient when processing large numbers of files due to the excessive computation time and Input/Output bottleneck. Distributed systems and more recent cloud-based systems offer an attractive solution. However, carefully designed and optimized workflow patterns and execution plans (schemas) are required to take full advantage of the increased computing power while overcoming bottlenecks to achieve high performance. In this study, we custom-design optimized schemas for three Apache big data platforms, Hadoop (MapReduce), HBase, and Spark, to perform sorted merging of a large number of VCF files. These schemas all adopt the divide-and-conquer strategy to split the merging job into sequential phases/stages consisting of subtasks that are conquered in an ordered, parallel, and bottleneck-free way. In two illustrating examples, we test the performance of our schemas on merging multiple VCF files into either a single TPED or a single VCF file, which are benchmarked with the traditional single/parallel multiway-merge methods, message passing interface (MPI)-based high-performance computing (HPC) implementation, and the popular VCFTools. Our experiments suggest all three schemas either deliver a significant improvement in efficiency or render much better strong and weak scalabilities over traditional methods. Our findings provide generalized scalable schemas for performing sorted merging on genetics and genomics data using these Apache distributed systems.

Optimized distributed systems achieve significant performance improvement on sorted merging of massive VCF files

PubMed Central

Gao, Jingjing; Jin, Peng; Eng, Celeste; Burchard, Esteban G; Beaty, Terri H; Ruczinski, Ingo; Mathias, Rasika A; Barnes, Kathleen; Wang, Fusheng

2018-01-01

Abstract Background Sorted merging of genomic data is a common data operation necessary in many sequencing-based studies. It involves sorting and merging genomic data from different subjects by their genomic locations. In particular, merging a large number of variant call format (VCF) files is frequently required in large-scale whole-genome sequencing or whole-exome sequencing projects. Traditional single-machine based methods become increasingly inefficient when processing large numbers of files due to the excessive computation time and Input/Output bottleneck. Distributed systems and more recent cloud-based systems offer an attractive solution. However, carefully designed and optimized workflow patterns and execution plans (schemas) are required to take full advantage of the increased computing power while overcoming bottlenecks to achieve high performance. Findings In this study, we custom-design optimized schemas for three Apache big data platforms, Hadoop (MapReduce), HBase, and Spark, to perform sorted merging of a large number of VCF files. These schemas all adopt the divide-and-conquer strategy to split the merging job into sequential phases/stages consisting of subtasks that are conquered in an ordered, parallel, and bottleneck-free way. In two illustrating examples, we test the performance of our schemas on merging multiple VCF files into either a single TPED or a single VCF file, which are benchmarked with the traditional single/parallel multiway-merge methods, message passing interface (MPI)–based high-performance computing (HPC) implementation, and the popular VCFTools. Conclusions Our experiments suggest all three schemas either deliver a significant improvement in efficiency or render much better strong and weak scalabilities over traditional methods. Our findings provide generalized scalable schemas for performing sorted merging on genetics and genomics data using these Apache distributed systems. PMID:29762754

The Computational Complexity, Parallel Scalability, and Performance of Atmospheric Data Assimilation Algorithms

NASA Technical Reports Server (NTRS)

Lyster, Peter M.; Guo, J.; Clune, T.; Larson, J. W.; Atlas, Robert (Technical Monitor)

2001-01-01

The computational complexity of algorithms for Four Dimensional Data Assimilation (4DDA) at NASA's Data Assimilation Office (DAO) is discussed. In 4DDA, observations are assimilated with the output of a dynamical model to generate best-estimates of the states of the system. It is thus a mapping problem, whereby scattered observations are converted into regular accurate maps of wind, temperature, moisture and other variables. The DAO is developing and using 4DDA algorithms that provide these datasets, or analyses, in support of Earth System Science research. Two large-scale algorithms are discussed. The first approach, the Goddard Earth Observing System Data Assimilation System (GEOS DAS), uses an atmospheric general circulation model (GCM) and an observation-space based analysis system, the Physical-space Statistical Analysis System (PSAS). GEOS DAS is very similar to global meteorological weather forecasting data assimilation systems, but is used at NASA for climate research. Systems of this size typically run at between 1 and 20 gigaflop/s. The second approach, the Kalman filter, uses a more consistent algorithm to determine the forecast error covariance matrix than does GEOS DAS. For atmospheric assimilation, the gridded dynamical fields typically have More than 10(exp 6) variables, therefore the full error covariance matrix may be in excess of a teraword. For the Kalman filter this problem can easily scale to petaflop/s proportions. We discuss the computational complexity of GEOS DAS and our implementation of the Kalman filter. We also discuss and quantify some of the technical issues and limitations in developing efficient, in terms of wall clock time, and scalable parallel implementations of the algorithms.

Structure preserving parallel algorithms for solving the Bethe–Salpeter eigenvalue problem

DOE PAGES

Shao, Meiyue; da Jornada, Felipe H.; Yang, Chao; ...

2015-10-02

The Bethe–Salpeter eigenvalue problem is a dense structured eigenvalue problem arising from discretized Bethe–Salpeter equation in the context of computing exciton energies and states. A computational challenge is that at least half of the eigenvalues and the associated eigenvectors are desired in practice. In this paper, we establish the equivalence between Bethe–Salpeter eigenvalue problems and real Hamiltonian eigenvalue problems. Based on theoretical analysis, structure preserving algorithms for a class of Bethe–Salpeter eigenvalue problems are proposed. We also show that for this class of problems all eigenvalues obtained from the Tamm–Dancoff approximation are overestimated. In order to solve large scale problemsmore » of practical interest, we discuss parallel implementations of our algorithms targeting distributed memory systems. Finally, several numerical examples are presented to demonstrate the efficiency and accuracy of our algorithms.« less