On efficiency of fire simulation realization: parallelization with greater number of computational meshes

NASA Astrophysics Data System (ADS)

Valasek, Lukas; Glasa, Jan

2017-12-01

Current fire simulation systems are capable to utilize advantages of high-performance computer (HPC) platforms available and to model fires efficiently in parallel. In this paper, efficiency of a corridor fire simulation on a HPC computer cluster is discussed. The parallel MPI version of Fire Dynamics Simulator is used for testing efficiency of selected strategies of allocation of computational resources of the cluster using a greater number of computational cores. Simulation results indicate that if the number of cores used is not equal to a multiple of the total number of cluster node cores there are allocation strategies which provide more efficient calculations.

Job Management Requirements for NAS Parallel Systems and Clusters

NASA Technical Reports Server (NTRS)

Saphir, William; Tanner, Leigh Ann; Traversat, Bernard

1995-01-01

A job management system is a critical component of a production supercomputing environment, permitting oversubscribed resources to be shared fairly and efficiently. Job management systems that were originally designed for traditional vector supercomputers are not appropriate for the distributed-memory parallel supercomputers that are becoming increasingly important in the high performance computing industry. Newer job management systems offer new functionality but do not solve fundamental problems. We address some of the main issues in resource allocation and job scheduling we have encountered on two parallel computers - a 160-node IBM SP2 and a cluster of 20 high performance workstations located at the Numerical Aerodynamic Simulation facility. We describe the requirements for resource allocation and job management that are necessary to provide a production supercomputing environment on these machines, prioritizing according to difficulty and importance, and advocating a return to fundamental issues.

Access and visualization using clusters and other parallel computers

NASA Technical Reports Server (NTRS)

Katz, Daniel S.; Bergou, Attila; Berriman, Bruce; Block, Gary; Collier, Jim; Curkendall, Dave; Good, John; Husman, Laura; Jacob, Joe; Laity, Anastasia;

Accessing and visualizing scientific spatiotemporal data

NASA Technical Reports Server (NTRS)

Katz, Daniel S.; Bergou, Attila; Berriman, G. Bruce; Block, Gary L.; Collier, Jim; Curkendall, David W.; Good, John; Husman, Laura; Jacob, Joseph C.; Laity, Anastasia;

Scaling predictive modeling in drug development with cloud computing.

PubMed

Moghadam, Behrooz Torabi; Alvarsson, Jonathan; Holm, Marcus; Eklund, Martin; Carlsson, Lars; Spjuth, Ola

2015-01-26

Growing data sets with increased time for analysis is hampering predictive modeling in drug discovery. Model building can be carried out on high-performance computer clusters, but these can be expensive to purchase and maintain. We have evaluated ligand-based modeling on cloud computing resources where computations are parallelized and run on the Amazon Elastic Cloud. We trained models on open data sets of varying sizes for the end points logP and Ames mutagenicity and compare with model building parallelized on a traditional high-performance computing cluster. We show that while high-performance computing results in faster model building, the use of cloud computing resources is feasible for large data sets and scales well within cloud instances. An additional advantage of cloud computing is that the costs of predictive models can be easily quantified, and a choice can be made between speed and economy. The easy access to computational resources with no up-front investments makes cloud computing an attractive alternative for scientists, especially for those without access to a supercomputer, and our study shows that it enables cost-efficient modeling of large data sets on demand within reasonable time.

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.

Engineering and Computing Portal to Solve Environmental Problems

NASA Astrophysics Data System (ADS)

Gudov, A. M.; Zavozkin, S. Y.; Sotnikov, I. Y.

2018-01-01

This paper describes architecture and services of the Engineering and Computing Portal, which is considered to be a complex solution that provides access to high-performance computing resources, enables to carry out computational experiments, teach parallel technologies and solve computing tasks, including technogenic safety ones.

Parallel and Preemptable Dynamically Dimensioned Search Algorithms for Single and Multi-objective Optimization in Water Resources

NASA Astrophysics Data System (ADS)

Tolson, B.; Matott, L. S.; Gaffoor, T. A.; Asadzadeh, M.; Shafii, M.; Pomorski, P.; Xu, X.; Jahanpour, M.; Razavi, S.; Haghnegahdar, A.; Craig, J. R.

2015-12-01

We introduce asynchronous parallel implementations of the Dynamically Dimensioned Search (DDS) family of algorithms including DDS, discrete DDS, PA-DDS and DDS-AU. These parallel algorithms are unique from most existing parallel optimization algorithms in the water resources field in that parallel DDS is asynchronous and does not require an entire population (set of candidate solutions) to be evaluated before generating and then sending a new candidate solution for evaluation. One key advance in this study is developing the first parallel PA-DDS multi-objective optimization algorithm. The other key advance is enhancing the computational efficiency of solving optimization problems (such as model calibration) by combining a parallel optimization algorithm with the deterministic model pre-emption concept. These two efficiency techniques can only be combined because of the asynchronous nature of parallel DDS. Model pre-emption functions to terminate simulation model runs early, prior to completely simulating the model calibration period for example, when intermediate results indicate the candidate solution is so poor that it will definitely have no influence on the generation of further candidate solutions. The computational savings of deterministic model preemption available in serial implementations of population-based algorithms (e.g., PSO) disappear in synchronous parallel implementations as these algorithms. In addition to the key advances above, we implement the algorithms across a range of computation platforms (Windows and Unix-based operating systems from multi-core desktops to a supercomputer system) and package these for future modellers within a model-independent calibration software package called Ostrich as well as MATLAB versions. Results across multiple platforms and multiple case studies (from 4 to 64 processors) demonstrate the vast improvement over serial DDS-based algorithms and highlight the important role model pre-emption plays in the performance of parallel, pre-emptable DDS algorithms. Case studies include single- and multiple-objective optimization problems in water resources model calibration and in many cases linear or near linear speedups are observed.

An efficient parallel algorithm: Poststack and prestack Kirchhoff 3D depth migration using flexi-depth iterations

NASA Astrophysics Data System (ADS)

Rastogi, Richa; Srivastava, Abhishek; Khonde, Kiran; Sirasala, Kirannmayi M.; Londhe, Ashutosh; Chavhan, Hitesh

2015-07-01

This paper presents an efficient parallel 3D Kirchhoff depth migration algorithm suitable for current class of multicore architecture. The fundamental Kirchhoff depth migration algorithm exhibits inherent parallelism however, when it comes to 3D data migration, as the data size increases the resource requirement of the algorithm also increases. This challenges its practical implementation even on current generation high performance computing systems. Therefore a smart parallelization approach is essential to handle 3D data for migration. The most compute intensive part of Kirchhoff depth migration algorithm is the calculation of traveltime tables due to its resource requirements such as memory/storage and I/O. In the current research work, we target this area and develop a competent parallel algorithm for post and prestack 3D Kirchhoff depth migration, using hybrid MPI+OpenMP programming techniques. We introduce a concept of flexi-depth iterations while depth migrating data in parallel imaging space, using optimized traveltime table computations. This concept provides flexibility to the algorithm by migrating data in a number of depth iterations, which depends upon the available node memory and the size of data to be migrated during runtime. Furthermore, it minimizes the requirements of storage, I/O and inter-node communication, thus making it advantageous over the conventional parallelization approaches. The developed parallel algorithm is demonstrated and analysed on Yuva II, a PARAM series of supercomputers. Optimization, performance and scalability experiment results along with the migration outcome show the effectiveness of the parallel algorithm.

Modelling parallel programs and multiprocessor architectures with AXE

NASA Technical Reports Server (NTRS)

Yan, Jerry C.; Fineman, Charles E.

1991-01-01

AXE, An Experimental Environment for Parallel Systems, was designed to model and simulate for parallel systems at the process level. It provides an integrated environment for specifying computation models, multiprocessor architectures, data collection, and performance visualization. AXE is being used at NASA-Ames for developing resource management strategies, parallel problem formulation, multiprocessor architectures, and operating system issues related to the High Performance Computing and Communications Program. AXE's simple, structured user-interface enables the user to model parallel programs and machines precisely and efficiently. Its quick turn-around time keeps the user interested and productive. AXE models multicomputers. The user may easily modify various architectural parameters including the number of sites, connection topologies, and overhead for operating system activities. Parallel computations in AXE are represented as collections of autonomous computing objects known as players. Their use and behavior is described. Performance data of the multiprocessor model can be observed on a color screen. These include CPU and message routing bottlenecks, and the dynamic status of the software.

HeNCE: A Heterogeneous Network Computing Environment

DOE PAGES

Beguelin, Adam; Dongarra, Jack J.; Geist, George Al; ...

1994-01-01

Network computing seeks to utilize the aggregate resources of many networked computers to solve a single problem. In so doing it is often possible to obtain supercomputer performance from an inexpensive local area network. The drawback is that network computing is complicated and error prone when done by hand, especially if the computers have different operating systems and data formats and are thus heterogeneous. The heterogeneous network computing environment (HeNCE) is an integrated graphical environment for creating and running parallel programs over a heterogeneous collection of computers. It is built on a lower level package called parallel virtual machine (PVM).more » The HeNCE philosophy of parallel programming is to have the programmer graphically specify the parallelism of a computation and to automate, as much as possible, the tasks of writing, compiling, executing, debugging, and tracing the network computation. Key to HeNCE is a graphical language based on directed graphs that describe the parallelism and data dependencies of an application. Nodes in the graphs represent conventional Fortran or C subroutines and the arcs represent data and control flow. This article describes the present state of HeNCE, its capabilities, limitations, and areas of future research.« less

Processing data communications events by awakening threads in parallel active messaging interface of a parallel computer

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

Archer, Charles J.; Blocksome, Michael A.; Ratterman, Joseph D.

Processing data communications events in a parallel active messaging interface (`PAMI`) of a parallel computer that includes compute nodes that execute a parallel application, with the PAMI including data communications endpoints, and the endpoints are coupled for data communications through the PAMI and through other data communications resources, including determining by an advance function that there are no actionable data communications events pending for its context, placing by the advance function its thread of execution into a wait state, waiting for a subsequent data communications event for the context; responsive to occurrence of a subsequent data communications event for themore » context, awakening by the thread from the wait state; and processing by the advance function the subsequent data communications event now pending for the context.« less

Design of on-board parallel computer on nano-satellite

NASA Astrophysics Data System (ADS)

You, Zheng; Tian, Hexiang; Yu, Shijie; Meng, Li

2007-11-01

This paper provides one scheme of the on-board parallel computer system designed for the Nano-satellite. Based on the development request that the Nano-satellite should have a small volume, low weight, low power cost, and intelligence, this scheme gets rid of the traditional one-computer system and dual-computer system with endeavor to improve the dependability, capability and intelligence simultaneously. According to the method of integration design, it employs the parallel computer system with shared memory as the main structure, connects the telemetric system, attitude control system, and the payload system by the intelligent bus, designs the management which can deal with the static tasks and dynamic task-scheduling, protect and recover the on-site status and so forth in light of the parallel algorithms, and establishes the fault diagnosis, restoration and system restructure mechanism. It accomplishes an on-board parallel computer system with high dependability, capability and intelligence, a flexible management on hardware resources, an excellent software system, and a high ability in extension, which satisfies with the conception and the tendency of the integration electronic design sufficiently.

Solving Coupled Gross--Pitaevskii Equations on a Cluster of PlayStation 3 Computers

NASA Astrophysics Data System (ADS)

Edwards, Mark; Heward, Jeffrey; Clark, C. W.

2009-05-01

At Georgia Southern University we have constructed an 8+1--node cluster of Sony PlayStation 3 (PS3) computers with the intention of using this computing resource to solve problems related to the behavior of ultra--cold atoms in general with a particular emphasis on studying bose--bose and bose--fermi mixtures confined in optical lattices. As a first project that uses this computing resource, we have implemented a parallel solver of the coupled time--dependent, one--dimensional Gross--Pitaevskii (TDGP) equations. These equations govern the behavior of dual-- species bosonic mixtures. We chose the split--operator/FFT to solve the coupled 1D TDGP equations. The fast Fourier transform component of this solver can be readily parallelized on the PS3 cpu known as the Cell Broadband Engine (CellBE). Each CellBE chip contains a single 64--bit PowerPC Processor Element known as the PPE and eight ``Synergistic Processor Element'' identified as the SPE's. We report on this algorithm and compare its performance to a non--parallel solver as applied to modeling evaporative cooling in dual--species bosonic mixtures.

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.

Parallel computing for automated model calibration

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

Burke, John S.; Danielson, Gary R.; Schulz, Douglas A.

2002-07-29

Natural resources model calibration is a significant burden on computing and staff resources in modeling efforts. Most assessments must consider multiple calibration objectives (for example magnitude and timing of stream flow peak). An automated calibration process that allows real time updating of data/models, allowing scientists to focus effort on improving models is needed. We are in the process of building a fully featured multi objective calibration tool capable of processing multiple models cheaply and efficiently using null cycle computing. Our parallel processing and calibration software routines have been generically, but our focus has been on natural resources model calibration. Somore » far, the natural resources models have been friendly to parallel calibration efforts in that they require no inter-process communication, only need a small amount of input data and only output a small amount of statistical information for each calibration run. A typical auto calibration run might involve running a model 10,000 times with a variety of input parameters and summary statistical output. In the past model calibration has been done against individual models for each data set. The individual model runs are relatively fast, ranging from seconds to minutes. The process was run on a single computer using a simple iterative process. We have completed two Auto Calibration prototypes and are currently designing a more feature rich tool. Our prototypes have focused on running the calibration in a distributed computing cross platform environment. They allow incorporation of?smart? calibration parameter generation (using artificial intelligence processing techniques). Null cycle computing similar to SETI@Home has also been a focus of our efforts. This paper details the design of the latest prototype and discusses our plans for the next revision of the software.« less

Parallel Optimization of 3D Cardiac Electrophysiological Model Using GPU

PubMed Central

Xia, Yong; Zhang, Henggui

2015-01-01

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

Parallel Optimization of 3D Cardiac Electrophysiological Model Using GPU.

PubMed

Xia, Yong; Wang, Kuanquan; Zhang, Henggui

2015-01-01

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

Toward an automated parallel computing environment for geosciences

NASA Astrophysics Data System (ADS)

Zhang, Huai; Liu, Mian; Shi, Yaolin; Yuen, David A.; Yan, Zhenzhen; Liang, Guoping

2007-08-01

Software for geodynamic modeling has not kept up with the fast growing computing hardware and network resources. In the past decade supercomputing power has become available to most researchers in the form of affordable Beowulf clusters and other parallel computer platforms. However, to take full advantage of such computing power requires developing parallel algorithms and associated software, a task that is often too daunting for geoscience modelers whose main expertise is in geosciences. We introduce here an automated parallel computing environment built on open-source algorithms and libraries. Users interact with this computing environment by specifying the partial differential equations, solvers, and model-specific properties using an English-like modeling language in the input files. The system then automatically generates the finite element codes that can be run on distributed or shared memory parallel machines. This system is dynamic and flexible, allowing users to address different problems in geosciences. It is capable of providing web-based services, enabling users to generate source codes online. This unique feature will facilitate high-performance computing to be integrated with distributed data grids in the emerging cyber-infrastructures for geosciences. In this paper we discuss the principles of this automated modeling environment and provide examples to demonstrate its versatility.

Fencing data transfers in a parallel active messaging interface of a parallel computer

DOEpatents

Blocksome, Michael A.; Mamidala, Amith R.

2015-06-02

Fencing data transfers in a parallel active messaging interface (`PAMI`) of a parallel computer, the PAMI including data communications endpoints, each endpoint including a specification of data communications parameters for a thread of execution on a compute node, including specifications of a client, a context, and a task; the compute nodes coupled for data communications through the PAMI and through data communications resources including at least one segment of shared random access memory; including initiating execution through the PAMI of an ordered sequence of active SEND instructions for SEND data transfers between two endpoints, effecting deterministic SEND data transfers through a segment of shared memory; and executing through the PAMI, with no FENCE accounting for SEND data transfers, an active FENCE instruction, the FENCE instruction completing execution only after completion of all SEND instructions initiated prior to execution of the FENCE instruction for SEND data transfers between the two endpoints.

Fencing data transfers in a parallel active messaging interface of a parallel computer

DOEpatents

Blocksome, Michael A.; Mamidala, Amith R.

2015-06-09

Fencing data transfers in a parallel active messaging interface (`PAMI`) of a parallel computer, the PAMI including data communications endpoints, each endpoint including a specification of data communications parameters for a thread of execution on a compute node, including specifications of a client, a context, and a task; the compute nodes coupled for data communications through the PAMI and through data communications resources including at least one segment of shared random access memory; including initiating execution through the PAMI of an ordered sequence of active SEND instructions for SEND data transfers between two endpoints, effecting deterministic SEND data transfers through a segment of shared memory; and executing through the PAMI, with no FENCE accounting for SEND data transfers, an active FENCE instruction, the FENCE instruction completing execution only after completion of all SEND instructions initiated prior to execution of the FENCE instruction for SEND data transfers between the two endpoints.

Fencing data transfers in a parallel active messaging interface of a parallel computer

DOEpatents

Blocksome, Michael A.; Mamidala, Amith R.

2015-08-11

Fencing data transfers in a parallel active messaging interface (`PAMI`) of a parallel computer, the PAMI including data communications endpoints, each endpoint comprising a specification of data communications parameters for a thread of execution on a compute node, including specifications of a client, a context, and a task, the compute nodes coupled for data communications through the PAMI and through data communications resources including a deterministic data communications network, including initiating execution through the PAMI of an ordered sequence of active SEND instructions for SEND data transfers between two endpoints, effecting deterministic SEND data transfers; and executing through the PAMI, with no FENCE accounting for SEND data transfers, an active FENCE instruction, the FENCE instruction completing execution only after completion of all SEND instructions initiated prior to execution of the FENCE instruction for SEND data transfers between the two endpoints.

Fencing data transfers in a parallel active messaging interface of a parallel computer

DOEpatents

Blocksome, Michael A.; Mamidala, Amith R.

2015-06-30

Fencing data transfers in a parallel active messaging interface (`PAMI`) of a parallel computer, the PAMI including data communications endpoints, each endpoint comprising a specification of data communications parameters for a thread of execution on a compute node, including specifications of a client, a context, and a task, the compute nodes coupled for data communications through the PAMI and through data communications resources including a deterministic data communications network, including initiating execution through the PAMI of an ordered sequence of active SEND instructions for SEND data transfers between two endpoints, effecting deterministic SEND data transfers; and executing through the PAMI, with no FENCE accounting for SEND data transfers, an active FENCE instruction, the FENCE instruction completing execution only after completion of all SEND instructions initiated prior to execution of the FENCE instruction for SEND data transfers between the two endpoints.

Dynamically allocating sets of fine-grained processors to running computations

NASA Technical Reports Server (NTRS)

Middleton, David

1988-01-01

Researchers explore an approach to using general purpose parallel computers which involves mapping hardware resources onto computations instead of mapping computations onto hardware. Problems such as processor allocation, task scheduling and load balancing, which have traditionally proven to be challenging, change significantly under this approach and may become amenable to new attacks. Researchers describe the implementation of this approach used by the FFP Machine whose computation and communication resources are repeatedly partitioned into disjoint groups that match the needs of available tasks from moment to moment. Several consequences of this system are examined.

Encapsulating model complexity and landscape-scale analyses of state-and-transition simulation models: an application of ecoinformatics and juniper encroachment in sagebrush steppe ecosystems

USGS Publications Warehouse

O'Donnell, Michael

2015-01-01

State-and-transition simulation modeling relies on knowledge of vegetation composition and structure (states) that describe community conditions, mechanistic feedbacks such as fire that can affect vegetation establishment, and ecological processes that drive community conditions as well as the transitions between these states. However, as the need for modeling larger and more complex landscapes increase, a more advanced awareness of computing resources becomes essential. The objectives of this study include identifying challenges of executing state-and-transition simulation models, identifying common bottlenecks of computing resources, developing a workflow and software that enable parallel processing of Monte Carlo simulations, and identifying the advantages and disadvantages of different computing resources. To address these objectives, this study used the ApexRMS® SyncroSim software and embarrassingly parallel tasks of Monte Carlo simulations on a single multicore computer and on distributed computing systems. The results demonstrated that state-and-transition simulation models scale best in distributed computing environments, such as high-throughput and high-performance computing, because these environments disseminate the workloads across many compute nodes, thereby supporting analysis of larger landscapes, higher spatial resolution vegetation products, and more complex models. Using a case study and five different computing environments, the top result (high-throughput computing versus serial computations) indicated an approximate 96.6% decrease of computing time. With a single, multicore compute node (bottom result), the computing time indicated an 81.8% decrease relative to using serial computations. These results provide insight into the tradeoffs of using different computing resources when research necessitates advanced integration of ecoinformatics incorporating large and complicated data inputs and models. - See more at: http://aimspress.com/aimses/ch/reader/view_abstract.aspx?file_no=Environ2015030&flag=1#sthash.p1XKDtF8.dpuf

Real science at the petascale.

PubMed

Saksena, Radhika S; Boghosian, Bruce; Fazendeiro, Luis; Kenway, Owain A; Manos, Steven; Mazzeo, Marco D; Sadiq, S Kashif; Suter, James L; Wright, David; Coveney, Peter V

2009-06-28

We describe computational science research that uses petascale resources to achieve scientific results at unprecedented scales and resolution. The applications span a wide range of domains, from investigation of fundamental problems in turbulence through computational materials science research to biomedical applications at the forefront of HIV/AIDS research and cerebrovascular haemodynamics. This work was mainly performed on the US TeraGrid 'petascale' resource, Ranger, at Texas Advanced Computing Center, in the first half of 2008 when it was the largest computing system in the world available for open scientific research. We have sought to use this petascale supercomputer optimally across application domains and scales, exploiting the excellent parallel scaling performance found on up to at least 32 768 cores for certain of our codes in the so-called 'capability computing' category as well as high-throughput intermediate-scale jobs for ensemble simulations in the 32-512 core range. Furthermore, this activity provides evidence that conventional parallel programming with MPI should be successful at the petascale in the short to medium term. We also report on the parallel performance of some of our codes on up to 65 636 cores on the IBM Blue Gene/P system at the Argonne Leadership Computing Facility, which has recently been named the fastest supercomputer in the world for open science.

Exact parallel algorithms for some members of the traveling salesman problem family

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

Pekny, J.F.

1989-01-01

The traveling salesman problem and its many generalizations comprise one of the best known combinatorial optimization problem families. Most members of the family are NP-complete problems so that exact algorithms require an unpredictable and sometimes large computational effort. Parallel computers offer hope for providing the power required to meet these demands. A major barrier to applying parallel computers is the lack of parallel algorithms. The contributions presented in this thesis center around new exact parallel algorithms for the asymmetric traveling salesman problem (ATSP), prize collecting traveling salesman problem (PCTSP), and resource constrained traveling salesman problem (RCTSP). The RCTSP is amore » particularly difficult member of the family since finding a feasible solution is an NP-complete problem. An exact sequential algorithm is also presented for the directed hamiltonian cycle problem (DHCP). The DHCP algorithm is superior to current heuristic approaches and represents the first exact method applicable to large graphs. Computational results presented for each of the algorithms demonstrates the effectiveness of combining efficient algorithms with parallel computing methods. Performance statistics are reported for randomly generated ATSPs with 7,500 cities, PCTSPs with 200 cities, RCTSPs with 200 cities, DHCPs with 3,500 vertices, and assignment problems of size 10,000. Sequential results were collected on a Sun 4/260 engineering workstation, while parallel results were collected using a 14 and 100 processor BBN Butterfly Plus computer. The computational results represent the largest instances ever solved to optimality on any type of computer.« less

Parallel-In-Time For Moving Meshes

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

Falgout, R. D.; Manteuffel, T. A.; Southworth, B.

2016-02-04

With steadily growing computational resources available, scientists must develop e ective ways to utilize the increased resources. High performance, highly parallel software has be- come a standard. However until recent years parallelism has focused primarily on the spatial domain. When solving a space-time partial di erential equation (PDE), this leads to a sequential bottleneck in the temporal dimension, particularly when taking a large number of time steps. The XBraid parallel-in-time library was developed as a practical way to add temporal parallelism to existing se- quential codes with only minor modi cations. In this work, a rezoning-type moving mesh is appliedmore » to a di usion problem and formulated in a parallel-in-time framework. Tests and scaling studies are run using XBraid and demonstrate excellent results for the simple model problem considered herein.« less

Continuous development of schemes for parallel computing of the electrostatics in biological systems: implementation in DelPhi.

PubMed

Li, Chuan; Petukh, Marharyta; Li, Lin; Alexov, Emil

2013-08-15

Due to the enormous importance of electrostatics in molecular biology, calculating the electrostatic potential and corresponding energies has become a standard computational approach for the study of biomolecules and nano-objects immersed in water and salt phase or other media. However, the electrostatics of large macromolecules and macromolecular complexes, including nano-objects, may not be obtainable via explicit methods and even the standard continuum electrostatics methods may not be applicable due to high computational time and memory requirements. Here, we report further development of the parallelization scheme reported in our previous work (Li, et al., J. Comput. Chem. 2012, 33, 1960) to include parallelization of the molecular surface and energy calculations components of the algorithm. The parallelization scheme utilizes different approaches such as space domain parallelization, algorithmic parallelization, multithreading, and task scheduling, depending on the quantity being calculated. This allows for efficient use of the computing resources of the corresponding computer cluster. The parallelization scheme is implemented in the popular software DelPhi and results in speedup of several folds. As a demonstration of the efficiency and capability of this methodology, the electrostatic potential, and electric field distributions are calculated for the bovine mitochondrial supercomplex illustrating their complex topology, which cannot be obtained by modeling the supercomplex components alone. Copyright © 2013 Wiley Periodicals, Inc.

The engine design engine. A clustered computer platform for the aerodynamic inverse design and analysis of a full engine

NASA Technical Reports Server (NTRS)

Sanz, J.; Pischel, K.; Hubler, D.

1992-01-01

An application for parallel computation on a combined cluster of powerful workstations and supercomputers was developed. A Parallel Virtual Machine (PVM) is used as message passage language on a macro-tasking parallelization of the Aerodynamic Inverse Design and Analysis for a Full Engine computer code. The heterogeneous nature of the cluster is perfectly handled by the controlling host machine. Communication is established via Ethernet with the TCP/IP protocol over an open network. A reasonable overhead is imposed for internode communication, rendering an efficient utilization of the engaged processors. Perhaps one of the most interesting features of the system is its versatile nature, that permits the usage of the computational resources available that are experiencing less use at a given point in time.

Nick Grue | NREL

Science.gov Websites

geospatial data analysis using parallel processing High performance computing Renewable resource technical potential and supply curve analysis Spatial database utilization Rapid analysis of large geospatial datasets energy and geospatial analysis products Research Interests Rapid, web-based renewable resource analysis

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.

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

Ali, Amjad Majid; Albert, Don; Andersson, Par

SLURM is an open source, fault-tolerant, and highly scalable cluster management and job scheduling system for large and small computer clusters. As a cluster resource manager, SLURM has three key functions. First, it allocates exclusive and/or non-exclusive access to resources (compute nodes) to users for some duration of time so they can perform work. Second, it provides a framework for starting, executing, and monitoring work 9normally a parallel job) on the set of allocated nodes. Finally, it arbitrates conflicting requests for resources by managing a queue of pending work.

Institutional Computing Executive Group Review of Multi-programmatic & Institutional Computing, Fiscal Year 2005 and 2006

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

Langer, S; Rotman, D; Schwegler, E

The Institutional Computing Executive Group (ICEG) review of FY05-06 Multiprogrammatic and Institutional Computing (M and IC) activities is presented in the attached report. In summary, we find that the M and IC staff does an outstanding job of acquiring and supporting a wide range of institutional computing resources to meet the programmatic and scientific goals of LLNL. The responsiveness and high quality of support given to users and the programs investing in M and IC reflects the dedication and skill of the M and IC staff. M and IC has successfully managed serial capacity, parallel capacity, and capability computing resources.more » Serial capacity computing supports a wide range of scientific projects which require access to a few high performance processors within a shared memory computer. Parallel capacity computing supports scientific projects that require a moderate number of processors (up to roughly 1000) on a parallel computer. Capability computing supports parallel jobs that push the limits of simulation science. M and IC has worked closely with Stockpile Stewardship, and together they have made LLNL a premier institution for computational and simulation science. Such a standing is vital to the continued success of laboratory science programs and to the recruitment and retention of top scientists. This report provides recommendations to build on M and IC's accomplishments and improve simulation capabilities at LLNL. We recommend that institution fully fund (1) operation of the atlas cluster purchased in FY06 to support a few large projects; (2) operation of the thunder and zeus clusters to enable 'mid-range' parallel capacity simulations during normal operation and a limited number of large simulations during dedicated application time; (3) operation of the new yana cluster to support a wide range of serial capacity simulations; (4) improvements to the reliability and performance of the Lustre parallel file system; (5) support for the new GDO petabyte-class storage facility on the green network for use in data intensive external collaborations; and (6) continued support for visualization and other methods for analyzing large simulations. We also recommend that M and IC begin planning in FY07 for the next upgrade of its parallel clusters. LLNL investments in M and IC have resulted in a world-class simulation capability leading to innovative science. We thank the LLNL management for its continued support and thank the M and IC staff for its vision and dedicated efforts to make it all happen.« less

Spatiotemporal Domain Decomposition for Massive Parallel Computation of Space-Time Kernel Density

NASA Astrophysics Data System (ADS)

Hohl, A.; Delmelle, E. M.; Tang, W.

2015-07-01

Accelerated processing capabilities are deemed critical when conducting analysis on spatiotemporal datasets of increasing size, diversity and availability. High-performance parallel computing offers the capacity to solve computationally demanding problems in a limited timeframe, but likewise poses the challenge of preventing processing inefficiency due to workload imbalance between computing resources. Therefore, when designing new algorithms capable of implementing parallel strategies, careful spatiotemporal domain decomposition is necessary to account for heterogeneity in the data. In this study, we perform octtree-based adaptive decomposition of the spatiotemporal domain for parallel computation of space-time kernel density. In order to avoid edge effects near subdomain boundaries, we establish spatiotemporal buffers to include adjacent data-points that are within the spatial and temporal kernel bandwidths. Then, we quantify computational intensity of each subdomain to balance workloads among processors. We illustrate the benefits of our methodology using a space-time epidemiological dataset of Dengue fever, an infectious vector-borne disease that poses a severe threat to communities in tropical climates. Our parallel implementation of kernel density reaches substantial speedup compared to sequential processing, and achieves high levels of workload balance among processors due to great accuracy in quantifying computational intensity. Our approach is portable of other space-time analytical tests.

IOPA: I/O-aware parallelism adaption for parallel programs

PubMed Central

Liu, Tao; Liu, Yi; Qian, Chen; Qian, Depei

2017-01-01

With the development of multi-/many-core processors, applications need to be written as parallel programs to improve execution efficiency. For data-intensive applications that use multiple threads to read/write files simultaneously, an I/O sub-system can easily become a bottleneck when too many of these types of threads exist; on the contrary, too few threads will cause insufficient resource utilization and hurt performance. Therefore, programmers must pay much attention to parallelism control to find the appropriate number of I/O threads for an application. This paper proposes a parallelism control mechanism named IOPA that can adjust the parallelism of applications to adapt to the I/O capability of a system and balance computing resources and I/O bandwidth. The programming interface of IOPA is also provided to programmers to simplify parallel programming. IOPA is evaluated using multiple applications with both solid state and hard disk drives. The results show that the parallel applications using IOPA can achieve higher efficiency than those with a fixed number of threads. PMID:28278236

IOPA: I/O-aware parallelism adaption for parallel programs.

PubMed

Liu, Tao; Liu, Yi; Qian, Chen; Qian, Depei

2017-01-01

With the development of multi-/many-core processors, applications need to be written as parallel programs to improve execution efficiency. For data-intensive applications that use multiple threads to read/write files simultaneously, an I/O sub-system can easily become a bottleneck when too many of these types of threads exist; on the contrary, too few threads will cause insufficient resource utilization and hurt performance. Therefore, programmers must pay much attention to parallelism control to find the appropriate number of I/O threads for an application. This paper proposes a parallelism control mechanism named IOPA that can adjust the parallelism of applications to adapt to the I/O capability of a system and balance computing resources and I/O bandwidth. The programming interface of IOPA is also provided to programmers to simplify parallel programming. IOPA is evaluated using multiple applications with both solid state and hard disk drives. The results show that the parallel applications using IOPA can achieve higher efficiency than those with a fixed number of threads.

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 Harmony Search Based Distributed Energy Resource Optimization

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

Ceylan, Oguzhan; Liu, Guodong; Tomsovic, Kevin

2015-01-01

This paper presents a harmony search based parallel optimization algorithm to minimize voltage deviations in three phase unbalanced electrical distribution systems and to maximize active power outputs of distributed energy resources (DR). The main contribution is to reduce the adverse impacts on voltage profile during a day as photovoltaics (PVs) output or electrical vehicles (EVs) charging changes throughout a day. The IEEE 123- bus distribution test system is modified by adding DRs and EVs under different load profiles. The simulation results show that by using parallel computing techniques, heuristic methods may be used as an alternative optimization tool in electricalmore » power distribution systems operation.« less

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

PubMed Central

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

2011-01-01

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

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

PubMed

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

2012-06-01

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

Running ATLAS workloads within massively parallel distributed applications using Athena Multi-Process framework (AthenaMP)

NASA Astrophysics Data System (ADS)

Calafiura, Paolo; Leggett, Charles; Seuster, Rolf; Tsulaia, Vakhtang; Van Gemmeren, Peter

2015-12-01

AthenaMP is a multi-process version of the ATLAS reconstruction, simulation and data analysis framework Athena. By leveraging Linux fork and copy-on-write mechanisms, it allows for sharing of memory pages between event processors running on the same compute node with little to no change in the application code. Originally targeted to optimize the memory footprint of reconstruction jobs, AthenaMP has demonstrated that it can reduce the memory usage of certain configurations of ATLAS production jobs by a factor of 2. AthenaMP has also evolved to become the parallel event-processing core of the recently developed ATLAS infrastructure for fine-grained event processing (Event Service) which allows the running of AthenaMP inside massively parallel distributed applications on hundreds of compute nodes simultaneously. We present the architecture of AthenaMP, various strategies implemented by AthenaMP for scheduling workload to worker processes (for example: Shared Event Queue and Shared Distributor of Event Tokens) and the usage of AthenaMP in the diversity of ATLAS event processing workloads on various computing resources: Grid, opportunistic resources and HPC.

Parallel Processing of Images in Mobile Devices using BOINC

NASA Astrophysics Data System (ADS)

Curiel, Mariela; Calle, David F.; Santamaría, Alfredo S.; Suarez, David F.; Flórez, Leonardo

2018-04-01

Medical image processing helps health professionals make decisions for the diagnosis and treatment of patients. Since some algorithms for processing images require substantial amounts of resources, one could take advantage of distributed or parallel computing. A mobile grid can be an adequate computing infrastructure for this problem. A mobile grid is a grid that includes mobile devices as resource providers. In a previous step of this research, we selected BOINC as the infrastructure to build our mobile grid. However, parallel processing of images in mobile devices poses at least two important challenges: the execution of standard libraries for processing images and obtaining adequate performance when compared to desktop computers grids. By the time we started our research, the use of BOINC in mobile devices also involved two issues: a) the execution of programs in mobile devices required to modify the code to insert calls to the BOINC API, and b) the division of the image among the mobile devices as well as its merging required additional code in some BOINC components. This article presents answers to these four challenges.

A Novel College Network Resource Management Method using Cloud Computing

NASA Astrophysics Data System (ADS)

Lin, Chen

At present information construction of college mainly has construction of college networks and management information system; there are many problems during the process of information. Cloud computing is development of distributed processing, parallel processing and grid computing, which make data stored on the cloud, make software and services placed in the cloud and build on top of various standards and protocols, you can get it through all kinds of equipments. This article introduces cloud computing and function of cloud computing, then analyzes the exiting problems of college network resource management, the cloud computing technology and methods are applied in the construction of college information sharing platform.

Massively parallel multicanonical simulations

NASA Astrophysics Data System (ADS)

Gross, Jonathan; Zierenberg, Johannes; Weigel, Martin; Janke, Wolfhard

2018-03-01

Generalized-ensemble Monte Carlo simulations such as the multicanonical method and similar techniques are among the most efficient approaches for simulations of systems undergoing discontinuous phase transitions or with rugged free-energy landscapes. As Markov chain methods, they are inherently serial computationally. It was demonstrated recently, however, that a combination of independent simulations that communicate weight updates at variable intervals allows for the efficient utilization of parallel computational resources for multicanonical simulations. Implementing this approach for the many-thread architecture provided by current generations of graphics processing units (GPUs), we show how it can be efficiently employed with of the order of 104 parallel walkers and beyond, thus constituting a versatile tool for Monte Carlo simulations in the era of massively parallel computing. We provide the fully documented source code for the approach applied to the paradigmatic example of the two-dimensional Ising model as starting point and reference for practitioners in the field.

Data communications in a parallel active messaging interface of a parallel computer

DOEpatents

Archer, Charles J; Blocksome, Michael A; Ratterman, Joseph D; Smith, Brian E

2015-02-03

Data communications in a parallel active messaging interface (`PAMI`) of a parallel computer, the PAMI composed of data communications endpoints, each endpoint including a specification of data communications parameters for a thread of execution on a compute node, including specifications of a client, a context, and a task, endpoints coupled for data communications through the PAMI and through data communications resources, including receiving in an origin endpoint of the PAMI a SEND instruction, the SEND instruction specifying a transmission of transfer data from the origin endpoint to a first target endpoint; transmitting from the origin endpoint to the first target endpoint a Request-To-Send (`RTS`) message advising the first target endpoint of the location and size of the transfer data; assigning by the first target endpoint to each of a plurality of target endpoints separate portions of the transfer data; and receiving by the plurality of target endpoints the transfer data.

Data communications in a parallel active messaging interface of a parallel computer

DOEpatents

Archer, Charles J; Blocksome, Michael A; Ratterman, Joseph D; Smith, Brian E

2014-11-18

Data communications in a parallel active messaging interface (`PAMI`) of a parallel computer, the PAMI composed of data communications endpoints, each endpoint including a specification of data communications parameters for a thread of execution on a compute node, including specifications of a client, a context, and a task, endpoints coupled for data communications through the PAMI and through data communications resources, including receiving in an origin endpoint of the PAMI a SEND instruction, the SEND instruction specifying a transmission of transfer data from the origin endpoint to a first target endpoint; transmitting from the origin endpoint to the first target endpoint a Request-To-Send (`RTS`) message advising the first target endpoint of the location and size of the transfer data; assigning by the first target endpoint to each of a plurality of target endpoints separate portions of the transfer data; and receiving by the plurality of target endpoints the transfer data.

Parallel algorithm for computation of second-order sequential best rotations

NASA Astrophysics Data System (ADS)

Redif, Soydan; Kasap, Server

2013-12-01

Algorithms for computing an approximate polynomial matrix eigenvalue decomposition of para-Hermitian systems have emerged as a powerful, generic signal processing tool. A technique that has shown much success in this regard is the sequential best rotation (SBR2) algorithm. Proposed is a scheme for parallelising SBR2 with a view to exploiting the modern architectural features and inherent parallelism of field-programmable gate array (FPGA) technology. Experiments show that the proposed scheme can achieve low execution times while requiring minimal FPGA resources.

Exploiting Parallel R in the Cloud with SPRINT

PubMed Central

Piotrowski, M.; McGilvary, G.A.; Sloan, T. M.; Mewissen, M.; Lloyd, A.D.; Forster, T.; Mitchell, L.; Ghazal, P.; Hill, J.

2012-01-01

Background Advances in DNA Microarray devices and next-generation massively parallel DNA sequencing platforms have led to an exponential growth in data availability but the arising opportunities require adequate computing resources. High Performance Computing (HPC) in the Cloud offers an affordable way of meeting this need. Objectives Bioconductor, a popular tool for high-throughput genomic data analysis, is distributed as add-on modules for the R statistical programming language but R has no native capabilities for exploiting multi-processor architectures. SPRINT is an R package that enables easy access to HPC for genomics researchers. This paper investigates: setting up and running SPRINT-enabled genomic analyses on Amazon’s Elastic Compute Cloud (EC2), the advantages of submitting applications to EC2 from different parts of the world and, if resource underutilization can improve application performance. Methods The SPRINT parallel implementations of correlation, permutation testing, partitioning around medoids and the multi-purpose papply have been benchmarked on data sets of various size on Amazon EC2. Jobs have been submitted from both the UK and Thailand to investigate monetary differences. Results It is possible to obtain good, scalable performance but the level of improvement is dependent upon the nature of algorithm. Resource underutilization can further improve the time to result. End-user’s location impacts on costs due to factors such as local taxation. Conclusions: Although not designed to satisfy HPC requirements, Amazon EC2 and cloud computing in general provides an interesting alternative and provides new possibilities for smaller organisations with limited funds. PMID:23223611

Exploiting parallel R in the cloud with SPRINT.

PubMed

Piotrowski, M; McGilvary, G A; Sloan, T M; Mewissen, M; Lloyd, A D; Forster, T; Mitchell, L; Ghazal, P; Hill, J

2013-01-01

Advances in DNA Microarray devices and next-generation massively parallel DNA sequencing platforms have led to an exponential growth in data availability but the arising opportunities require adequate computing resources. High Performance Computing (HPC) in the Cloud offers an affordable way of meeting this need. Bioconductor, a popular tool for high-throughput genomic data analysis, is distributed as add-on modules for the R statistical programming language but R has no native capabilities for exploiting multi-processor architectures. SPRINT is an R package that enables easy access to HPC for genomics researchers. This paper investigates: setting up and running SPRINT-enabled genomic analyses on Amazon's Elastic Compute Cloud (EC2), the advantages of submitting applications to EC2 from different parts of the world and, if resource underutilization can improve application performance. The SPRINT parallel implementations of correlation, permutation testing, partitioning around medoids and the multi-purpose papply have been benchmarked on data sets of various size on Amazon EC2. Jobs have been submitted from both the UK and Thailand to investigate monetary differences. It is possible to obtain good, scalable performance but the level of improvement is dependent upon the nature of the algorithm. Resource underutilization can further improve the time to result. End-user's location impacts on costs due to factors such as local taxation. Although not designed to satisfy HPC requirements, Amazon EC2 and cloud computing in general provides an interesting alternative and provides new possibilities for smaller organisations with limited funds.

On computational methods for crashworthiness

NASA Technical Reports Server (NTRS)

Belytschko, T.

1992-01-01

The evolution of computational methods for crashworthiness and related fields is described and linked with the decreasing cost of computational resources and with improvements in computation methodologies. The latter includes more effective time integration procedures and more efficient elements. Some recent developments in methodologies and future trends are also summarized. These include multi-time step integration (or subcycling), further improvements in elements, adaptive meshes, and the exploitation of parallel computers.

A parallel offline CFD and closed-form approximation strategy for computationally efficient analysis of complex fluid flows

NASA Astrophysics Data System (ADS)

Allphin, Devin

Computational fluid dynamics (CFD) solution approximations for complex fluid flow problems have become a common and powerful engineering analysis technique. These tools, though qualitatively useful, remain limited in practice by their underlying inverse relationship between simulation accuracy and overall computational expense. While a great volume of research has focused on remedying these issues inherent to CFD, one traditionally overlooked area of resource reduction for engineering analysis concerns the basic definition and determination of functional relationships for the studied fluid flow variables. This artificial relationship-building technique, called meta-modeling or surrogate/offline approximation, uses design of experiments (DOE) theory to efficiently approximate non-physical coupling between the variables of interest in a fluid flow analysis problem. By mathematically approximating these variables, DOE methods can effectively reduce the required quantity of CFD simulations, freeing computational resources for other analytical focuses. An idealized interpretation of a fluid flow problem can also be employed to create suitably accurate approximations of fluid flow variables for the purposes of engineering analysis. When used in parallel with a meta-modeling approximation, a closed-form approximation can provide useful feedback concerning proper construction, suitability, or even necessity of an offline approximation tool. It also provides a short-circuit pathway for further reducing the overall computational demands of a fluid flow analysis, again freeing resources for otherwise unsuitable resource expenditures. To validate these inferences, a design optimization problem was presented requiring the inexpensive estimation of aerodynamic forces applied to a valve operating on a simulated piston-cylinder heat engine. The determination of these forces was to be found using parallel surrogate and exact approximation methods, thus evidencing the comparative benefits of this technique. For the offline approximation, latin hypercube sampling (LHS) was used for design space filling across four (4) independent design variable degrees of freedom (DOF). Flow solutions at the mapped test sites were converged using STAR-CCM+ with aerodynamic forces from the CFD models then functionally approximated using Kriging interpolation. For the closed-form approximation, the problem was interpreted as an ideal 2-D converging-diverging (C-D) nozzle, where aerodynamic forces were directly mapped by application of the Euler equation solutions for isentropic compression/expansion. A cost-weighting procedure was finally established for creating model-selective discretionary logic, with a synthesized parallel simulation resource summary provided.

A Review of Enhanced Sampling Approaches for Accelerated Molecular Dynamics

NASA Astrophysics Data System (ADS)

Tiwary, Pratyush; van de Walle, Axel

Molecular dynamics (MD) simulations have become a tool of immense use and popularity for simulating a variety of systems. With the advent of massively parallel computer resources, one now routinely sees applications of MD to systems as large as hundreds of thousands to even several million atoms, which is almost the size of most nanomaterials. However, it is not yet possible to reach laboratory timescales of milliseconds and beyond with MD simulations. Due to the essentially sequential nature of time, parallel computers have been of limited use in solving this so-called timescale problem. Instead, over the years a large range of statistical mechanics based enhanced sampling approaches have been proposed for accelerating molecular dynamics, and accessing timescales that are well beyond the reach of the fastest computers. In this review we provide an overview of these approaches, including the underlying theory, typical applications, and publicly available software resources to implement them.

PP-SWAT: A phython-based computing software for efficient multiobjective callibration of SWAT

USDA-ARS?s Scientific Manuscript database

With enhanced data availability, distributed watershed models for large areas with high spatial and temporal resolution are increasingly used to understand water budgets and examine effects of human activities and climate change/variability on water resources. Developing parallel computing software...

Achieving production-level use of HEP software at the Argonne Leadership Computing Facility

NASA Astrophysics Data System (ADS)

Uram, T. D.; Childers, J. T.; LeCompte, T. J.; Papka, M. E.; Benjamin, D.

2015-12-01

HEP's demand for computing resources has grown beyond the capacity of the Grid, and these demands will accelerate with the higher energy and luminosity planned for Run II. Mira, the ten petaFLOPs supercomputer at the Argonne Leadership Computing Facility, is a potentially significant compute resource for HEP research. Through an award of fifty million hours on Mira, we have delivered millions of events to LHC experiments by establishing the means of marshaling jobs through serial stages on local clusters, and parallel stages on Mira. We are running several HEP applications, including Alpgen, Pythia, Sherpa, and Geant4. Event generators, such as Sherpa, typically have a split workload: a small scale integration phase, and a second, more scalable, event-generation phase. To accommodate this workload on Mira we have developed two Python-based Django applications, Balsam and ARGO. Balsam is a generalized scheduler interface which uses a plugin system for interacting with scheduler software such as HTCondor, Cobalt, and TORQUE. ARGO is a workflow manager that submits jobs to instances of Balsam. Through these mechanisms, the serial and parallel tasks within jobs are executed on the appropriate resources. This approach and its integration with the PanDA production system will be discussed.

Simple Linux Utility for Resource Management

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

Jette, M.

2009-09-09

SLURM is an open source, fault-tolerant, and highly scalable cluster management and job scheduling system for large and small computer clusters. As a cluster resource manager, SLURM has three key functions. First, it allocates exclusive and/or non exclusive access to resources (compute nodes) to users for some duration of time so they can perform work. Second, it provides a framework for starting, executing, and monitoring work (normally a parallel job) on the set of allciated nodes. Finally, it arbitrates conflicting requests for resouces by managing a queue of pending work.

Simulation of LHC events on a millions threads

NASA Astrophysics Data System (ADS)

Childers, J. T.; Uram, T. D.; LeCompte, T. J.; Papka, M. E.; Benjamin, D. P.

2015-12-01

Demand for Grid resources is expected to double during LHC Run II as compared to Run I; the capacity of the Grid, however, will not double. The HEP community must consider how to bridge this computing gap by targeting larger compute resources and using the available compute resources as efficiently as possible. Argonne's Mira, the fifth fastest supercomputer in the world, can run roughly five times the number of parallel processes that the ATLAS experiment typically uses on the Grid. We ported Alpgen, a serial x86 code, to run as a parallel application under MPI on the Blue Gene/Q architecture. By analysis of the Alpgen code, we reduced the memory footprint to allow running 64 threads per node, utilizing the four hardware threads available per core on the PowerPC A2 processor. Event generation and unweighting, typically run as independent serial phases, are coupled together in a single job in this scenario, reducing intermediate writes to the filesystem. By these optimizations, we have successfully run LHC proton-proton physics event generation at the scale of a million threads, filling two-thirds of Mira.

JANUS: A Compilation System for Balancing Parallelism and Performance in OpenVX

NASA Astrophysics Data System (ADS)

Omidian, Hossein; Lemieux, Guy G. F.

2018-04-01

Embedded systems typically do not have enough on-chip memory for entire an image buffer. Programming systems like OpenCV operate on entire image frames at each step, making them use excessive memory bandwidth and power. In contrast, the paradigm used by OpenVX is much more efficient; it uses image tiling, and the compilation system is allowed to analyze and optimize the operation sequence, specified as a compute graph, before doing any pixel processing. In this work, we are building a compilation system for OpenVX that can analyze and optimize the compute graph to take advantage of parallel resources in many-core systems or FPGAs. Using a database of prewritten OpenVX kernels, it automatically adjusts the image tile size as well as using kernel duplication and coalescing to meet a defined area (resource) target, or to meet a specified throughput target. This allows a single compute graph to target implementations with a wide range of performance needs or capabilities, e.g. from handheld to datacenter, that use minimal resources and power to reach the performance target.

Polyphony: A Workflow Orchestration Framework for Cloud Computing

NASA Technical Reports Server (NTRS)

Shams, Khawaja S.; Powell, Mark W.; Crockett, Tom M.; Norris, Jeffrey S.; Rossi, Ryan; Soderstrom, Tom

2010-01-01

Cloud Computing has delivered unprecedented compute capacity to NASA missions at affordable rates. Missions like the Mars Exploration Rovers (MER) and Mars Science Lab (MSL) are enjoying the elasticity that enables them to leverage hundreds, if not thousands, or machines for short durations without making any hardware procurements. In this paper, we describe Polyphony, a resilient, scalable, and modular framework that efficiently leverages a large set of computing resources to perform parallel computations. Polyphony can employ resources on the cloud, excess capacity on local machines, as well as spare resources on the supercomputing center, and it enables these resources to work in concert to accomplish a common goal. Polyphony is resilient to node failures, even if they occur in the middle of a transaction. We will conclude with an evaluation of a production-ready application built on top of Polyphony to perform image-processing operations of images from around the solar system, including Mars, Saturn, and Titan.

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.

Node Resource Manager: A Distributed Computing Software Framework Used for Solving Geophysical Problems

NASA Astrophysics Data System (ADS)

Lawry, B. J.; Encarnacao, A.; Hipp, J. R.; Chang, M.; Young, C. J.

2011-12-01

With the rapid growth of multi-core computing hardware, it is now possible for scientific researchers to run complex, computationally intensive software on affordable, in-house commodity hardware. Multi-core CPUs (Central Processing Unit) and GPUs (Graphics Processing Unit) are now commonplace in desktops and servers. Developers today have access to extremely powerful hardware that enables the execution of software that could previously only be run on expensive, massively-parallel systems. It is no longer cost-prohibitive for an institution to build a parallel computing cluster consisting of commodity multi-core servers. In recent years, our research team has developed a distributed, multi-core computing system and used it to construct global 3D earth models using seismic tomography. Traditionally, computational limitations forced certain assumptions and shortcuts in the calculation of tomographic models; however, with the recent rapid growth in computational hardware including faster CPU's, increased RAM, and the development of multi-core computers, we are now able to perform seismic tomography, 3D ray tracing and seismic event location using distributed parallel algorithms running on commodity hardware, thereby eliminating the need for many of these shortcuts. We describe Node Resource Manager (NRM), a system we developed that leverages the capabilities of a parallel computing cluster. NRM is a software-based parallel computing management framework that works in tandem with the Java Parallel Processing Framework (JPPF, http://www.jppf.org/), a third party library that provides a flexible and innovative way to take advantage of modern multi-core hardware. NRM enables multiple applications to use and share a common set of networked computers, regardless of their hardware platform or operating system. Using NRM, algorithms can be parallelized to run on multiple processing cores of a distributed computing cluster of servers and desktops, which results in a dramatic speedup in execution time. NRM is sufficiently generic to support applications in any domain, as long as the application is parallelizable (i.e., can be subdivided into multiple individual processing tasks). At present, NRM has been effective in decreasing the overall runtime of several algorithms: 1) the generation of a global 3D model of the compressional velocity distribution in the Earth using tomographic inversion, 2) the calculation of the model resolution matrix, model covariance matrix, and travel time uncertainty for the aforementioned velocity model, and 3) the correlation of waveforms with archival data on a massive scale for seismic event detection. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000.

Development of a Distributed Parallel Computing Framework to Facilitate Regional/Global Gridded Crop Modeling with Various Scenarios

NASA Astrophysics Data System (ADS)

Jang, W.; Engda, T. A.; Neff, J. C.; Herrick, J.

2017-12-01

Many crop models are increasingly used to evaluate crop yields at regional and global scales. However, implementation of these models across large areas using fine-scale grids is limited by computational time requirements. In order to facilitate global gridded crop modeling with various scenarios (i.e., different crop, management schedule, fertilizer, and irrigation) using the Environmental Policy Integrated Climate (EPIC) model, we developed a distributed parallel computing framework in Python. Our local desktop with 14 cores (28 threads) was used to test the distributed parallel computing framework in Iringa, Tanzania which has 406,839 grid cells. High-resolution soil data, SoilGrids (250 x 250 m), and climate data, AgMERRA (0.25 x 0.25 deg) were also used as input data for the gridded EPIC model. The framework includes a master file for parallel computing, input database, input data formatters, EPIC model execution, and output analyzers. Through the master file for parallel computing, the user-defined number of threads of CPU divides the EPIC simulation into jobs. Then, Using EPIC input data formatters, the raw database is formatted for EPIC input data and the formatted data moves into EPIC simulation jobs. Then, 28 EPIC jobs run simultaneously and only interesting results files are parsed and moved into output analyzers. We applied various scenarios with seven different slopes and twenty-four fertilizer ranges. Parallelized input generators create different scenarios as a list for distributed parallel computing. After all simulations are completed, parallelized output analyzers are used to analyze all outputs according to the different scenarios. This saves significant computing time and resources, making it possible to conduct gridded modeling at regional to global scales with high-resolution data. For example, serial processing for the Iringa test case would require 113 hours, while using the framework developed in this study requires only approximately 6 hours, a nearly 95% reduction in computing time.

Processing communications events in parallel active messaging interface by awakening thread from wait state

DOEpatents

Archer, Charles J; Blocksome, Michael A; Ratterman, Joseph D; Smith, Brian E

2013-10-22

Processing data communications events in a parallel active messaging interface (`PAMI`) of a parallel computer that includes compute nodes that execute a parallel application, with the PAMI including data communications endpoints, and the endpoints are coupled for data communications through the PAMI and through other data communications resources, including determining by an advance function that there are no actionable data communications events pending for its context, placing by the advance function its thread of execution into a wait state, waiting for a subsequent data communications event for the context; responsive to occurrence of a subsequent data communications event for the context, awakening by the thread from the wait state; and processing by the advance function the subsequent data communications event now pending for the context.

Creating a Parallel Version of VisIt for Microsoft Windows

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

Whitlock, B J; Biagas, K S; Rawson, P L

2011-12-07

VisIt is a popular, free interactive parallel visualization and analysis tool for scientific data. Users can quickly generate visualizations from their data, animate them through time, manipulate them, and save the resulting images or movies for presentations. VisIt was designed from the ground up to work on many scales of computers from modest desktops up to massively parallel clusters. VisIt is comprised of a set of cooperating programs. All programs can be run locally or in client/server mode in which some run locally and some run remotely on compute clusters. The VisIt program most able to harness today's computing powermore » is the VisIt compute engine. The compute engine is responsible for reading simulation data from disk, processing it, and sending results or images back to the VisIt viewer program. In a parallel environment, the compute engine runs several processes, coordinating using the Message Passing Interface (MPI) library. Each MPI process reads some subset of the scientific data and filters the data in various ways to create useful visualizations. By using MPI, VisIt has been able to scale well into the thousands of processors on large computers such as dawn and graph at LLNL. The advent of multicore CPU's has made parallelism the 'new' way to achieve increasing performance. With today's computers having at least 2 cores and in many cases up to 8 and beyond, it is more important than ever to deploy parallel software that can use that computing power not only on clusters but also on the desktop. We have created a parallel version of VisIt for Windows that uses Microsoft's MPI implementation (MSMPI) to process data in parallel on the Windows desktop as well as on a Windows HPC cluster running Microsoft Windows Server 2008. Initial desktop parallel support for Windows was deployed in VisIt 2.4.0. Windows HPC cluster support has been completed and will appear in the VisIt 2.5.0 release. We plan to continue supporting parallel VisIt on Windows so our users will be able to take full advantage of their multicore resources.« less

Study on the water resources optimal operation based on riverbed wind erosion control in West Liaohe River plain

NASA Astrophysics Data System (ADS)

Wanguang, Sun; Chengzhen, Li; Baoshan, Fan

2018-06-01

Rivers are drying up most frequently in West Liaohe River plain and the bare river beds present fine sand belts on land. These sand belts, which yield a dust heavily in windy days, stress the local environment deeply as the riverbeds are eroded by wind. The optimal operation of water resources, thus, is one of the most important methods for preventing the wind erosion of riverbeds. In this paper, optimal operation model for water resources based on riverbed wind erosion control has been established, which contains objective function, constraints, and solution method. The objective function considers factors which include water volume diverted into reservoirs, river length and lower threshold of flow rate, etc. On the basis of ensuring the water requirement of each reservoir, the destruction of the vegetation in the riverbed by the frequent river flow is avoided. The multi core parallel solving method for optimal water resources operation in the West Liaohe River Plain is proposed, which the optimal solution is found by DPSA method under the POA framework and the parallel computing program is designed in Fork/Join mode. Based on the optimal operation results, the basic rules of water resources operation in the West Liaohe River Plain are summarized. Calculation results show that, on the basis of meeting the requirement of water volume of every reservoir, the frequency of reach river flow which from Taihekou to Talagan Water Diversion Project in the Xinkai River is reduced effectively. The speedup and parallel efficiency of parallel algorithm are 1.51 and 0.76 respectively, and the computing time is significantly decreased. The research results show in this paper can provide technical support for the prevention and control of riverbed wind erosion in the West Liaohe River plain.

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.

Automated design of spacecraft systems power subsystems

NASA Technical Reports Server (NTRS)

Terrile, Richard J.; Kordon, Mark; Mandutianu, Dan; Salcedo, Jose; Wood, Eric; Hashemi, Mona

2006-01-01

This paper discusses the application of evolutionary computing to a dynamic space vehicle power subsystem resource and performance simulation in a parallel processing environment. Our objective is to demonstrate the feasibility, application and advantage of using evolutionary computation techniques for the early design search and optimization of space systems.

Parallelization of a spatial random field characterization process using the Method of Anchored Distributions and the HTCondor high throughput computing system

NASA Astrophysics Data System (ADS)

Osorio-Murillo, C. A.; Over, M. W.; Frystacky, H.; Ames, D. P.; Rubin, Y.

2013-12-01

A new software application called MAD# has been coupled with the HTCondor high throughput computing system to aid scientists and educators with the characterization of spatial random fields and enable understanding the spatial distribution of parameters used in hydrogeologic and related modeling. MAD# is an open source desktop software application used to characterize spatial random fields using direct and indirect information through Bayesian inverse modeling technique called the Method of Anchored Distributions (MAD). MAD relates indirect information with a target spatial random field via a forward simulation model. MAD# executes inverse process running the forward model multiple times to transfer information from indirect information to the target variable. MAD# uses two parallelization profiles according to computational resources available: one computer with multiple cores and multiple computers - multiple cores through HTCondor. HTCondor is a system that manages a cluster of desktop computers for submits serial or parallel jobs using scheduling policies, resources monitoring, job queuing mechanism. This poster will show how MAD# reduces the time execution of the characterization of random fields using these two parallel approaches in different case studies. A test of the approach was conducted using 1D problem with 400 cells to characterize saturated conductivity, residual water content, and shape parameters of the Mualem-van Genuchten model in four materials via the HYDRUS model. The number of simulations evaluated in the inversion was 10 million. Using the one computer approach (eight cores) were evaluated 100,000 simulations in 12 hours (10 million - 1200 hours approximately). In the evaluation on HTCondor, 32 desktop computers (132 cores) were used, with a processing time of 60 hours non-continuous in five days. HTCondor reduced the processing time for uncertainty characterization by a factor of 20 (1200 hours reduced to 60 hours.)

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

Yang, C.

Almost every computer architect dreams of achieving high system performance with low implementation costs. A multigauge machine can reconfigure its data-path width, provide parallelism, achieve better resource utilization, and sometimes can trade computational precision for increased speed. A simple experimental method is used here to capture the main characteristics of multigauging. The measurements indicate evidence of near-optimal speedups. Adapting these ideas in designing parallel processors incurs low costs and provides flexibility. Several operational aspects of designing a multigauge machine are discussed as well. Thus, this research reports the technical, economical, and operational feasibility studies of multigauging.

Parallel Monte Carlo transport modeling in the context of a time-dependent, three-dimensional multi-physics code

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

Procassini, R.J.

1997-12-31

The fine-scale, multi-space resolution that is envisioned for accurate simulations of complex weapons systems in three spatial dimensions implies flop-rate and memory-storage requirements that will only be obtained in the near future through the use of parallel computational techniques. Since the Monte Carlo transport models in these simulations usually stress both of these computational resources, they are prime candidates for parallelization. The MONACO Monte Carlo transport package, which is currently under development at LLNL, will utilize two types of parallelism within the context of a multi-physics design code: decomposition of the spatial domain across processors (spatial parallelism) and distribution ofmore » particles in a given spatial subdomain across additional processors (particle parallelism). This implementation of the package will utilize explicit data communication between domains (message passing). Such a parallel implementation of a Monte Carlo transport model will result in non-deterministic communication patterns. The communication of particles between subdomains during a Monte Carlo time step may require a significant level of effort to achieve a high parallel efficiency.« less

Parallel discontinuous Galerkin FEM for computing hyperbolic conservation law on unstructured grids

NASA Astrophysics Data System (ADS)

Ma, Xinrong; Duan, Zhijian

2018-04-01

High-order resolution Discontinuous Galerkin finite element methods (DGFEM) has been known as a good method for solving Euler equations and Navier-Stokes equations on unstructured grid, but it costs too much computational resources. An efficient parallel algorithm was presented for solving the compressible Euler equations. Moreover, the multigrid strategy based on three-stage three-order TVD Runge-Kutta scheme was used in order to improve the computational efficiency of DGFEM and accelerate the convergence of the solution of unsteady compressible Euler equations. In order to make each processor maintain load balancing, the domain decomposition method was employed. Numerical experiment performed for the inviscid transonic flow fluid problems around NACA0012 airfoil and M6 wing. The results indicated that our parallel algorithm can improve acceleration and efficiency significantly, which is suitable for calculating the complex flow fluid.

IPython: components for interactive and parallel computing across disciplines. (Invited)

NASA Astrophysics Data System (ADS)

Perez, F.; Bussonnier, M.; Frederic, J. D.; Froehle, B. M.; Granger, B. E.; Ivanov, P.; Kluyver, T.; Patterson, E.; Ragan-Kelley, B.; Sailer, Z.

2013-12-01

Scientific computing is an inherently exploratory activity that requires constantly cycling between code, data and results, each time adjusting the computations as new insights and questions arise. To support such a workflow, good interactive environments are critical. The IPython project (http://ipython.org) provides a rich architecture for interactive computing with: 1. Terminal-based and graphical interactive consoles. 2. A web-based Notebook system with support for code, text, mathematical expressions, inline plots and other rich media. 3. Easy to use, high performance tools for parallel computing. Despite its roots in Python, the IPython architecture is designed in a language-agnostic way to facilitate interactive computing in any language. This allows users to mix Python with Julia, R, Octave, Ruby, Perl, Bash and more, as well as to develop native clients in other languages that reuse the IPython clients. In this talk, I will show how IPython supports all stages in the lifecycle of a scientific idea: 1. Individual exploration. 2. Collaborative development. 3. Production runs with parallel resources. 4. Publication. 5. Education. In particular, the IPython Notebook provides an environment for "literate computing" with a tight integration of narrative and computation (including parallel computing). These Notebooks are stored in a JSON-based document format that provides an "executable paper": notebooks can be version controlled, exported to HTML or PDF for publication, and used for teaching.

An efficient dynamic load balancing algorithm

NASA Astrophysics Data System (ADS)

Lagaros, Nikos D.

2014-01-01

In engineering problems, randomness and uncertainties are inherent. Robust design procedures, formulated in the framework of multi-objective optimization, have been proposed in order to take into account sources of randomness and uncertainty. These design procedures require orders of magnitude more computational effort than conventional analysis or optimum design processes since a very large number of finite element analyses is required to be dealt. It is therefore an imperative need to exploit the capabilities of computing resources in order to deal with this kind of problems. In particular, parallel computing can be implemented at the level of metaheuristic optimization, by exploiting the physical parallelization feature of the nondominated sorting evolution strategies method, as well as at the level of repeated structural analyses required for assessing the behavioural constraints and for calculating the objective functions. In this study an efficient dynamic load balancing algorithm for optimum exploitation of available computing resources is proposed and, without loss of generality, is applied for computing the desired Pareto front. In such problems the computation of the complete Pareto front with feasible designs only, constitutes a very challenging task. The proposed algorithm achieves linear speedup factors and almost 100% speedup factor values with reference to the sequential procedure.

Sublattice parallel replica dynamics.

PubMed

Martínez, Enrique; Uberuaga, Blas P; Voter, Arthur F

2014-06-01

Exascale computing presents a challenge for the scientific community as new algorithms must be developed to take full advantage of the new computing paradigm. Atomistic simulation methods that offer full fidelity to the underlying potential, i.e., molecular dynamics (MD) and parallel replica dynamics, fail to use the whole machine speedup, leaving a region in time and sample size space that is unattainable with current algorithms. In this paper, we present an extension of the parallel replica dynamics algorithm [A. F. Voter, Phys. Rev. B 57, R13985 (1998)] by combining it with the synchronous sublattice approach of Shim and Amar [ and , Phys. Rev. B 71, 125432 (2005)], thereby exploiting event locality to improve the algorithm scalability. This algorithm is based on a domain decomposition in which events happen independently in different regions in the sample. We develop an analytical expression for the speedup given by this sublattice parallel replica dynamics algorithm and compare it with parallel MD and traditional parallel replica dynamics. We demonstrate how this algorithm, which introduces a slight additional approximation of event locality, enables the study of physical systems unreachable with traditional methodologies and promises to better utilize the resources of current high performance and future exascale computers.

High Performance Computing Based Parallel HIearchical Modal Association Clustering (HPAR HMAC)

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

Patlolla, Dilip R; Surendran Nair, Sujithkumar; Graves, Daniel A.

For many applications, clustering is a crucial step in order to gain insight into the makeup of a dataset. The best approach to a given problem often depends on a variety of factors, such as the size of the dataset, time restrictions, and soft clustering requirements. The HMAC algorithm seeks to combine the strengths of 2 particular clustering approaches: model-based and linkage-based clustering. One particular weakness of HMAC is its computational complexity. HMAC is not practical for mega-scale data clustering. For high-definition imagery, a user would have to wait months or years for a result; for a 16-megapixel image, themore » estimated runtime skyrockets to over a decade! To improve the execution time of HMAC, it is reasonable to consider an multi-core implementation that utilizes available system resources. An existing imple-mentation (Ray and Cheng 2014) divides the dataset into N partitions - one for each thread prior to executing the HMAC algorithm. This implementation benefits from 2 types of optimization: parallelization and divide-and-conquer. By running each partition in parallel, the program is able to accelerate computation by utilizing more system resources. Although the parallel implementation provides considerable improvement over the serial HMAC, it still suffers from poor computational complexity, O(N2). Once the maximum number of cores on a system is exhausted, the program exhibits slower behavior. We now consider a modification to HMAC that involves a recursive partitioning scheme. Our modification aims to exploit divide-and-conquer benefits seen by the parallel HMAC implementation. At each level in the recursion tree, partitions are divided into 2 sub-partitions until a threshold size is reached. When the partition can no longer be divided without falling below threshold size, the base HMAC algorithm is applied. This results in a significant speedup over the parallel HMAC.« less

Using Cloud Computing infrastructure with CloudBioLinux, CloudMan and Galaxy

PubMed Central

Afgan, Enis; Chapman, Brad; Jadan, Margita; Franke, Vedran; Taylor, James

2012-01-01

Cloud computing has revolutionized availability and access to computing and storage resources; making it possible to provision a large computational infrastructure with only a few clicks in a web browser. However, those resources are typically provided in the form of low-level infrastructure components that need to be procured and configured before use. In this protocol, we demonstrate how to utilize cloud computing resources to perform open-ended bioinformatics analyses, with fully automated management of the underlying cloud infrastructure. By combining three projects, CloudBioLinux, CloudMan, and Galaxy into a cohesive unit, we have enabled researchers to gain access to more than 100 preconfigured bioinformatics tools and gigabytes of reference genomes on top of the flexible cloud computing infrastructure. The protocol demonstrates how to setup the available infrastructure and how to use the tools via a graphical desktop interface, a parallel command line interface, and the web-based Galaxy interface. PMID:22700313

Using cloud computing infrastructure with CloudBioLinux, CloudMan, and Galaxy.

PubMed

Afgan, Enis; Chapman, Brad; Jadan, Margita; Franke, Vedran; Taylor, James

2012-06-01

Cloud computing has revolutionized availability and access to computing and storage resources, making it possible to provision a large computational infrastructure with only a few clicks in a Web browser. However, those resources are typically provided in the form of low-level infrastructure components that need to be procured and configured before use. In this unit, we demonstrate how to utilize cloud computing resources to perform open-ended bioinformatic analyses, with fully automated management of the underlying cloud infrastructure. By combining three projects, CloudBioLinux, CloudMan, and Galaxy, into a cohesive unit, we have enabled researchers to gain access to more than 100 preconfigured bioinformatics tools and gigabytes of reference genomes on top of the flexible cloud computing infrastructure. The protocol demonstrates how to set up the available infrastructure and how to use the tools via a graphical desktop interface, a parallel command-line interface, and the Web-based Galaxy interface.

West Virginia US Department of Energy experimental program to stimulate competitive research. Section 2: Human resource development; Section 3: Carbon-based structural materials research cluster; Section 3: Data parallel algorithms for scientific computing

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

Not Available

1994-02-02

This report consists of three separate but related reports. They are (1) Human Resource Development, (2) Carbon-based Structural Materials Research Cluster, and (3) Data Parallel Algorithms for Scientific Computing. To meet the objectives of the Human Resource Development plan, the plan includes K--12 enrichment activities, undergraduate research opportunities for students at the state`s two Historically Black Colleges and Universities, graduate research through cluster assistantships and through a traineeship program targeted specifically to minorities, women and the disabled, and faculty development through participation in research clusters. One research cluster is the chemistry and physics of carbon-based materials. The objective of thismore » cluster is to develop a self-sustaining group of researchers in carbon-based materials research within the institutions of higher education in the state of West Virginia. The projects will involve analysis of cokes, graphites and other carbons in order to understand the properties that provide desirable structural characteristics including resistance to oxidation, levels of anisotropy and structural characteristics of the carbons themselves. In the proposed cluster on parallel algorithms, research by four WVU faculty and three state liberal arts college faculty are: (1) modeling of self-organized critical systems by cellular automata; (2) multiprefix algorithms and fat-free embeddings; (3) offline and online partitioning of data computation; and (4) manipulating and rendering three dimensional objects. This cluster furthers the state Experimental Program to Stimulate Competitive Research plan by building on existing strengths at WVU in parallel algorithms.« less

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.

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.

Development of a SaaS application probe to the physical properties of the Earth's interior: An attempt at moving HPC to the cloud

NASA Astrophysics Data System (ADS)

Huang, Qian

2014-09-01

Scientific computing often requires the availability of a massive number of computers for performing large-scale simulations, and computing in mineral physics is no exception. In order to investigate physical properties of minerals at extreme conditions in computational mineral physics, parallel computing technology is used to speed up the performance by utilizing multiple computer resources to process a computational task simultaneously thereby greatly reducing computation time. Traditionally, parallel computing has been addressed by using High Performance Computing (HPC) solutions and installed facilities such as clusters and super computers. Today, it has been seen that there is a tremendous growth in cloud computing. Infrastructure as a Service (IaaS), the on-demand and pay-as-you-go model, creates a flexible and cost-effective mean to access computing resources. In this paper, a feasibility report of HPC on a cloud infrastructure is presented. It is found that current cloud services in IaaS layer still need to improve performance to be useful to research projects. On the other hand, Software as a Service (SaaS), another type of cloud computing, is introduced into an HPC system for computing in mineral physics, and an application of which is developed. In this paper, an overall description of this SaaS application is presented. This contribution can promote cloud application development in computational mineral physics, and cross-disciplinary studies.

Fluid/Structure Interaction Studies of Aircraft Using High Fidelity Equations on Parallel Computers

NASA Technical Reports Server (NTRS)

Guruswamy, Guru; VanDalsem, William (Technical Monitor)

1994-01-01

Abstract Aeroelasticity which involves strong coupling of fluids, structures and controls is an important element in designing an aircraft. Computational aeroelasticity using low fidelity methods such as the linear aerodynamic flow equations coupled with the modal structural equations are well advanced. Though these low fidelity approaches are computationally less intensive, they are not adequate for the analysis of modern aircraft such as High Speed Civil Transport (HSCT) and Advanced Subsonic Transport (AST) which can experience complex flow/structure interactions. HSCT can experience vortex induced aeroelastic oscillations whereas AST can experience transonic buffet associated structural oscillations. Both aircraft may experience a dip in the flutter speed at the transonic regime. For accurate aeroelastic computations at these complex fluid/structure interaction situations, high fidelity equations such as the Navier-Stokes for fluids and the finite-elements for structures are needed. Computations using these high fidelity equations require large computational resources both in memory and speed. Current conventional super computers have reached their limitations both in memory and speed. As a result, parallel computers have evolved to overcome the limitations of conventional computers. This paper will address the transition that is taking place in computational aeroelasticity from conventional computers to parallel computers. The paper will address special techniques needed to take advantage of the architecture of new parallel computers. Results will be illustrated from computations made on iPSC/860 and IBM SP2 computer by using ENSAERO code that directly couples the Euler/Navier-Stokes flow equations with high resolution finite-element structural equations.

Evolutionary computing for the design search and optimization of space vehicle power subsystems

NASA Technical Reports Server (NTRS)

Kordon, M.; Klimeck, G.; Hanks, D.

2004-01-01

Evolutionary computing has proven to be a straightforward and robust approach for optimizing a wide range of difficult analysis and design problems. This paper discusses the application of these techniques to an existing space vehicle power subsystem resource and performance analysis simulation in a parallel processing environment.

Evaluating local indirect addressing in SIMD proc essors

NASA Technical Reports Server (NTRS)

Middleton, David; Tomboulian, Sherryl

1989-01-01

In the design of parallel computers, there exists a tradeoff between the number and power of individual processors. The single instruction stream, multiple data stream (SIMD) model of parallel computers lies at one extreme of the resulting spectrum. The available hardware resources are devoted to creating the largest possible number of processors, and consequently each individual processor must use the fewest possible resources. Disagreement exists as to whether SIMD processors should be able to generate addresses individually into their local data memory, or all processors should access the same address. The tradeoff is examined between the increased capability and the reduced number of processors that occurs in this single instruction stream, multiple, locally addressed, data (SIMLAD) model. Factors are assembled that affect this design choice, and the SIMLAD model is compared with the bare SIMD and the MIMD models.

Real-time simulation of a spiking neural network model of the basal ganglia circuitry using general purpose computing on graphics processing units.

PubMed

Igarashi, Jun; Shouno, Osamu; Fukai, Tomoki; Tsujino, Hiroshi

2011-11-01

Real-time simulation of a biologically realistic spiking neural network is necessary for evaluation of its capacity to interact with real environments. However, the real-time simulation of such a neural network is difficult due to its high computational costs that arise from two factors: (1) vast network size and (2) the complicated dynamics of biologically realistic neurons. In order to address these problems, mainly the latter, we chose to use general purpose computing on graphics processing units (GPGPUs) for simulation of such a neural network, taking advantage of the powerful computational capability of a graphics processing unit (GPU). As a target for real-time simulation, we used a model of the basal ganglia that has been developed according to electrophysiological and anatomical knowledge. The model consists of heterogeneous populations of 370 spiking model neurons, including computationally heavy conductance-based models, connected by 11,002 synapses. Simulation of the model has not yet been performed in real-time using a general computing server. By parallelization of the model on the NVIDIA Geforce GTX 280 GPU in data-parallel and task-parallel fashion, faster-than-real-time simulation was robustly realized with only one-third of the GPU's total computational resources. Furthermore, we used the GPU's full computational resources to perform faster-than-real-time simulation of three instances of the basal ganglia model; these instances consisted of 1100 neurons and 33,006 synapses and were synchronized at each calculation step. Finally, we developed software for simultaneous visualization of faster-than-real-time simulation output. These results suggest the potential power of GPGPU techniques in real-time simulation of realistic neural networks. Copyright © 2011 Elsevier Ltd. All rights reserved.

High-Performance Compute Infrastructure in Astronomy: 2020 Is Only Months Away

NASA Astrophysics Data System (ADS)

Berriman, B.; Deelman, E.; Juve, G.; Rynge, M.; Vöckler, J. S.

2012-09-01

By 2020, astronomy will be awash with as much as 60 PB of public data. Full scientific exploitation of such massive volumes of data will require high-performance computing on server farms co-located with the data. Development of this computing model will be a community-wide enterprise that has profound cultural and technical implications. Astronomers must be prepared to develop environment-agnostic applications that support parallel processing. The community must investigate the applicability and cost-benefit of emerging technologies such as cloud computing to astronomy, and must engage the Computer Science community to develop science-driven cyberinfrastructure such as workflow schedulers and optimizers. We report here the results of collaborations between a science center, IPAC, and a Computer Science research institute, ISI. These collaborations may be considered pathfinders in developing a high-performance compute infrastructure in astronomy. These collaborations investigated two exemplar large-scale science-driver workflow applications: 1) Calculation of an infrared atlas of the Galactic Plane at 18 different wavelengths by placing data from multiple surveys on a common plate scale and co-registering all the pixels; 2) Calculation of an atlas of periodicities present in the public Kepler data sets, which currently contain 380,000 light curves. These products have been generated with two workflow applications, written in C for performance and designed to support parallel processing on multiple environments and platforms, but with different compute resource needs: the Montage image mosaic engine is I/O-bound, and the NASA Star and Exoplanet Database periodogram code is CPU-bound. Our presentation will report cost and performance metrics and lessons-learned for continuing development. Applicability of Cloud Computing: Commercial Cloud providers generally charge for all operations, including processing, transfer of input and output data, and for storage of data, and so the costs of running applications vary widely according to how they use resources. The cloud is well suited to processing CPU-bound (and memory bound) workflows such as the periodogram code, given the relatively low cost of processing in comparison with I/O operations. I/O-bound applications such as Montage perform best on high-performance clusters with fast networks and parallel file-systems. Science-driven Cyberinfrastructure: Montage has been widely used as a driver application to develop workflow management services, such as task scheduling in distributed environments, designing fault tolerance techniques for job schedulers, and developing workflow orchestration techniques. Running Parallel Applications Across Distributed Cloud Environments: Data processing will eventually take place in parallel distributed across cyber infrastructure environments having different architectures. We have used the Pegasus Work Management System (WMS) to successfully run applications across three very different environments: TeraGrid, OSG (Open Science Grid), and FutureGrid. Provisioning resources across different grids and clouds (also referred to as Sky Computing), involves establishing a distributed environment, where issues of, e.g, remote job submission, data management, and security need to be addressed. This environment also requires building virtual machine images that can run in different environments. Usually, each cloud provides basic images that can be customized with additional software and services. In most of our work, we provisioned compute resources using a custom application, called Wrangler. Pegasus WMS abstracts the architectures of the compute environments away from the end-user, and can be considered a first-generation tool suitable for scientists to run their applications on disparate environments.

Parallel-hierarchical processing and classification of laser beam profile images based on the GPU-oriented architecture

NASA Astrophysics Data System (ADS)

Yarovyi, Andrii A.; Timchenko, Leonid I.; Kozhemiako, Volodymyr P.; Kokriatskaia, Nataliya I.; Hamdi, Rami R.; Savchuk, Tamara O.; Kulyk, Oleksandr O.; Surtel, Wojciech; Amirgaliyev, Yedilkhan; Kashaganova, Gulzhan

2017-08-01

The paper deals with a problem of insufficient productivity of existing computer means for large image processing, which do not meet modern requirements posed by resource-intensive computing tasks of laser beam profiling. The research concentrated on one of the profiling problems, namely, real-time processing of spot images of the laser beam profile. Development of a theory of parallel-hierarchic transformation allowed to produce models for high-performance parallel-hierarchical processes, as well as algorithms and software for their implementation based on the GPU-oriented architecture using GPGPU technologies. The analyzed performance of suggested computerized tools for processing and classification of laser beam profile images allows to perform real-time processing of dynamic images of various sizes.

Molecular simulation workflows as parallel algorithms: the execution engine of Copernicus, a distributed high-performance computing platform.

PubMed

Pronk, Sander; Pouya, Iman; Lundborg, Magnus; Rotskoff, Grant; Wesén, Björn; Kasson, Peter M; Lindahl, Erik

2015-06-09

Computational chemistry and other simulation fields are critically dependent on computing resources, but few problems scale efficiently to the hundreds of thousands of processors available in current supercomputers-particularly for molecular dynamics. This has turned into a bottleneck as new hardware generations primarily provide more processing units rather than making individual units much faster, which simulation applications are addressing by increasingly focusing on sampling with algorithms such as free-energy perturbation, Markov state modeling, metadynamics, or milestoning. All these rely on combining results from multiple simulations into a single observation. They are potentially powerful approaches that aim to predict experimental observables directly, but this comes at the expense of added complexity in selecting sampling strategies and keeping track of dozens to thousands of simulations and their dependencies. Here, we describe how the distributed execution framework Copernicus allows the expression of such algorithms in generic workflows: dataflow programs. Because dataflow algorithms explicitly state dependencies of each constituent part, algorithms only need to be described on conceptual level, after which the execution is maximally parallel. The fully automated execution facilitates the optimization of these algorithms with adaptive sampling, where undersampled regions are automatically detected and targeted without user intervention. We show how several such algorithms can be formulated for computational chemistry problems, and how they are executed efficiently with many loosely coupled simulations using either distributed or parallel resources with Copernicus.

Adapting the serial Alpgen parton-interaction generator to simulate LHC collisions on millions of parallel threads

NASA Astrophysics Data System (ADS)

Childers, J. T.; Uram, T. D.; LeCompte, T. J.; Papka, M. E.; Benjamin, D. P.

2017-01-01

As the LHC moves to higher energies and luminosity, the demand for computing resources increases accordingly and will soon outpace the growth of the Worldwide LHC Computing Grid. To meet this greater demand, event generation Monte Carlo was targeted for adaptation to run on Mira, the supercomputer at the Argonne Leadership Computing Facility. Alpgen is a Monte Carlo event generation application that is used by LHC experiments in the simulation of collisions that take place in the Large Hadron Collider. This paper details the process by which Alpgen was adapted from a single-processor serial-application to a large-scale parallel-application and the performance that was achieved.

VLSI neuroprocessors

NASA Technical Reports Server (NTRS)

Kemeny, Sabrina E.

1994-01-01

Electronic and optoelectronic hardware implementations of highly parallel computing architectures address several ill-defined and/or computation-intensive problems not easily solved by conventional computing techniques. The concurrent processing architectures developed are derived from a variety of advanced computing paradigms including neural network models, fuzzy logic, and cellular automata. Hardware implementation technologies range from state-of-the-art digital/analog custom-VLSI to advanced optoelectronic devices such as computer-generated holograms and e-beam fabricated Dammann gratings. JPL's concurrent processing devices group has developed a broad technology base in hardware implementable parallel algorithms, low-power and high-speed VLSI designs and building block VLSI chips, leading to application-specific high-performance embeddable processors. Application areas include high throughput map-data classification using feedforward neural networks, terrain based tactical movement planner using cellular automata, resource optimization (weapon-target assignment) using a multidimensional feedback network with lateral inhibition, and classification of rocks using an inner-product scheme on thematic mapper data. In addition to addressing specific functional needs of DOD and NASA, the JPL-developed concurrent processing device technology is also being customized for a variety of commercial applications (in collaboration with industrial partners), and is being transferred to U.S. industries. This viewgraph p resentation focuses on two application-specific processors which solve the computation intensive tasks of resource allocation (weapon-target assignment) and terrain based tactical movement planning using two extremely different topologies. Resource allocation is implemented as an asynchronous analog competitive assignment architecture inspired by the Hopfield network. Hardware realization leads to a two to four order of magnitude speed-up over conventional techniques and enables multiple assignments, (many to many), not achievable with standard statistical approaches. Tactical movement planning (finding the best path from A to B) is accomplished with a digital two-dimensional concurrent processor array. By exploiting the natural parallel decomposition of the problem in silicon, a four order of magnitude speed-up over optimized software approaches has been demonstrated.

Fast data reconstructed method of Fourier transform imaging spectrometer based on multi-core CPU

NASA Astrophysics Data System (ADS)

Yu, Chunchao; Du, Debiao; Xia, Zongze; Song, Li; Zheng, Weijian; Yan, Min; Lei, Zhenggang

2017-10-01

Imaging spectrometer can gain two-dimensional space image and one-dimensional spectrum at the same time, which shows high utility in color and spectral measurements, the true color image synthesis, military reconnaissance and so on. In order to realize the fast reconstructed processing of the Fourier transform imaging spectrometer data, the paper designed the optimization reconstructed algorithm with OpenMP parallel calculating technology, which was further used for the optimization process for the HyperSpectral Imager of `HJ-1' Chinese satellite. The results show that the method based on multi-core parallel computing technology can control the multi-core CPU hardware resources competently and significantly enhance the calculation of the spectrum reconstruction processing efficiency. If the technology is applied to more cores workstation in parallel computing, it will be possible to complete Fourier transform imaging spectrometer real-time data processing with a single computer.

Precision Parameter Estimation and Machine Learning

NASA Astrophysics Data System (ADS)

Wandelt, Benjamin D.

2008-12-01

I discuss the strategy of ``Acceleration by Parallel Precomputation and Learning'' (AP-PLe) that can vastly accelerate parameter estimation in high-dimensional parameter spaces and costly likelihood functions, using trivially parallel computing to speed up sequential exploration of parameter space. This strategy combines the power of distributed computing with machine learning and Markov-Chain Monte Carlo techniques efficiently to explore a likelihood function, posterior distribution or χ2-surface. This strategy is particularly successful in cases where computing the likelihood is costly and the number of parameters is moderate or large. We apply this technique to two central problems in cosmology: the solution of the cosmological parameter estimation problem with sufficient accuracy for the Planck data using PICo; and the detailed calculation of cosmological helium and hydrogen recombination with RICO. Since the APPLe approach is designed to be able to use massively parallel resources to speed up problems that are inherently serial, we can bring the power of distributed computing to bear on parameter estimation problems. We have demonstrated this with the CosmologyatHome project.

The application of the large particles method of numerical modeling of the process of carbonic nanostructures synthesis in plasma

NASA Astrophysics Data System (ADS)

Abramov, G. V.; Gavrilov, A. N.

2018-03-01

The article deals with the numerical solution of the mathematical model of the particles motion and interaction in multicomponent plasma by the example of electric arc synthesis of carbon nanostructures. The high order of the particles and the number of their interactions requires a significant input of machine resources and time for calculations. Application of the large particles method makes it possible to reduce the amount of computation and the requirements for hardware resources without affecting the accuracy of numerical calculations. The use of technology of GPGPU parallel computing using the Nvidia CUDA technology allows organizing all General purpose computation on the basis of the graphical processor graphics card. The comparative analysis of different approaches to parallelization of computations to speed up calculations with the choice of the algorithm in which to calculate the accuracy of the solution shared memory is used. Numerical study of the influence of particles density in the macro particle on the motion parameters and the total number of particle collisions in the plasma for different modes of synthesis has been carried out. The rational range of the coherence coefficient of particle in the macro particle is computed.

Analog Processor To Solve Optimization Problems

NASA Technical Reports Server (NTRS)

Duong, Tuan A.; Eberhardt, Silvio P.; Thakoor, Anil P.

1993-01-01

Proposed analog processor solves "traveling-salesman" problem, considered paradigm of global-optimization problems involving routing or allocation of resources. Includes electronic neural network and auxiliary circuitry based partly on concepts described in "Neural-Network Processor Would Allocate Resources" (NPO-17781) and "Neural Network Solves 'Traveling-Salesman' Problem" (NPO-17807). Processor based on highly parallel computing solves problem in significantly less time.

Role of HPC in Advancing Computational Aeroelasticity

NASA Technical Reports Server (NTRS)

Guruswamy, Guru P.

2004-01-01

On behalf of the High Performance Computing and Modernization Program (HPCMP) and NASA Advanced Supercomputing Division (NAS) a study is conducted to assess the role of supercomputers on computational aeroelasticity of aerospace vehicles. The study is mostly based on the responses to a web based questionnaire that was designed to capture the nuances of high performance computational aeroelasticity, particularly on parallel computers. A procedure is presented to assign a fidelity-complexity index to each application. Case studies based on major applications using HPCMP resources are presented.

P-HS-SFM: a parallel harmony search algorithm for the reproduction of experimental data in the continuous microscopic crowd dynamic models

NASA Astrophysics Data System (ADS)

Jaber, Khalid Mohammad; Alia, Osama Moh'd.; Shuaib, Mohammed Mahmod

2018-03-01

Finding the optimal parameters that can reproduce experimental data (such as the velocity-density relation and the specific flow rate) is a very important component of the validation and calibration of microscopic crowd dynamic models. Heavy computational demand during parameter search is a known limitation that exists in a previously developed model known as the Harmony Search-Based Social Force Model (HS-SFM). In this paper, a parallel-based mechanism is proposed to reduce the computational time and memory resource utilisation required to find these parameters. More specifically, two MATLAB-based multicore techniques (parfor and create independent jobs) using shared memory are developed by taking advantage of the multithreading capabilities of parallel computing, resulting in a new framework called the Parallel Harmony Search-Based Social Force Model (P-HS-SFM). The experimental results show that the parfor-based P-HS-SFM achieved a better computational time of about 26 h, an efficiency improvement of ? 54% and a speedup factor of 2.196 times in comparison with the HS-SFM sequential processor. The performance of the P-HS-SFM using the create independent jobs approach is also comparable to parfor with a computational time of 26.8 h, an efficiency improvement of about 30% and a speedup of 2.137 times.

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

Shipman, Galen M.

These are the slides for a presentation on programming models in HPC, at the Los Alamos National Laboratory's Parallel Computing Summer School. The following topics are covered: Flynn's Taxonomy of computer architectures; single instruction single data; single instruction multiple data; multiple instruction multiple data; address space organization; definition of Trinity (Intel Xeon-Phi is a MIMD architecture); single program multiple data; multiple program multiple data; ExMatEx workflow overview; definition of a programming model, programming languages, runtime systems; programming model and environments; MPI (Message Passing Interface); OpenMP; Kokkos (Performance Portable Thread-Parallel Programming Model); Kokkos abstractions, patterns, policies, and spaces; RAJA, a systematicmore » approach to node-level portability and tuning; overview of the Legion Programming Model; mapping tasks and data to hardware resources; interoperability: supporting task-level models; Legion S3D execution and performance details; workflow, integration of external resources into the programming model.« less

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.

A quantitative assessment of the Hadoop framework for analyzing massively parallel DNA sequencing data.

PubMed

Siretskiy, Alexey; Sundqvist, Tore; Voznesenskiy, Mikhail; Spjuth, Ola

2015-01-01

New high-throughput technologies, such as massively parallel sequencing, have transformed the life sciences into a data-intensive field. The most common e-infrastructure for analyzing this data consists of batch systems that are based on high-performance computing resources; however, the bioinformatics software that is built on this platform does not scale well in the general case. Recently, the Hadoop platform has emerged as an interesting option to address the challenges of increasingly large datasets with distributed storage, distributed processing, built-in data locality, fault tolerance, and an appealing programming methodology. In this work we introduce metrics and report on a quantitative comparison between Hadoop and a single node of conventional high-performance computing resources for the tasks of short read mapping and variant calling. We calculate efficiency as a function of data size and observe that the Hadoop platform is more efficient for biologically relevant data sizes in terms of computing hours for both split and un-split data files. We also quantify the advantages of the data locality provided by Hadoop for NGS problems, and show that a classical architecture with network-attached storage will not scale when computing resources increase in numbers. Measurements were performed using ten datasets of different sizes, up to 100 gigabases, using the pipeline implemented in Crossbow. To make a fair comparison, we implemented an improved preprocessor for Hadoop with better performance for splittable data files. For improved usability, we implemented a graphical user interface for Crossbow in a private cloud environment using the CloudGene platform. All of the code and data in this study are freely available as open source in public repositories. From our experiments we can conclude that the improved Hadoop pipeline scales better than the same pipeline on high-performance computing resources, we also conclude that Hadoop is an economically viable option for the common data sizes that are currently used in massively parallel sequencing. Given that datasets are expected to increase over time, Hadoop is a framework that we envision will have an increasingly important role in future biological data analysis.

Adapting the serial Alpgen parton-interaction generator to simulate LHC collisions on millions of parallel threads

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

Childers, J. T.; Uram, T. D.; LeCompte, T. J.

As the LHC moves to higher energies and luminosity, the demand for computing resources increases accordingly and will soon outpace the growth of the World- wide LHC Computing Grid. To meet this greater demand, event generation Monte Carlo was targeted for adaptation to run on Mira, the supercomputer at the Argonne Leadership Computing Facility. Alpgen is a Monte Carlo event generation application that is used by LHC experiments in the simulation of collisions that take place in the Large Hadron Collider. This paper details the process by which Alpgen was adapted from a single-processor serial-application to a large-scale parallel-application andmore » the performance that was achieved.« less

Adapting the serial Alpgen parton-interaction generator to simulate LHC collisions on millions of parallel threads

DOE PAGES

Childers, J. T.; Uram, T. D.; LeCompte, T. J.; ...

2016-09-29

As the LHC moves to higher energies and luminosity, the demand for computing resources increases accordingly and will soon outpace the growth of the Worldwide LHC Computing Grid. To meet this greater demand, event generation Monte Carlo was targeted for adaptation to run on Mira, the supercomputer at the Argonne Leadership Computing Facility. Alpgen is a Monte Carlo event generation application that is used by LHC experiments in the simulation of collisions that take place in the Large Hadron Collider. Finally, this paper details the process by which Alpgen was adapted from a single-processor serial-application to a large-scale parallel-application andmore » the performance that was achieved.« less

Adapting the serial Alpgen parton-interaction generator to simulate LHC collisions on millions of parallel threads

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

Childers, J. T.; Uram, T. D.; LeCompte, T. J.

As the LHC moves to higher energies and luminosity, the demand for computing resources increases accordingly and will soon outpace the growth of the Worldwide LHC Computing Grid. To meet this greater demand, event generation Monte Carlo was targeted for adaptation to run on Mira, the supercomputer at the Argonne Leadership Computing Facility. Alpgen is a Monte Carlo event generation application that is used by LHC experiments in the simulation of collisions that take place in the Large Hadron Collider. Finally, this paper details the process by which Alpgen was adapted from a single-processor serial-application to a large-scale parallel-application andmore » the performance that was achieved.« less

A grid-enabled web service for low-resolution crystal structure refinement.

PubMed

O'Donovan, Daniel J; Stokes-Rees, Ian; Nam, Yunsun; Blacklow, Stephen C; Schröder, Gunnar F; Brunger, Axel T; Sliz, Piotr

2012-03-01

Deformable elastic network (DEN) restraints have proved to be a powerful tool for refining structures from low-resolution X-ray crystallographic data sets. Unfortunately, optimal refinement using DEN restraints requires extensive calculations and is often hindered by a lack of access to sufficient computational resources. The DEN web service presented here intends to provide structural biologists with access to resources for running computationally intensive DEN refinements in parallel on the Open Science Grid, the US cyberinfrastructure. Access to the grid is provided through a simple and intuitive web interface integrated into the SBGrid Science Portal. Using this portal, refinements combined with full parameter optimization that would take many thousands of hours on standard computational resources can now be completed in several hours. An example of the successful application of DEN restraints to the human Notch1 transcriptional complex using the grid resource, and summaries of all submitted refinements, are presented as justification.

CFD in design - A government perspective

NASA Technical Reports Server (NTRS)

Kutler, Paul; Gross, Anthony R.

1989-01-01

Some of the research programs involving the use of CFD in the aerodynamic design process at government laboratories around the United States are presented. Technology transfer issues and future directions in the discipline or CFD are addressed. The major challengers in the aerosciences as well as other disciplines that will require high-performance computing resources such as massively parallel computers are examined.

Efficient Parallel Algorithms on Restartable Fail-Stop Processors

DTIC Science & Technology

1991-01-01

resource (memory), and ( 3 ) that processors, memory and their interconnection must be The model of parallel computation known as the Par- perfectly...setting), arid ure an(I restart errors. We describe these arguments if] [AAtPS 871 (in a deterministic setting). Fault-tolerance Section 3 . of...grannmarity at the processor level --- for recent work on where Al is the nmber of failures during this step’s gate granilarities see [All 90, Pip 85

Reprocessing Multiyear GPS Data from Continuously Operating Reference Stations on Cloud Computing Platform

NASA Astrophysics Data System (ADS)

Yoon, S.

2016-12-01

To define geodetic reference frame using GPS data collected by Continuously Operating Reference Stations (CORS) network, historical GPS data needs to be reprocessed regularly. Reprocessing GPS data collected by upto 2000 CORS sites for the last two decades requires a lot of computational resource. At National Geodetic Survey (NGS), there has been one completed reprocessing in 2011, and currently, the second reprocessing is undergoing. For the first reprocessing effort, in-house computing resource was utilized. In the current second reprocessing effort, outsourced cloud computing platform is being utilized. In this presentation, the outline of data processing strategy at NGS is described as well as the effort to parallelize the data processing procedure in order to maximize the benefit of the cloud computing. The time and cost savings realized by utilizing cloud computing approach will also be discussed.

Parallel workflow tools to facilitate human brain MRI post-processing

PubMed Central

Cui, Zaixu; Zhao, Chenxi; Gong, Gaolang

2015-01-01

Multi-modal magnetic resonance imaging (MRI) techniques are widely applied in human brain studies. To obtain specific brain measures of interest from MRI datasets, a number of complex image post-processing steps are typically required. Parallel workflow tools have recently been developed, concatenating individual processing steps and enabling fully automated processing of raw MRI data to obtain the final results. These workflow tools are also designed to make optimal use of available computational resources and to support the parallel processing of different subjects or of independent processing steps for a single subject. Automated, parallel MRI post-processing tools can greatly facilitate relevant brain investigations and are being increasingly applied. In this review, we briefly summarize these parallel workflow tools and discuss relevant issues. PMID:26029043

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.

Optimisation of the usage of LHC and local computing resources in a multidisciplinary physics department hosting a WLCG Tier-2 centre

NASA Astrophysics Data System (ADS)

Barberis, Stefano; Carminati, Leonardo; Leveraro, Franco; Mazza, Simone Michele; Perini, Laura; Perlz, Francesco; Rebatto, David; Tura, Ruggero; Vaccarossa, Luca; Villaplana, Miguel

2015-12-01

We present the approach of the University of Milan Physics Department and the local unit of INFN to allow and encourage the sharing among different research areas of computing, storage and networking resources (the largest ones being those composing the Milan WLCG Tier-2 centre and tailored to the needs of the ATLAS experiment). Computing resources are organised as independent HTCondor pools, with a global master in charge of monitoring them and optimising their usage. The configuration has to provide satisfactory throughput for both serial and parallel (multicore, MPI) jobs. A combination of local, remote and cloud storage options are available. The experience of users from different research areas operating on this shared infrastructure is discussed. The promising direction of improving scientific computing throughput by federating access to distributed computing and storage also seems to fit very well with the objectives listed in the European Horizon 2020 framework for research and development.

Multilevel Parallelization of AutoDock 4.2.

PubMed

Norgan, Andrew P; Coffman, Paul K; Kocher, Jean-Pierre A; Katzmann, David J; Sosa, Carlos P

2011-04-28

Virtual (computational) screening is an increasingly important tool for drug discovery. AutoDock is a popular open-source application for performing molecular docking, the prediction of ligand-receptor interactions. AutoDock is a serial application, though several previous efforts have parallelized various aspects of the program. In this paper, we report on a multi-level parallelization of AutoDock 4.2 (mpAD4). Using MPI and OpenMP, AutoDock 4.2 was parallelized for use on MPI-enabled systems and to multithread the execution of individual docking jobs. In addition, code was implemented to reduce input/output (I/O) traffic by reusing grid maps at each node from docking to docking. Performance of mpAD4 was examined on two multiprocessor computers. Using MPI with OpenMP multithreading, mpAD4 scales with near linearity on the multiprocessor systems tested. In situations where I/O is limiting, reuse of grid maps reduces both system I/O and overall screening time. Multithreading of AutoDock's Lamarkian Genetic Algorithm with OpenMP increases the speed of execution of individual docking jobs, and when combined with MPI parallelization can significantly reduce the execution time of virtual screens. This work is significant in that mpAD4 speeds the execution of certain molecular docking workloads and allows the user to optimize the degree of system-level (MPI) and node-level (OpenMP) parallelization to best fit both workloads and computational resources.

OpenMP parallelization of a gridded SWAT (SWATG)

NASA Astrophysics Data System (ADS)

Zhang, Ying; Hou, Jinliang; Cao, Yongpan; Gu, Juan; Huang, Chunlin

2017-12-01

Large-scale, long-term and high spatial resolution simulation is a common issue in environmental modeling. A Gridded Hydrologic Response Unit (HRU)-based Soil and Water Assessment Tool (SWATG) that integrates grid modeling scheme with different spatial representations also presents such problems. The time-consuming problem affects applications of very high resolution large-scale watershed modeling. The OpenMP (Open Multi-Processing) parallel application interface is integrated with SWATG (called SWATGP) to accelerate grid modeling based on the HRU level. Such parallel implementation takes better advantage of the computational power of a shared memory computer system. We conducted two experiments at multiple temporal and spatial scales of hydrological modeling using SWATG and SWATGP on a high-end server. At 500-m resolution, SWATGP was found to be up to nine times faster than SWATG in modeling over a roughly 2000 km2 watershed with 1 CPU and a 15 thread configuration. The study results demonstrate that parallel models save considerable time relative to traditional sequential simulation runs. Parallel computations of environmental models are beneficial for model applications, especially at large spatial and temporal scales and at high resolutions. The proposed SWATGP model is thus a promising tool for large-scale and high-resolution water resources research and management in addition to offering data fusion and model coupling ability.

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

Parallel Photonic Quantum Computation Assisted by Quantum Dots in One-Side Optical Microcavities

PubMed Central

Luo, Ming-Xing; Wang, Xiaojun

2014-01-01

Universal quantum logic gates are important elements for a quantum computer. In contrast to previous constructions on one degree of freedom (DOF) of quantum systems, we investigate the possibility of parallel quantum computations dependent on two DOFs of photon systems. We construct deterministic hyper-controlled-not (hyper-CNOT) gates operating on the spatial-mode and the polarization DOFs of two-photon or one-photon systems by exploring the giant optical circular birefringence induced by quantum-dot spins in one-sided optical microcavities. These hyper-CNOT gates show that the quantum states of two DOFs can be viewed as independent qubits without requiring auxiliary DOFs in theory. This result can reduce the quantum resources by half for quantum applications with large qubit systems, such as the quantum Shor algorithm. PMID:25030424

Parallel photonic quantum computation assisted by quantum dots in one-side optical microcavities.

PubMed

Luo, Ming-Xing; Wang, Xiaojun

2014-07-17

Universal quantum logic gates are important elements for a quantum computer. In contrast to previous constructions on one degree of freedom (DOF) of quantum systems, we investigate the possibility of parallel quantum computations dependent on two DOFs of photon systems. We construct deterministic hyper-controlled-not (hyper-CNOT) gates operating on the spatial-mode and the polarization DOFs of two-photon or one-photon systems by exploring the giant optical circular birefringence induced by quantum-dot spins in one-sided optical microcavities. These hyper-CNOT gates show that the quantum states of two DOFs can be viewed as independent qubits without requiring auxiliary DOFs in theory. This result can reduce the quantum resources by half for quantum applications with large qubit systems, such as the quantum Shor algorithm.

Constraint-Based Scheduling System

NASA Technical Reports Server (NTRS)

Zweben, Monte; Eskey, Megan; Stock, Todd; Taylor, Will; Kanefsky, Bob; Drascher, Ellen; Deale, Michael; Daun, Brian; Davis, Gene

1995-01-01

Report describes continuing development of software for constraint-based scheduling system implemented eventually on massively parallel computer. Based on machine learning as means of improving scheduling. Designed to learn when to change search strategy by analyzing search progress and learning general conditions under which resource bottleneck occurs.

The performance of low-cost commercial cloud computing as an alternative in computational chemistry.

PubMed

Thackston, Russell; Fortenberry, Ryan C

2015-05-05

The growth of commercial cloud computing (CCC) as a viable means of computational infrastructure is largely unexplored for the purposes of quantum chemistry. In this work, the PSI4 suite of computational chemistry programs is installed on five different types of Amazon World Services CCC platforms. The performance for a set of electronically excited state single-point energies is compared between these CCC platforms and typical, "in-house" physical machines. Further considerations are made for the number of cores or virtual CPUs (vCPUs, for the CCC platforms), but no considerations are made for full parallelization of the program (even though parallelization of the BLAS library is implemented), complete high-performance computing cluster utilization, or steal time. Even with this most pessimistic view of the computations, CCC resources are shown to be more cost effective for significant numbers of typical quantum chemistry computations. Large numbers of large computations are still best utilized by more traditional means, but smaller-scale research may be more effectively undertaken through CCC services. © 2015 Wiley Periodicals, Inc.

Computational Science at the Argonne Leadership Computing Facility

NASA Astrophysics Data System (ADS)

Romero, Nichols

2014-03-01

The goal of the Argonne Leadership Computing Facility (ALCF) is to extend the frontiers of science by solving problems that require innovative approaches and the largest-scale computing systems. ALCF's most powerful computer - Mira, an IBM Blue Gene/Q system - has nearly one million cores. How does one program such systems? What software tools are available? Which scientific and engineering applications are able to utilize such levels of parallelism? This talk will address these questions and describe a sampling of projects that are using ALCF systems in their research, including ones in nanoscience, materials science, and chemistry. Finally, the ways to gain access to ALCF resources will be presented. This research used resources of the Argonne Leadership Computing Facility at Argonne National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under contract DE-AC02-06CH11357.

Approaches in highly parameterized inversion-PESTCommander, a graphical user interface for file and run management across networks

USGS Publications Warehouse

Karanovic, Marinko; Muffels, Christopher T.; Tonkin, Matthew J.; Hunt, Randall J.

2012-01-01

Models of environmental systems have become increasingly complex, incorporating increasingly large numbers of parameters in an effort to represent physical processes on a scale approaching that at which they occur in nature. Consequently, the inverse problem of parameter estimation (specifically, model calibration) and subsequent uncertainty analysis have become increasingly computation-intensive endeavors. Fortunately, advances in computing have made computational power equivalent to that of dozens to hundreds of desktop computers accessible through a variety of alternate means: modelers have various possibilities, ranging from traditional Local Area Networks (LANs) to cloud computing. Commonly used parameter estimation software is well suited to take advantage of the availability of such increased computing power. Unfortunately, logistical issues become increasingly important as an increasing number and variety of computers are brought to bear on the inverse problem. To facilitate efficient access to disparate computer resources, the PESTCommander program documented herein has been developed to provide a Graphical User Interface (GUI) that facilitates the management of model files ("file management") and remote launching and termination of "slave" computers across a distributed network of computers ("run management"). In version 1.0 described here, PESTCommander can access and ascertain resources across traditional Windows LANs: however, the architecture of PESTCommander has been developed with the intent that future releases will be able to access computing resources (1) via trusted domains established in Wide Area Networks (WANs) in multiple remote locations and (2) via heterogeneous networks of Windows- and Unix-based operating systems. The design of PESTCommander also makes it suitable for extension to other computational resources, such as those that are available via cloud computing. Version 1.0 of PESTCommander was developed primarily to work with the parameter estimation software PEST; the discussion presented in this report focuses on the use of the PESTCommander together with Parallel PEST. However, PESTCommander can be used with a wide variety of programs and models that require management, distribution, and cleanup of files before or after model execution. In addition to its use with the Parallel PEST program suite, discussion is also included in this report regarding the use of PESTCommander with the Global Run Manager GENIE, which was developed simultaneously with PESTCommander.

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.

Computational Performance of a Parallelized Three-Dimensional High-Order Spectral Element Toolbox

NASA Astrophysics Data System (ADS)

Bosshard, Christoph; Bouffanais, Roland; Clémençon, Christian; Deville, Michel O.; Fiétier, Nicolas; Gruber, Ralf; Kehtari, Sohrab; Keller, Vincent; Latt, Jonas

In this paper, a comprehensive performance review of an MPI-based high-order three-dimensional spectral element method C++ toolbox is presented. The focus is put on the performance evaluation of several aspects with a particular emphasis on the parallel efficiency. The performance evaluation is analyzed with help of a time prediction model based on a parameterization of the application and the hardware resources. A tailor-made CFD computation benchmark case is introduced and used to carry out this review, stressing the particular interest for clusters with up to 8192 cores. Some problems in the parallel implementation have been detected and corrected. The theoretical complexities with respect to the number of elements, to the polynomial degree, and to communication needs are correctly reproduced. It is concluded that this type of code has a nearly perfect speed up on machines with thousands of cores, and is ready to make the step to next-generation petaflop machines.

Time Warp Operating System, Version 2.5.1

NASA Technical Reports Server (NTRS)

Bellenot, Steven F.; Gieselman, John S.; Hawley, Lawrence R.; Peterson, Judy; Presley, Matthew T.; Reiher, Peter L.; Springer, Paul L.; Tupman, John R.; Wedel, John J., Jr.; Wieland, Frederick P.;

Multi-petascale highly efficient parallel supercomputer

DOEpatents

Asaad, Sameh; Bellofatto, Ralph E.; Blocksome, Michael A.; Blumrich, Matthias A.; Boyle, Peter; Brunheroto, Jose R.; Chen, Dong; Cher, Chen -Yong; Chiu, George L.; Christ, Norman; Coteus, Paul W.; Davis, Kristan D.; Dozsa, Gabor J.; Eichenberger, Alexandre E.; Eisley, Noel A.; Ellavsky, Matthew R.; Evans, Kahn C.; Fleischer, Bruce M.; Fox, Thomas W.; Gara, Alan; Giampapa, Mark E.; Gooding, Thomas M.; Gschwind, Michael K.; Gunnels, John A.; Hall, Shawn A.; Haring, Rudolf A.; Heidelberger, Philip; Inglett, Todd A.; Knudson, Brant L.; Kopcsay, Gerard V.; Kumar, Sameer; Mamidala, Amith R.; Marcella, James A.; Megerian, Mark G.; Miller, Douglas R.; Miller, Samuel J.; Muff, Adam J.; Mundy, Michael B.; O'Brien, John K.; O'Brien, Kathryn M.; Ohmacht, Martin; Parker, Jeffrey J.; Poole, Ruth J.; Ratterman, Joseph D.; Salapura, Valentina; Satterfield, David L.; Senger, Robert M.; Smith, Brian; Steinmacher-Burow, Burkhard; Stockdell, William M.; Stunkel, Craig B.; Sugavanam, Krishnan; Sugawara, Yutaka; Takken, Todd E.; Trager, Barry M.; Van Oosten, James L.; Wait, Charles D.; Walkup, Robert E.; Watson, Alfred T.; Wisniewski, Robert W.; Wu, Peng

2015-07-14

A Multi-Petascale Highly Efficient Parallel Supercomputer of 100 petaOPS-scale computing, at decreased cost, power and footprint, and that allows for a maximum packaging density of processing nodes from an interconnect point of view. The Supercomputer exploits technological advances in VLSI that enables a computing model where many processors can be integrated into a single Application Specific Integrated Circuit (ASIC). Each ASIC computing node comprises a system-on-chip ASIC utilizing four or more processors integrated into one die, with each having full access to all system resources and enabling adaptive partitioning of the processors to functions such as compute or messaging I/O on an application by application basis, and preferably, enable adaptive partitioning of functions in accordance with various algorithmic phases within an application, or if I/O or other processors are underutilized, then can participate in computation or communication nodes are interconnected by a five dimensional torus network with DMA that optimally maximize the throughput of packet communications between nodes and minimize latency.

Design for Run-Time Monitor on Cloud Computing

NASA Astrophysics Data System (ADS)

Kang, Mikyung; Kang, Dong-In; Yun, Mira; Park, Gyung-Leen; Lee, Junghoon

Cloud computing is a new information technology trend that moves computing and data away from desktops and portable PCs into large data centers. The basic principle of cloud computing is to deliver applications as services over the Internet as well as infrastructure. A cloud is the type of a parallel and distributed system consisting of a collection of inter-connected and virtualized computers that are dynamically provisioned and presented as one or more unified computing resources. The large-scale distributed applications on a cloud require adaptive service-based software, which has the capability of monitoring the system status change, analyzing the monitored information, and adapting its service configuration while considering tradeoffs among multiple QoS features simultaneously. In this paper, we design Run-Time Monitor (RTM) which is a system software to monitor the application behavior at run-time, analyze the collected information, and optimize resources on cloud computing. RTM monitors application software through library instrumentation as well as underlying hardware through performance counter optimizing its computing configuration based on the analyzed data.

Churchill: an ultra-fast, deterministic, highly scalable and balanced parallelization strategy for the discovery of human genetic variation in clinical and population-scale genomics.

PubMed

Kelly, Benjamin J; Fitch, James R; Hu, Yangqiu; Corsmeier, Donald J; Zhong, Huachun; Wetzel, Amy N; Nordquist, Russell D; Newsom, David L; White, Peter

2015-01-20

While advances in genome sequencing technology make population-scale genomics a possibility, current approaches for analysis of these data rely upon parallelization strategies that have limited scalability, complex implementation and lack reproducibility. Churchill, a balanced regional parallelization strategy, overcomes these challenges, fully automating the multiple steps required to go from raw sequencing reads to variant discovery. Through implementation of novel deterministic parallelization techniques, Churchill allows computationally efficient analysis of a high-depth whole genome sample in less than two hours. The method is highly scalable, enabling full analysis of the 1000 Genomes raw sequence dataset in a week using cloud resources. http://churchill.nchri.org/.

Parallel scalability and efficiency of vortex particle method for aeroelasticity analysis of bluff bodies

NASA Astrophysics Data System (ADS)

Tolba, Khaled Ibrahim; Morgenthal, Guido

2018-01-01

This paper presents an analysis of the scalability and efficiency of a simulation framework based on the vortex particle method. The code is applied for the numerical aerodynamic analysis of line-like structures. The numerical code runs on multicore CPU and GPU architectures using OpenCL framework. The focus of this paper is the analysis of the parallel efficiency and scalability of the method being applied to an engineering test case, specifically the aeroelastic response of a long-span bridge girder at the construction stage. The target is to assess the optimal configuration and the required computer architecture, such that it becomes feasible to efficiently utilise the method within the computational resources available for a regular engineering office. The simulations and the scalability analysis are performed on a regular gaming type computer.

Surrogates for numerical simulations; optimization of eddy-promoter heat exchangers

NASA Technical Reports Server (NTRS)

Patera, Anthony T.; Patera, Anthony

1993-01-01

Although the advent of fast and inexpensive parallel computers has rendered numerous previously intractable calculations feasible, many numerical simulations remain too resource-intensive to be directly inserted in engineering optimization efforts. An attractive alternative to direct insertion considers models for computational systems: the expensive simulation is evoked only to construct and validate a simplified, input-output model; this simplified input-output model then serves as a simulation surrogate in subsequent engineering optimization studies. A simple 'Bayesian-validated' statistical framework for the construction, validation, and purposive application of static computer simulation surrogates is presented. As an example, dissipation-transport optimization of laminar-flow eddy-promoter heat exchangers are considered: parallel spectral element Navier-Stokes calculations serve to construct and validate surrogates for the flowrate and Nusselt number; these surrogates then represent the originating Navier-Stokes equations in the ensuing design process.

Parallel processing for scientific computations

NASA Technical Reports Server (NTRS)

Alkhatib, Hasan S.

1991-01-01

The main contribution of the effort in the last two years is the introduction of the MOPPS system. After doing extensive literature search, we introduced the system which is described next. MOPPS employs a new solution to the problem of managing programs which solve scientific and engineering applications on a distributed processing environment. Autonomous computers cooperate efficiently in solving large scientific problems with this solution. MOPPS has the advantage of not assuming the presence of any particular network topology or configuration, computer architecture, or operating system. It imposes little overhead on network and processor resources while efficiently managing programs concurrently. The core of MOPPS is an intelligent program manager that builds a knowledge base of the execution performance of the parallel programs it is managing under various conditions. The manager applies this knowledge to improve the performance of future runs. The program manager learns from experience.

Accelerating Astronomy & Astrophysics in the New Era of Parallel Computing: GPUs, Phi and Cloud Computing

NASA Astrophysics Data System (ADS)

Ford, Eric B.; Dindar, Saleh; Peters, Jorg

2015-08-01

The realism of astrophysical simulations and statistical analyses of astronomical data are set by the available computational resources. Thus, astronomers and astrophysicists are constantly pushing the limits of computational capabilities. For decades, astronomers benefited from massive improvements in computational power that were driven primarily by increasing clock speeds and required relatively little attention to details of the computational hardware. For nearly a decade, increases in computational capabilities have come primarily from increasing the degree of parallelism, rather than increasing clock speeds. Further increases in computational capabilities will likely be led by many-core architectures such as Graphical Processing Units (GPUs) and Intel Xeon Phi. Successfully harnessing these new architectures, requires significantly more understanding of the hardware architecture, cache hierarchy, compiler capabilities and network network characteristics.I will provide an astronomer's overview of the opportunities and challenges provided by modern many-core architectures and elastic cloud computing. The primary goal is to help an astronomical audience understand what types of problems are likely to yield more than order of magnitude speed-ups and which problems are unlikely to parallelize sufficiently efficiently to be worth the development time and/or costs.I will draw on my experience leading a team in developing the Swarm-NG library for parallel integration of large ensembles of small n-body systems on GPUs, as well as several smaller software projects. I will share lessons learned from collaborating with computer scientists, including both technical and soft skills. Finally, I will discuss the challenges of training the next generation of astronomers to be proficient in this new era of high-performance computing, drawing on experience teaching a graduate class on High-Performance Scientific Computing for Astrophysics and organizing a 2014 advanced summer school on Bayesian Computing for Astronomical Data Analysis with support of the Penn State Center for Astrostatistics and Institute for CyberScience.

High-performance scientific computing in the cloud

NASA Astrophysics Data System (ADS)

Jorissen, Kevin; Vila, Fernando; Rehr, John

2011-03-01

Cloud computing has the potential to open up high-performance computational science to a much broader class of researchers, owing to its ability to provide on-demand, virtualized computational resources. However, before such approaches can become commonplace, user-friendly tools must be developed that hide the unfamiliar cloud environment and streamline the management of cloud resources for many scientific applications. We have recently shown that high-performance cloud computing is feasible for parallelized x-ray spectroscopy calculations. We now present benchmark results for a wider selection of scientific applications focusing on electronic structure and spectroscopic simulation software in condensed matter physics. These applications are driven by an improved portable interface that can manage virtual clusters and run various applications in the cloud. We also describe a next generation of cluster tools, aimed at improved performance and a more robust cluster deployment. Supported by NSF grant OCI-1048052.

Exploiting Vector and Multicore Parallelsim for Recursive, Data- and Task-Parallel Programs

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

Ren, Bin; Krishnamoorthy, Sriram; Agrawal, Kunal

Modern hardware contains parallel execution resources that are well-suited for data-parallelism-vector units-and task parallelism-multicores. However, most work on parallel scheduling focuses on one type of hardware or the other. In this work, we present a scheduling framework that allows for a unified treatment of task- and data-parallelism. Our key insight is an abstraction, task blocks, that uniformly handles data-parallel iterations and task-parallel tasks, allowing them to be scheduled on vector units or executed independently as multicores. Our framework allows us to define schedulers that can dynamically select between executing task- blocks on vector units or multicores. We show that thesemore » schedulers are asymptotically optimal, and deliver the maximum amount of parallelism available in computation trees. To evaluate our schedulers, we develop program transformations that can convert mixed data- and task-parallel pro- grams into task block-based programs. Using a prototype instantiation of our scheduling framework, we show that, on an 8-core system, we can simultaneously exploit vector and multicore parallelism to achieve 14×-108× speedup over sequential baselines.« less

A Parallel Numerical Micromagnetic Code Using FEniCS

NASA Astrophysics Data System (ADS)

Nagy, L.; Williams, W.; Mitchell, L.

2013-12-01

Many problems in the geosciences depend on understanding the ability of magnetic minerals to provide stable paleomagnetic recordings. Numerical micromagnetic modelling allows us to calculate the domain structures found in naturally occurring magnetic materials. However the computational cost rises exceedingly quickly with respect to the size and complexity of the geometries that we wish to model. This problem is compounded by the fact that the modern processor design no longer focuses on the speed at which calculations are performed, but rather on the number of computational units amongst which we may distribute our calculations. Consequently to better exploit modern computational resources our micromagnetic simulations must "go parallel". We present a parallel and scalable micromagnetics code written using FEniCS. FEniCS is a multinational collaboration involving several institutions (University of Cambridge, University of Chicago, The Simula Research Laboratory, etc.) that aims to provide a set of tools for writing scientific software; in particular software that employs the finite element method. The advantages of this approach are the leveraging of pre-existing projects from the world of scientific computing (PETSc, Trilinos, Metis/Parmetis, etc.) and exposing these so that researchers may pose problems in a manner closer to the mathematical language of their domain. Our code provides a scriptable interface (in Python) that allows users to not only run micromagnetic models in parallel, but also to perform pre/post processing of data.

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.

Incentive Compatible Online Scheduling of Malleable Parallel Jobs with Individual Deadlines

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

Carroll, Thomas E.; Grosu, Daniel

2010-09-13

We consider the online scheduling of malleable jobs on parallel systems, such as clusters, symmetric multiprocessing computers, and multi-core processor computers. Malleable jobs is a model of parallel processing in which jobs adapt to the number of processors assigned to them. This model permits the scheduler and resource manager to make more efficient use of the available resources. Each malleable job is characterized by arrival time, deadline, and value. If the job completes by its deadline, the user earns the payoff indicated by the value; otherwise, she earns a payoff of zero. The scheduling objective is to maximize the summore » of the values of the jobs that complete by their associated deadlines. Complicating the matter is that users in the real world are rational and they will attempt to manipulate the scheduler by misreporting their jobs’ parameters if it benefits them to do so. To mitigate this behavior, we design an incentive compatible online scheduling mechanism. Incentive compatibility assures us that the users will obtain the maximum payoff only if they truthfully report their jobs’ parameters to the scheduler. Finally, we simulate and study the mechanism to show the effects of misreports on the cheaters and on the system.« less

SNAVA-A real-time multi-FPGA multi-model spiking neural network simulation architecture.

PubMed

Sripad, Athul; Sanchez, Giovanny; Zapata, Mireya; Pirrone, Vito; Dorta, Taho; Cambria, Salvatore; Marti, Albert; Krishnamourthy, Karthikeyan; Madrenas, Jordi

2018-01-01

Spiking Neural Networks (SNN) for Versatile Applications (SNAVA) simulation platform is a scalable and programmable parallel architecture that supports real-time, large-scale, multi-model SNN computation. This parallel architecture is implemented in modern Field-Programmable Gate Arrays (FPGAs) devices to provide high performance execution and flexibility to support large-scale SNN models. Flexibility is defined in terms of programmability, which allows easy synapse and neuron implementation. This has been achieved by using a special-purpose Processing Elements (PEs) for computing SNNs, and analyzing and customizing the instruction set according to the processing needs to achieve maximum performance with minimum resources. The parallel architecture is interfaced with customized Graphical User Interfaces (GUIs) to configure the SNN's connectivity, to compile the neuron-synapse model and to monitor SNN's activity. Our contribution intends to provide a tool that allows to prototype SNNs faster than on CPU/GPU architectures but significantly cheaper than fabricating a customized neuromorphic chip. This could be potentially valuable to the computational neuroscience and neuromorphic engineering communities. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

Balancing exploration, uncertainty and computational demands in many objective reservoir optimization

NASA Astrophysics Data System (ADS)

Zatarain Salazar, Jazmin; Reed, Patrick M.; Quinn, Julianne D.; Giuliani, Matteo; Castelletti, Andrea

2017-11-01

Reservoir operations are central to our ability to manage river basin systems serving conflicting multi-sectoral demands under increasingly uncertain futures. These challenges motivate the need for new solution strategies capable of effectively and efficiently discovering the multi-sectoral tradeoffs that are inherent to alternative reservoir operation policies. Evolutionary many-objective direct policy search (EMODPS) is gaining importance in this context due to its capability of addressing multiple objectives and its flexibility in incorporating multiple sources of uncertainties. This simulation-optimization framework has high potential for addressing the complexities of water resources management, and it can benefit from current advances in parallel computing and meta-heuristics. This study contributes a diagnostic assessment of state-of-the-art parallel strategies for the auto-adaptive Borg Multi Objective Evolutionary Algorithm (MOEA) to support EMODPS. Our analysis focuses on the Lower Susquehanna River Basin (LSRB) system where multiple sectoral demands from hydropower production, urban water supply, recreation and environmental flows need to be balanced. Using EMODPS with different parallel configurations of the Borg MOEA, we optimize operating policies over different size ensembles of synthetic streamflows and evaporation rates. As we increase the ensemble size, we increase the statistical fidelity of our objective function evaluations at the cost of higher computational demands. This study demonstrates how to overcome the mathematical and computational barriers associated with capturing uncertainties in stochastic multiobjective reservoir control optimization, where parallel algorithmic search serves to reduce the wall-clock time in discovering high quality representations of key operational tradeoffs. Our results show that emerging self-adaptive parallelization schemes exploiting cooperative search populations are crucial. Such strategies provide a promising new set of tools for effectively balancing exploration, uncertainty, and computational demands when using EMODPS.

Collectively loading an application in a parallel computer

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

Aho, Michael E.; Attinella, John E.; Gooding, Thomas M.

Collectively loading an application in a parallel computer, the parallel computer comprising a plurality of compute nodes, including: identifying, by a parallel computer control system, a subset of compute nodes in the parallel computer to execute a job; selecting, by the parallel computer control system, one of the subset of compute nodes in the parallel computer as a job leader compute node; retrieving, by the job leader compute node from computer memory, an application for executing the job; and broadcasting, by the job leader to the subset of compute nodes in the parallel computer, the application for executing the job.

3D Kirchhoff depth migration algorithm: A new scalable approach for parallelization on multicore CPU based cluster

NASA Astrophysics Data System (ADS)

Rastogi, Richa; Londhe, Ashutosh; Srivastava, Abhishek; Sirasala, Kirannmayi M.; Khonde, Kiran

2017-03-01

In this article, a new scalable 3D Kirchhoff depth migration algorithm is presented on state of the art multicore CPU based cluster. Parallelization of 3D Kirchhoff depth migration is challenging due to its high demand of compute time, memory, storage and I/O along with the need of their effective management. The most resource intensive modules of the algorithm are traveltime calculations and migration summation which exhibit an inherent trade off between compute time and other resources. The parallelization strategy of the algorithm largely depends on the storage of calculated traveltimes and its feeding mechanism to the migration process. The presented work is an extension of our previous work, wherein a 3D Kirchhoff depth migration application for multicore CPU based parallel system had been developed. Recently, we have worked on improving parallel performance of this application by re-designing the parallelization approach. The new algorithm is capable to efficiently migrate both prestack and poststack 3D data. It exhibits flexibility for migrating large number of traces within the available node memory and with minimal requirement of storage, I/O and inter-node communication. The resultant application is tested using 3D Overthrust data on PARAM Yuva II, which is a Xeon E5-2670 based multicore CPU cluster with 16 cores/node and 64 GB shared memory. Parallel performance of the algorithm is studied using different numerical experiments and the scalability results show striking improvement over its previous version. An impressive 49.05X speedup with 76.64% efficiency is achieved for 3D prestack data and 32.00X speedup with 50.00% efficiency for 3D poststack data, using 64 nodes. The results also demonstrate the effectiveness and robustness of the improved algorithm with high scalability and efficiency on a multicore CPU cluster.

Managing Parallelism and Resources in Scientific Dataflow Programs

DTIC Science & Technology

1990-03-01

1983. [52] K. Hiraki , K. Nishida, S. Sekiguchi, and T. Shimada. Maintainence architecture and its LSI implementation of a dataflow computer with a... Hiraki , and K. Nishida. An architecture of a data flow machine and its evaluation. In Proceedings of CompCon 84, pages 486-490. IEEE, 1984. [84] N

An integrated system for land resources supervision based on the IoT and cloud computing

NASA Astrophysics Data System (ADS)

Fang, Shifeng; Zhu, Yunqiang; Xu, Lida; Zhang, Jinqu; Zhou, Peiji; Luo, Kan; Yang, Jie

2017-01-01

Integrated information systems are important safeguards for the utilisation and development of land resources. Information technologies, including the Internet of Things (IoT) and cloud computing, are inevitable requirements for the quality and efficiency of land resources supervision tasks. In this study, an economical and highly efficient supervision system for land resources has been established based on IoT and cloud computing technologies; a novel online and offline integrated system with synchronised internal and field data that includes the entire process of 'discovering breaches, analysing problems, verifying fieldwork and investigating cases' was constructed. The system integrates key technologies, such as the automatic extraction of high-precision information based on remote sensing, semantic ontology-based technology to excavate and discriminate public sentiment on the Internet that is related to illegal incidents, high-performance parallel computing based on MapReduce, uniform storing and compressing (bitwise) technology, global positioning system data communication and data synchronisation mode, intelligent recognition and four-level ('device, transfer, system and data') safety control technology. The integrated system based on a 'One Map' platform has been officially implemented by the Department of Land and Resources of Guizhou Province, China, and was found to significantly increase the efficiency and level of land resources supervision. The system promoted the overall development of informatisation in fields related to land resource management.

Enabling Grid Computing resources within the KM3NeT computing model

NASA Astrophysics Data System (ADS)

Filippidis, Christos

2016-04-01

KM3NeT is a future European deep-sea research infrastructure hosting a new generation neutrino detectors that - located at the bottom of the Mediterranean Sea - will open a new window on the universe and answer fundamental questions both in particle physics and astrophysics. International collaborative scientific experiments, like KM3NeT, are generating datasets which are increasing exponentially in both complexity and volume, making their analysis, archival, and sharing one of the grand challenges of the 21st century. These experiments, in their majority, adopt computing models consisting of different Tiers with several computing centres and providing a specific set of services for the different steps of data processing such as detector calibration, simulation and data filtering, reconstruction and analysis. The computing requirements are extremely demanding and, usually, span from serial to multi-parallel or GPU-optimized jobs. The collaborative nature of these experiments demands very frequent WAN data transfers and data sharing among individuals and groups. In order to support the aforementioned demanding computing requirements we enabled Grid Computing resources, operated by EGI, within the KM3NeT computing model. In this study we describe our first advances in this field and the method for the KM3NeT users to utilize the EGI computing resources in a simulation-driven use-case.

Graph Partitioning for Parallel Applications in Heterogeneous Grid Environments

NASA Technical Reports Server (NTRS)

Bisws, Rupak; Kumar, Shailendra; Das, Sajal K.; Biegel, Bryan (Technical Monitor)

2002-01-01

The problem of partitioning irregular graphs and meshes for parallel computations on homogeneous systems has been extensively studied. However, these partitioning schemes fail when the target system architecture exhibits heterogeneity in resource characteristics. With the emergence of technologies such as the Grid, it is imperative to study the partitioning problem taking into consideration the differing capabilities of such distributed heterogeneous systems. In our model, the heterogeneous system consists of processors with varying processing power and an underlying non-uniform communication network. We present in this paper a novel multilevel partitioning scheme for irregular graphs and meshes, that takes into account issues pertinent to Grid computing environments. Our partitioning algorithm, called MiniMax, generates and maps partitions onto a heterogeneous system with the objective of minimizing the maximum execution time of the parallel distributed application. For experimental performance study, we have considered both a realistic mesh problem from NASA as well as synthetic workloads. Simulation results demonstrate that MiniMax generates high quality partitions for various classes of applications targeted for parallel execution in a distributed heterogeneous environment.

Reactor Dosimetry Applications Using RAPTOR-M3G:. a New Parallel 3-D Radiation Transport Code

NASA Astrophysics Data System (ADS)

Longoni, Gianluca; Anderson, Stanwood L.

2009-08-01

The numerical solution of the Linearized Boltzmann Equation (LBE) via the Discrete Ordinates method (SN) requires extensive computational resources for large 3-D neutron and gamma transport applications due to the concurrent discretization of the angular, spatial, and energy domains. This paper will discuss the development RAPTOR-M3G (RApid Parallel Transport Of Radiation - Multiple 3D Geometries), a new 3-D parallel radiation transport code, and its application to the calculation of ex-vessel neutron dosimetry responses in the cavity of a commercial 2-loop Pressurized Water Reactor (PWR). RAPTOR-M3G is based domain decomposition algorithms, where the spatial and angular domains are allocated and processed on multi-processor computer architectures. As compared to traditional single-processor applications, this approach reduces the computational load as well as the memory requirement per processor, yielding an efficient solution methodology for large 3-D problems. Measured neutron dosimetry responses in the reactor cavity air gap will be compared to the RAPTOR-M3G predictions. This paper is organized as follows: Section 1 discusses the RAPTOR-M3G methodology; Section 2 describes the 2-loop PWR model and the numerical results obtained. Section 3 addresses the parallel performance of the code, and Section 4 concludes this paper with final remarks and future work.

Dispel4py: An Open-Source Python library for Data-Intensive Seismology

NASA Astrophysics Data System (ADS)

Filgueira, Rosa; Krause, Amrey; Spinuso, Alessandro; Klampanos, Iraklis; Danecek, Peter; Atkinson, Malcolm

2015-04-01

Scientific workflows are a necessary tool for many scientific communities as they enable easy composition and execution of applications on computing resources while scientists can focus on their research without being distracted by the computation management. Nowadays, scientific communities (e.g. Seismology) have access to a large variety of computing resources and their computational problems are best addressed using parallel computing technology. However, successful use of these technologies requires a lot of additional machinery whose use is not straightforward for non-experts: different parallel frameworks (MPI, Storm, multiprocessing, etc.) must be used depending on the computing resources (local machines, grids, clouds, clusters) where applications are run. This implies that for achieving the best applications' performance, users usually have to change their codes depending on the features of the platform selected for running them. This work presents dispel4py, a new open-source Python library for describing abstract stream-based workflows for distributed data-intensive applications. Special care has been taken to provide dispel4py with the ability to map abstract workflows to different platforms dynamically at run-time. Currently dispel4py has four mappings: Apache Storm, MPI, multi-threading and sequential. The main goal of dispel4py is to provide an easy-to-use tool to develop and test workflows in local resources by using the sequential mode with a small dataset. Later, once a workflow is ready for long runs, it can be automatically executed on different parallel resources. dispel4py takes care of the underlying mappings by performing an efficient parallelisation. Processing Elements (PE) represent the basic computational activities of any dispel4Py workflow, which can be a seismologic algorithm, or a data transformation process. For creating a dispel4py workflow, users only have to write very few lines of code to describe their PEs and how they are connected by using Python, which is widely supported on many platforms and is popular in many scientific domains, such as in geosciences. Once, a dispel4py workflow is written, a user only has to select which mapping they would like to use, and everything else (parallelisation, distribution of data) is carried on by dispel4py without any cost to the user. Among all dispel4py features we would like to highlight the following: * The PEs are connected by streams and not by writing to and reading from intermediate files, avoiding many IO operations. * The PEs can be stored into a registry. Therefore, different users can recombine PEs in many different workflows. * dispel4py has been enriched with a provenance mechanism to support runtime provenance analysis. We have adopted the W3C-PROV data model, which is accessible via a prototypal browser-based user interface and a web API. It supports the users with the visualisation of graphical products and offers combined operations to access and download the data, which may be selectively stored at runtime, into dedicated data archives. dispel4py has been already used by seismologists in the VERCE project to develop different seismic workflows. One of them is the Seismic Ambient Noise Cross-Correlation workflow, which preprocesses and cross-correlates traces from several stations. First, this workflow was tested on a local machine by using a small number of stations as input data. Later, it was executed on different parallel platforms (SuperMUC cluster, and Terracorrelator machine), automatically scaling up by using MPI and multiprocessing mappings and up to 1000 stations as input data. The results show that the dispel4py achieves scalable performance in both mappings tested on different parallel platforms.

Developing science gateways for drug discovery in a grid environment.

PubMed

Pérez-Sánchez, Horacio; Rezaei, Vahid; Mezhuyev, Vitaliy; Man, Duhu; Peña-García, Jorge; den-Haan, Helena; Gesing, Sandra

2016-01-01

Methods for in silico screening of large databases of molecules increasingly complement and replace experimental techniques to discover novel compounds to combat diseases. As these techniques become more complex and computationally costly we are faced with an increasing problem to provide the research community of life sciences with a convenient tool for high-throughput virtual screening on distributed computing resources. To this end, we recently integrated the biophysics-based drug-screening program FlexScreen into a service, applicable for large-scale parallel screening and reusable in the context of scientific workflows. Our implementation is based on Pipeline Pilot and Simple Object Access Protocol and provides an easy-to-use graphical user interface to construct complex workflows, which can be executed on distributed computing resources, thus accelerating the throughput by several orders of magnitude.

GSHR-Tree: a spatial index tree based on dynamic spatial slot and hash table in grid environments

NASA Astrophysics Data System (ADS)

Chen, Zhanlong; Wu, Xin-cai; Wu, Liang

2008-12-01

Computation Grids enable the coordinated sharing of large-scale distributed heterogeneous computing resources that can be used to solve computationally intensive problems in science, engineering, and commerce. Grid spatial applications are made possible by high-speed networks and a new generation of Grid middleware that resides between networks and traditional GIS applications. The integration of the multi-sources and heterogeneous spatial information and the management of the distributed spatial resources and the sharing and cooperative of the spatial data and Grid services are the key problems to resolve in the development of the Grid GIS. The performance of the spatial index mechanism is the key technology of the Grid GIS and spatial database affects the holistic performance of the GIS in Grid Environments. In order to improve the efficiency of parallel processing of a spatial mass data under the distributed parallel computing grid environment, this paper presents a new grid slot hash parallel spatial index GSHR-Tree structure established in the parallel spatial indexing mechanism. Based on the hash table and dynamic spatial slot, this paper has improved the structure of the classical parallel R tree index. The GSHR-Tree index makes full use of the good qualities of R-Tree and hash data structure. This paper has constructed a new parallel spatial index that can meet the needs of parallel grid computing about the magnanimous spatial data in the distributed network. This arithmetic splits space in to multi-slots by multiplying and reverting and maps these slots to sites in distributed and parallel system. Each sites constructs the spatial objects in its spatial slot into an R tree. On the basis of this tree structure, the index data was distributed among multiple nodes in the grid networks by using large node R-tree method. The unbalance during process can be quickly adjusted by means of a dynamical adjusting algorithm. This tree structure has considered the distributed operation, reduplication operation transfer operation of spatial index in the grid environment. The design of GSHR-Tree has ensured the performance of the load balance in the parallel computation. This tree structure is fit for the parallel process of the spatial information in the distributed network environments. Instead of spatial object's recursive comparison where original R tree has been used, the algorithm builds the spatial index by applying binary code operation in which computer runs more efficiently, and extended dynamic hash code for bit comparison. In GSHR-Tree, a new server is assigned to the network whenever a split of a full node is required. We describe a more flexible allocation protocol which copes with a temporary shortage of storage resources. It uses a distributed balanced binary spatial tree that scales with insertions to potentially any number of storage servers through splits of the overloaded ones. The application manipulates the GSHR-Tree structure from a node in the grid environment. The node addresses the tree through its image that the splits can make outdated. This may generate addressing errors, solved by the forwarding among the servers. In this paper, a spatial index data distribution algorithm that limits the number of servers has been proposed. We improve the storage utilization at the cost of additional messages. The structure of GSHR-Tree is believed that the scheme of this grid spatial index should fit the needs of new applications using endlessly larger sets of spatial data. Our proposal constitutes a flexible storage allocation method for a distributed spatial index. The insertion policy can be tuned dynamically to cope with periods of storage shortage. In such cases storage balancing should be favored for better space utilization, at the price of extra message exchanges between servers. This structure makes a compromise in the updating of the duplicated index and the transformation of the spatial index data. Meeting the needs of the grid computing, GSHRTree has a flexible structure in order to satisfy new needs in the future. The GSHR-Tree provides the R-tree capabilities for large spatial datasets stored over interconnected servers. The analysis, including the experiments, confirmed the efficiency of our design choices. The scheme should fit the needs of new applications of spatial data, using endlessly larger datasets. Using the system response time of the parallel processing of spatial scope query algorithm as the performance evaluation factor, According to the result of the simulated the experiments, GSHR-Tree is performed to prove the reasonable design and the high performance of the indexing structure that the paper presented.

Development and application of operational techniques for the inventory and monitoring of resources and uses for the Texas coastal zone. Volume 1: Text

NASA Technical Reports Server (NTRS)

Harwood, P. (Principal Investigator); Finley, R.; Mcculloch, S.; Malin, P. A.; Schell, J. A.

1977-01-01

The author has identified the following significant results. Image interpretation and computer-assisted techniques were developed to analyze LANDSAT scenes in support of resource inventory and monitoring requirements for the Texas coastal region. Land cover and land use maps, at a scale of 1:125,000 for the image interpretation product and 1:24,000 for the computer-assisted product, were generated covering four Texas coastal test sites. Classification schemes which parallel national systems were developed for each procedure, including 23 classes for image interpretation technique and 13 classes for the computer-assisted technique. Results indicate that LANDSAT-derived land cover and land use maps can be successfully applied to a variety of planning and management activities on the Texas coast. Computer-derived land/water maps can be used with tide gage data to assess shoreline boundaries for management purposes.

Improving Resource Selection and Scheduling Using Predictions. Chapter 1

NASA Technical Reports Server (NTRS)

Smith, Warren

2003-01-01

The introduction of computational grids has resulted in several new problems in the area of scheduling that can be addressed using predictions. The first problem is selecting where to run an application on the many resources available in a grid. Our approach to help address this problem is to provide predictions of when an application would start to execute if submitted to specific scheduled computer systems. The second problem is gaining simultaneous access to multiple computer systems so that distributed applications can be executed. We help address this problem by investigating how to support advance reservations in local scheduling systems. Our approaches to both of these problems are based on predictions for the execution time of applications on space- shared parallel computers. As a side effect of this work, we also discuss how predictions of application run times can be used to improve scheduling performance.

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.

MrGrid: A Portable Grid Based Molecular Replacement Pipeline

PubMed Central

Reboul, Cyril F.; Androulakis, Steve G.; Phan, Jennifer M. N.; Whisstock, James C.; Goscinski, Wojtek J.; Abramson, David; Buckle, Ashley M.

2010-01-01

Background The crystallographic determination of protein structures can be computationally demanding and for difficult cases can benefit from user-friendly interfaces to high-performance computing resources. Molecular replacement (MR) is a popular protein crystallographic technique that exploits the structural similarity between proteins that share some sequence similarity. But the need to trial permutations of search models, space group symmetries and other parameters makes MR time- and labour-intensive. However, MR calculations are embarrassingly parallel and thus ideally suited to distributed computing. In order to address this problem we have developed MrGrid, web-based software that allows multiple MR calculations to be executed across a grid of networked computers, allowing high-throughput MR. Methodology/Principal Findings MrGrid is a portable web based application written in Java/JSP and Ruby, and taking advantage of Apple Xgrid technology. Designed to interface with a user defined Xgrid resource the package manages the distribution of multiple MR runs to the available nodes on the Xgrid. We evaluated MrGrid using 10 different protein test cases on a network of 13 computers, and achieved an average speed up factor of 5.69. Conclusions MrGrid enables the user to retrieve and manage the results of tens to hundreds of MR calculations quickly and via a single web interface, as well as broadening the range of strategies that can be attempted. This high-throughput approach allows parameter sweeps to be performed in parallel, improving the chances of MR success. PMID:20386612

Moose: An Open-Source Framework to Enable Rapid Development of Collaborative, Multi-Scale, Multi-Physics Simulation Tools

NASA Astrophysics Data System (ADS)

Slaughter, A. E.; Permann, C.; Peterson, J. W.; Gaston, D.; Andrs, D.; Miller, J.

2014-12-01

The Idaho National Laboratory (INL)-developed Multiphysics Object Oriented Simulation Environment (MOOSE; www.mooseframework.org), is an open-source, parallel computational framework for enabling the solution of complex, fully implicit multiphysics systems. MOOSE provides a set of computational tools that scientists and engineers can use to create sophisticated multiphysics simulations. Applications built using MOOSE have computed solutions for chemical reaction and transport equations, computational fluid dynamics, solid mechanics, heat conduction, mesoscale materials modeling, geomechanics, and others. To facilitate the coupling of diverse and highly-coupled physical systems, MOOSE employs the Jacobian-free Newton-Krylov (JFNK) method when solving the coupled nonlinear systems of equations arising in multiphysics applications. The MOOSE framework is written in C++, and leverages other high-quality, open-source scientific software packages such as LibMesh, Hypre, and PETSc. MOOSE uses a "hybrid parallel" model which combines both shared memory (thread-based) and distributed memory (MPI-based) parallelism to ensure efficient resource utilization on a wide range of computational hardware. MOOSE-based applications are inherently modular, which allows for simulation expansion (via coupling of additional physics modules) and the creation of multi-scale simulations. Any application developed with MOOSE supports running (in parallel) any other MOOSE-based application. Each application can be developed independently, yet easily communicate with other applications (e.g., conductivity in a slope-scale model could be a constant input, or a complete phase-field micro-structure simulation) without additional code being written. This method of development has proven effective at INL and expedites the development of sophisticated, sustainable, and collaborative simulation tools.

FPGA-Based Stochastic Echo State Networks for Time-Series Forecasting.

PubMed

Alomar, Miquel L; Canals, Vincent; Perez-Mora, Nicolas; Martínez-Moll, Víctor; Rosselló, Josep L

2016-01-01

Hardware implementation of artificial neural networks (ANNs) allows exploiting the inherent parallelism of these systems. Nevertheless, they require a large amount of resources in terms of area and power dissipation. Recently, Reservoir Computing (RC) has arisen as a strategic technique to design recurrent neural networks (RNNs) with simple learning capabilities. In this work, we show a new approach to implement RC systems with digital gates. The proposed method is based on the use of probabilistic computing concepts to reduce the hardware required to implement different arithmetic operations. The result is the development of a highly functional system with low hardware resources. The presented methodology is applied to chaotic time-series forecasting.

FPGA-Based Stochastic Echo State Networks for Time-Series Forecasting

PubMed Central

Alomar, Miquel L.; Canals, Vincent; Perez-Mora, Nicolas; Martínez-Moll, Víctor; Rosselló, Josep L.

2016-01-01

Hardware implementation of artificial neural networks (ANNs) allows exploiting the inherent parallelism of these systems. Nevertheless, they require a large amount of resources in terms of area and power dissipation. Recently, Reservoir Computing (RC) has arisen as a strategic technique to design recurrent neural networks (RNNs) with simple learning capabilities. In this work, we show a new approach to implement RC systems with digital gates. The proposed method is based on the use of probabilistic computing concepts to reduce the hardware required to implement different arithmetic operations. The result is the development of a highly functional system with low hardware resources. The presented methodology is applied to chaotic time-series forecasting. PMID:26880876

GPU-computing in econophysics and statistical physics

NASA Astrophysics Data System (ADS)

Preis, T.

2011-03-01

A recent trend in computer science and related fields is general purpose computing on graphics processing units (GPUs), which can yield impressive performance. With multiple cores connected by high memory bandwidth, today's GPUs offer resources for non-graphics parallel processing. This article provides a brief introduction into the field of GPU computing and includes examples. In particular computationally expensive analyses employed in financial market context are coded on a graphics card architecture which leads to a significant reduction of computing time. In order to demonstrate the wide range of possible applications, a standard model in statistical physics - the Ising model - is ported to a graphics card architecture as well, resulting in large speedup values.

Parallelization of a Fully-Distributed Hydrologic Model using Sub-basin Partitioning

NASA Astrophysics Data System (ADS)

Vivoni, E. R.; Mniszewski, S.; Fasel, P.; Springer, E.; Ivanov, V. Y.; Bras, R. L.

2005-12-01

A primary obstacle towards advances in watershed simulations has been the limited computational capacity available to most models. The growing trend of model complexity, data availability and physical representation has not been matched by adequate developments in computational efficiency. This situation has created a serious bottleneck which limits existing distributed hydrologic models to small domains and short simulations. In this study, we present novel developments in the parallelization of a fully-distributed hydrologic model. Our work is based on the TIN-based Real-time Integrated Basin Simulator (tRIBS), which provides continuous hydrologic simulation using a multiple resolution representation of complex terrain based on a triangulated irregular network (TIN). While the use of TINs reduces computational demand, the sequential version of the model is currently limited over large basins (>10,000 km2) and long simulation periods (>1 year). To address this, a parallel MPI-based version of the tRIBS model has been implemented and tested using high performance computing resources at Los Alamos National Laboratory. Our approach utilizes domain decomposition based on sub-basin partitioning of the watershed. A stream reach graph based on the channel network structure is used to guide the sub-basin partitioning. Individual sub-basins or sub-graphs of sub-basins are assigned to separate processors to carry out internal hydrologic computations (e.g. rainfall-runoff transformation). Routed streamflow from each sub-basin forms the major hydrologic data exchange along the stream reach graph. Individual sub-basins also share subsurface hydrologic fluxes across adjacent boundaries. We demonstrate how the sub-basin partitioning provides computational feasibility and efficiency for a set of test watersheds in northeastern Oklahoma. We compare the performance of the sequential and parallelized versions to highlight the efficiency gained as the number of processors increases. We also discuss how the coupled use of TINs and parallel processing can lead to feasible long-term simulations in regional watersheds while preserving basin properties at high-resolution.

Final Report for ALCC Allocation: Predictive Simulation of Complex Flow in Wind Farms

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

Barone, Matthew F.; Ananthan, Shreyas; Churchfield, Matt

This report documents work performed using ALCC computing resources granted under a proposal submitted in February 2016, with the resource allocation period spanning the period July 2016 through June 2017. The award allocation was 10.7 million processor-hours at the National Energy Research Scientific Computing Center. The simulations performed were in support of two projects: the Atmosphere to Electrons (A2e) project, supported by the DOE EERE office; and the Exascale Computing Project (ECP), supported by the DOE Office of Science. The project team for both efforts consists of staff scientists and postdocs from Sandia National Laboratories and the National Renewable Energymore » Laboratory. At the heart of these projects is the open-source computational-fluid-dynamics (CFD) code, Nalu. Nalu solves the low-Mach-number Navier-Stokes equations using an unstructured- grid discretization. Nalu leverages the open-source Trilinos solver library and the Sierra Toolkit (STK) for parallelization and I/O. This report documents baseline computational performance of the Nalu code on problems of direct relevance to the wind plant physics application - namely, Large Eddy Simulation (LES) of an atmospheric boundary layer (ABL) flow and wall-modeled LES of a flow past a static wind turbine rotor blade. Parallel performance of Nalu and its constituent solver routines residing in the Trilinos library has been assessed previously under various campaigns. However, both Nalu and Trilinos have been, and remain, in active development and resources have not been available previously to rigorously track code performance over time. With the initiation of the ECP, it is important to establish and document baseline code performance on the problems of interest. This will allow the project team to identify and target any deficiencies in performance, as well as highlight any performance bottlenecks as we exercise the code on a greater variety of platforms and at larger scales. The current study is rather modest in scale, examining performance on problem sizes of O(100 million) elements and core counts up to 8k cores. This will be expanded as more computational resources become available to the projects.« less

Aggregating job exit statuses of a plurality of compute nodes executing a parallel application

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

Aho, Michael E.; Attinella, John E.; Gooding, Thomas M.

Aggregating job exit statuses of a plurality of compute nodes executing a parallel application, including: identifying a subset of compute nodes in the parallel computer to execute the parallel application; selecting one compute node in the subset of compute nodes in the parallel computer as a job leader compute node; initiating execution of the parallel application on the subset of compute nodes; receiving an exit status from each compute node in the subset of compute nodes, where the exit status for each compute node includes information describing execution of some portion of the parallel application by the compute node; aggregatingmore » each exit status from each compute node in the subset of compute nodes; and sending an aggregated exit status for the subset of compute nodes in the parallel computer.« less

Design and Development of a Run-Time Monitor for Multi-Core Architectures in Cloud Computing

PubMed Central

Kang, Mikyung; Kang, Dong-In; Crago, Stephen P.; Park, Gyung-Leen; Lee, Junghoon

2011-01-01

Cloud computing is a new information technology trend that moves computing and data away from desktops and portable PCs into large data centers. The basic principle of cloud computing is to deliver applications as services over the Internet as well as infrastructure. A cloud is a type of parallel and distributed system consisting of a collection of inter-connected and virtualized computers that are dynamically provisioned and presented as one or more unified computing resources. The large-scale distributed applications on a cloud require adaptive service-based software, which has the capability of monitoring system status changes, analyzing the monitored information, and adapting its service configuration while considering tradeoffs among multiple QoS features simultaneously. In this paper, we design and develop a Run-Time Monitor (RTM) which is a system software to monitor the application behavior at run-time, analyze the collected information, and optimize cloud computing resources for multi-core architectures. RTM monitors application software through library instrumentation as well as underlying hardware through a performance counter optimizing its computing configuration based on the analyzed data. PMID:22163811

Design and development of a run-time monitor for multi-core architectures in cloud computing.

PubMed

Kang, Mikyung; Kang, Dong-In; Crago, Stephen P; Park, Gyung-Leen; Lee, Junghoon

2011-01-01

Cloud computing is a new information technology trend that moves computing and data away from desktops and portable PCs into large data centers. The basic principle of cloud computing is to deliver applications as services over the Internet as well as infrastructure. A cloud is a type of parallel and distributed system consisting of a collection of inter-connected and virtualized computers that are dynamically provisioned and presented as one or more unified computing resources. The large-scale distributed applications on a cloud require adaptive service-based software, which has the capability of monitoring system status changes, analyzing the monitored information, and adapting its service configuration while considering tradeoffs among multiple QoS features simultaneously. In this paper, we design and develop a Run-Time Monitor (RTM) which is a system software to monitor the application behavior at run-time, analyze the collected information, and optimize cloud computing resources for multi-core architectures. RTM monitors application software through library instrumentation as well as underlying hardware through a performance counter optimizing its computing configuration based on the analyzed data.

Large-scale parallel genome assembler over cloud computing environment.

PubMed

Das, Arghya Kusum; Koppa, Praveen Kumar; Goswami, Sayan; Platania, Richard; Park, Seung-Jong

2017-06-01

The size of high throughput DNA sequencing data has already reached the terabyte scale. To manage this huge volume of data, many downstream sequencing applications started using locality-based computing over different cloud infrastructures to take advantage of elastic (pay as you go) resources at a lower cost. However, the locality-based programming model (e.g. MapReduce) is relatively new. Consequently, developing scalable data-intensive bioinformatics applications using this model and understanding the hardware environment that these applications require for good performance, both require further research. In this paper, we present a de Bruijn graph oriented Parallel Giraph-based Genome Assembler (GiGA), as well as the hardware platform required for its optimal performance. GiGA uses the power of Hadoop (MapReduce) and Giraph (large-scale graph analysis) to achieve high scalability over hundreds of compute nodes by collocating the computation and data. GiGA achieves significantly higher scalability with competitive assembly quality compared to contemporary parallel assemblers (e.g. ABySS and Contrail) over traditional HPC cluster. Moreover, we show that the performance of GiGA is significantly improved by using an SSD-based private cloud infrastructure over traditional HPC cluster. We observe that the performance of GiGA on 256 cores of this SSD-based cloud infrastructure closely matches that of 512 cores of traditional HPC cluster.

Use of Hilbert Curves in Parallelized CUDA code: Interaction of Interstellar Atoms with the Heliosphere

NASA Astrophysics Data System (ADS)

Destefano, Anthony; Heerikhuisen, Jacob

2015-04-01

Fully 3D particle simulations can be a computationally and memory expensive task, especially when high resolution grid cells are required. The problem becomes further complicated when parallelization is needed. In this work we focus on computational methods to solve these difficulties. Hilbert curves are used to map the 3D particle space to the 1D contiguous memory space. This method of organization allows for minimized cache misses on the GPU as well as a sorted structure that is equivalent to an octal tree data structure. This type of sorted structure is attractive for uses in adaptive mesh implementations due to the logarithm search time. Implementations using the Message Passing Interface (MPI) library and NVIDIA's parallel computing platform CUDA will be compared, as MPI is commonly used on server nodes with many CPU's. We will also compare static grid structures with those of adaptive mesh structures. The physical test bed will be simulating heavy interstellar atoms interacting with a background plasma, the heliosphere, simulated from fully consistent coupled MHD/kinetic particle code. It is known that charge exchange is an important factor in space plasmas, specifically it modifies the structure of the heliosphere itself. We would like to thank the Alabama Supercomputer Authority for the use of their computational resources.

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.

Parallel Computation of Unsteady Flows on a Network of Workstations

NASA Technical Reports Server (NTRS)

1997-01-01

Parallel computation of unsteady flows requires significant computational resources. The utilization of a network of workstations seems an efficient solution to the problem where large problems can be treated at a reasonable cost. This approach requires the solution of several problems: 1) the partitioning and distribution of the problem over a network of workstation, 2) efficient communication tools, 3) managing the system efficiently for a given problem. Of course, there is the question of the efficiency of any given numerical algorithm to such a computing system. NPARC code was chosen as a sample for the application. For the explicit version of the NPARC code both two- and three-dimensional problems were studied. Again both steady and unsteady problems were investigated. The issues studied as a part of the research program were: 1) how to distribute the data between the workstations, 2) how to compute and how to communicate at each node efficiently, 3) how to balance the load distribution. In the following, a summary of these activities is presented. Details of the work have been presented and published as referenced.

Toward real-time Monte Carlo simulation using a commercial cloud computing infrastructure.

PubMed

Wang, Henry; Ma, Yunzhi; Pratx, Guillem; Xing, Lei

2011-09-07

Monte Carlo (MC) methods are the gold standard for modeling photon and electron transport in a heterogeneous medium; however, their computational cost prohibits their routine use in the clinic. Cloud computing, wherein computing resources are allocated on-demand from a third party, is a new approach for high performance computing and is implemented to perform ultra-fast MC calculation in radiation therapy. We deployed the EGS5 MC package in a commercial cloud environment. Launched from a single local computer with Internet access, a Python script allocates a remote virtual cluster. A handshaking protocol designates master and worker nodes. The EGS5 binaries and the simulation data are initially loaded onto the master node. The simulation is then distributed among independent worker nodes via the message passing interface, and the results aggregated on the local computer for display and data analysis. The described approach is evaluated for pencil beams and broad beams of high-energy electrons and photons. The output of cloud-based MC simulation is identical to that produced by single-threaded implementation. For 1 million electrons, a simulation that takes 2.58 h on a local computer can be executed in 3.3 min on the cloud with 100 nodes, a 47× speed-up. Simulation time scales inversely with the number of parallel nodes. The parallelization overhead is also negligible for large simulations. Cloud computing represents one of the most important recent advances in supercomputing technology and provides a promising platform for substantially improved MC simulation. In addition to the significant speed up, cloud computing builds a layer of abstraction for high performance parallel computing, which may change the way dose calculations are performed and radiation treatment plans are completed.

mGrid: A load-balanced distributed computing environment for the remote execution of the user-defined Matlab code

PubMed Central

Karpievitch, Yuliya V; Almeida, Jonas S

2006-01-01

Background Matlab, a powerful and productive language that allows for rapid prototyping, modeling and simulation, is widely used in computational biology. Modeling and simulation of large biological systems often require more computational resources then are available on a single computer. Existing distributed computing environments like the Distributed Computing Toolbox, MatlabMPI, Matlab*G and others allow for the remote (and possibly parallel) execution of Matlab commands with varying support for features like an easy-to-use application programming interface, load-balanced utilization of resources, extensibility over the wide area network, and minimal system administration skill requirements. However, all of these environments require some level of access to participating machines to manually distribute the user-defined libraries that the remote call may invoke. Results mGrid augments the usual process distribution seen in other similar distributed systems by adding facilities for user code distribution. mGrid's client-side interface is an easy-to-use native Matlab toolbox that transparently executes user-defined code on remote machines (i.e. the user is unaware that the code is executing somewhere else). Run-time variables are automatically packed and distributed with the user-defined code and automated load-balancing of remote resources enables smooth concurrent execution. mGrid is an open source environment. Apart from the programming language itself, all other components are also open source, freely available tools: light-weight PHP scripts and the Apache web server. Conclusion Transparent, load-balanced distribution of user-defined Matlab toolboxes and rapid prototyping of many simple parallel applications can now be done with a single easy-to-use Matlab command. Because mGrid utilizes only Matlab, light-weight PHP scripts and the Apache web server, installation and configuration are very simple. Moreover, the web-based infrastructure of mGrid allows for it to be easily extensible over the Internet. PMID:16539707

mGrid: a load-balanced distributed computing environment for the remote execution of the user-defined Matlab code.

PubMed

Karpievitch, Yuliya V; Almeida, Jonas S

2006-03-15

Matlab, a powerful and productive language that allows for rapid prototyping, modeling and simulation, is widely used in computational biology. Modeling and simulation of large biological systems often require more computational resources then are available on a single computer. Existing distributed computing environments like the Distributed Computing Toolbox, MatlabMPI, Matlab*G and others allow for the remote (and possibly parallel) execution of Matlab commands with varying support for features like an easy-to-use application programming interface, load-balanced utilization of resources, extensibility over the wide area network, and minimal system administration skill requirements. However, all of these environments require some level of access to participating machines to manually distribute the user-defined libraries that the remote call may invoke. mGrid augments the usual process distribution seen in other similar distributed systems by adding facilities for user code distribution. mGrid's client-side interface is an easy-to-use native Matlab toolbox that transparently executes user-defined code on remote machines (i.e. the user is unaware that the code is executing somewhere else). Run-time variables are automatically packed and distributed with the user-defined code and automated load-balancing of remote resources enables smooth concurrent execution. mGrid is an open source environment. Apart from the programming language itself, all other components are also open source, freely available tools: light-weight PHP scripts and the Apache web server. Transparent, load-balanced distribution of user-defined Matlab toolboxes and rapid prototyping of many simple parallel applications can now be done with a single easy-to-use Matlab command. Because mGrid utilizes only Matlab, light-weight PHP scripts and the Apache web server, installation and configuration are very simple. Moreover, the web-based infrastructure of mGrid allows for it to be easily extensible over the Internet.

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

Astrophysical data mining with GPU. A case study: Genetic classification of globular clusters

NASA Astrophysics Data System (ADS)

Cavuoti, S.; Garofalo, M.; Brescia, M.; Paolillo, M.; Pescape', A.; Longo, G.; Ventre, G.

2014-01-01

We present a multi-purpose genetic algorithm, designed and implemented with GPGPU/CUDA parallel computing technology. The model was derived from our CPU serial implementation, named GAME (Genetic Algorithm Model Experiment). It was successfully tested and validated on the detection of candidate Globular Clusters in deep, wide-field, single band HST images. The GPU version of GAME will be made available to the community by integrating it into the web application DAMEWARE (DAta Mining Web Application REsource, http://dame.dsf.unina.it/beta_info.html), a public data mining service specialized on massive astrophysical data. Since genetic algorithms are inherently parallel, the GPGPU computing paradigm leads to a speedup of a factor of 200× in the training phase with respect to the CPU based version.

Temporal parallelization of edge plasma simulations using the parareal algorithm and the SOLPS code

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

Samaddar, Debasmita; Coster, D. P.; Bonnin, X.

We show that numerical modelling of edge plasma physics may be successfully parallelized in time. The parareal algorithm has been employed for this purpose and the SOLPS code package coupling the B2.5 finite-volume fluid plasma solver with the kinetic Monte-Carlo neutral code Eirene has been used as a test bed. The complex dynamics of the plasma and neutrals in the scrape-off layer (SOL) region makes this a unique application. It is demonstrated that a significant computational gain (more than an order of magnitude) may be obtained with this technique. The use of the IPS framework for event-based parareal implementation optimizesmore » resource utilization and has been shown to significantly contribute to the computational gain.« less

Temporal parallelization of edge plasma simulations using the parareal algorithm and the SOLPS code

DOE PAGES

Samaddar, Debasmita; Coster, D. P.; Bonnin, X.; ...

2017-07-31

We show that numerical modelling of edge plasma physics may be successfully parallelized in time. The parareal algorithm has been employed for this purpose and the SOLPS code package coupling the B2.5 finite-volume fluid plasma solver with the kinetic Monte-Carlo neutral code Eirene has been used as a test bed. The complex dynamics of the plasma and neutrals in the scrape-off layer (SOL) region makes this a unique application. It is demonstrated that a significant computational gain (more than an order of magnitude) may be obtained with this technique. The use of the IPS framework for event-based parareal implementation optimizesmore » resource utilization and has been shown to significantly contribute to the computational gain.« less

Cpu/gpu Computing for AN Implicit Multi-Block Compressible Navier-Stokes Solver on Heterogeneous Platform

NASA Astrophysics Data System (ADS)

Deng, Liang; Bai, Hanli; Wang, Fang; Xu, Qingxin

2016-06-01

CPU/GPU computing allows scientists to tremendously accelerate their numerical codes. In this paper, we port and optimize a double precision alternating direction implicit (ADI) solver for three-dimensional compressible Navier-Stokes equations from our in-house Computational Fluid Dynamics (CFD) software on heterogeneous platform. First, we implement a full GPU version of the ADI solver to remove a lot of redundant data transfers between CPU and GPU, and then design two fine-grain schemes, namely “one-thread-one-point” and “one-thread-one-line”, to maximize the performance. Second, we present a dual-level parallelization scheme using the CPU/GPU collaborative model to exploit the computational resources of both multi-core CPUs and many-core GPUs within the heterogeneous platform. Finally, considering the fact that memory on a single node becomes inadequate when the simulation size grows, we present a tri-level hybrid programming pattern MPI-OpenMP-CUDA that merges fine-grain parallelism using OpenMP and CUDA threads with coarse-grain parallelism using MPI for inter-node communication. We also propose a strategy to overlap the computation with communication using the advanced features of CUDA and MPI programming. We obtain speedups of 6.0 for the ADI solver on one Tesla M2050 GPU in contrast to two Xeon X5670 CPUs. Scalability tests show that our implementation can offer significant performance improvement on heterogeneous platform.

A Flexible Computational Framework Using R and Map-Reduce for Permutation Tests of Massive Genetic Analysis of Complex Traits.

PubMed

Mahjani, Behrang; Toor, Salman; Nettelblad, Carl; Holmgren, Sverker

2017-01-01

In quantitative trait locus (QTL) mapping significance of putative QTL is often determined using permutation testing. The computational needs to calculate the significance level are immense, 10 4 up to 10 8 or even more permutations can be needed. We have previously introduced the PruneDIRECT algorithm for multiple QTL scan with epistatic interactions. This algorithm has specific strengths for permutation testing. Here, we present a flexible, parallel computing framework for identifying multiple interacting QTL using the PruneDIRECT algorithm which uses the map-reduce model as implemented in Hadoop. The framework is implemented in R, a widely used software tool among geneticists. This enables users to rearrange algorithmic steps to adapt genetic models, search algorithms, and parallelization steps to their needs in a flexible way. Our work underlines the maturity of accessing distributed parallel computing for computationally demanding bioinformatics applications through building workflows within existing scientific environments. We investigate the PruneDIRECT algorithm, comparing its performance to exhaustive search and DIRECT algorithm using our framework on a public cloud resource. We find that PruneDIRECT is vastly superior for permutation testing, and perform 2 ×10 5 permutations for a 2D QTL problem in 15 hours, using 100 cloud processes. We show that our framework scales out almost linearly for a 3D QTL search.

Speculation and replication in temperature accelerated dynamics

DOE PAGES

Zamora, Richard J.; Perez, Danny; Voter, Arthur F.

2018-02-12

Accelerated Molecular Dynamics (AMD) is a class of MD-based algorithms for the long-time scale simulation of atomistic systems that are characterized by rare-event transitions. Temperature-Accelerated Dynamics (TAD), a traditional AMD approach, hastens state-to-state transitions by performing MD at an elevated temperature. Recently, Speculatively-Parallel TAD (SpecTAD) was introduced, allowing the TAD procedure to exploit parallel computing systems by concurrently executing in a dynamically generated list of speculative future states. Although speculation can be very powerful, it is not always the most efficient use of parallel resources. In this paper, we compare the performance of speculative parallelism with a replica-based technique, similarmore » to the Parallel Replica Dynamics method. A hybrid SpecTAD approach is also presented, in which each speculation process is further accelerated by a local set of replicas. Finally and overall, this work motivates the use of hybrid parallelism whenever possible, as some combination of speculation and replication is typically most efficient.« less

Speculation and replication in temperature accelerated dynamics

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

Zamora, Richard J.; Perez, Danny; Voter, Arthur F.

Accelerated Molecular Dynamics (AMD) is a class of MD-based algorithms for the long-time scale simulation of atomistic systems that are characterized by rare-event transitions. Temperature-Accelerated Dynamics (TAD), a traditional AMD approach, hastens state-to-state transitions by performing MD at an elevated temperature. Recently, Speculatively-Parallel TAD (SpecTAD) was introduced, allowing the TAD procedure to exploit parallel computing systems by concurrently executing in a dynamically generated list of speculative future states. Although speculation can be very powerful, it is not always the most efficient use of parallel resources. In this paper, we compare the performance of speculative parallelism with a replica-based technique, similarmore » to the Parallel Replica Dynamics method. A hybrid SpecTAD approach is also presented, in which each speculation process is further accelerated by a local set of replicas. Finally and overall, this work motivates the use of hybrid parallelism whenever possible, as some combination of speculation and replication is typically most efficient.« less

Semantics-based distributed I/O with the ParaMEDIC framework.

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

Balaji, P.; Feng, W.; Lin, H.

2008-01-01

Many large-scale applications simultaneously rely on multiple resources for efficient execution. For example, such applications may require both large compute and storage resources; however, very few supercomputing centers can provide large quantities of both. Thus, data generated at the compute site oftentimes has to be moved to a remote storage site for either storage or visualization and analysis. Clearly, this is not an efficient model, especially when the two sites are distributed over a wide-area network. Thus, we present a framework called 'ParaMEDIC: Parallel Metadata Environment for Distributed I/O and Computing' which uses application-specific semantic information to convert the generatedmore » data to orders-of-magnitude smaller metadata at the compute site, transfer the metadata to the storage site, and re-process the metadata at the storage site to regenerate the output. Specifically, ParaMEDIC trades a small amount of additional computation (in the form of data post-processing) for a potentially significant reduction in data that needs to be transferred in distributed environments.« less

A Parallel Pipelined Renderer for the Time-Varying Volume Data

NASA Technical Reports Server (NTRS)

Chiueh, Tzi-Cker; Ma, Kwan-Liu

1997-01-01

This paper presents a strategy for efficiently rendering time-varying volume data sets on a distributed-memory parallel computer. Time-varying volume data take large storage space and visualizing them requires reading large files continuously or periodically throughout the course of the visualization process. Instead of using all the processors to collectively render one volume at a time, a pipelined rendering process is formed by partitioning processors into groups to render multiple volumes concurrently. In this way, the overall rendering time may be greatly reduced because the pipelined rendering tasks are overlapped with the I/O required to load each volume into a group of processors; moreover, parallelization overhead may be reduced as a result of partitioning the processors. We modify an existing parallel volume renderer to exploit various levels of rendering parallelism and to study how the partitioning of processors may lead to optimal rendering performance. Two factors which are important to the overall execution time are re-source utilization efficiency and pipeline startup latency. The optimal partitioning configuration is the one that balances these two factors. Tests on Intel Paragon computers show that in general optimal partitionings do exist for a given rendering task and result in 40-50% saving in overall rendering time.

System Level Applications of Adaptive Computing (SLAAC)

DTIC Science & Technology

2003-11-01

saved clock cycles, as the computation cycle time was directly proportional to the number of bitplanes in the image. The simulation was undertaken in...S-1][D -1] SK E W E R [k+K S-1][0] SK E W E R [k+K S-1][1] MinMax MinMax MinMax Min - IdxMin Max - IdxMax 0 Figure 3: PPI algorithm architeture ...parallel processing of data. The total throughput in these extended architectures is directly proportional to the amount of resources (CLB slices

Optimization of coupled systems: A critical overview of approaches

NASA Technical Reports Server (NTRS)

Balling, R. J.; Sobieszczanski-Sobieski, J.

1994-01-01

A unified overview is given of problem formulation approaches for the optimization of multidisciplinary coupled systems. The overview includes six fundamental approaches upon which a large number of variations may be made. Consistent approach names and a compact approach notation are given. The approaches are formulated to apply to general nonhierarchic systems. The approaches are compared both from a computational viewpoint and a managerial viewpoint. Opportunities for parallelism of both computation and manpower resources are discussed. Recommendations regarding the need for future research are advanced.

Parallelisation study of a three-dimensional environmental flow model

NASA Astrophysics Data System (ADS)

O'Donncha, Fearghal; Ragnoli, Emanuele; Suits, Frank

2014-03-01

There are many simulation codes in the geosciences that are serial and cannot take advantage of the parallel computational resources commonly available today. One model important for our work in coastal ocean current modelling is EFDC, a Fortran 77 code configured for optimal deployment on vector computers. In order to take advantage of our cache-based, blade computing system we restructured EFDC from serial to parallel, thereby allowing us to run existing models more quickly, and to simulate larger and more detailed models that were previously impractical. Since the source code for EFDC is extensive and involves detailed computation, it is important to do such a port in a manner that limits changes to the files, while achieving the desired speedup. We describe a parallelisation strategy involving surgical changes to the source files to minimise error-prone alteration of the underlying computations, while allowing load-balanced domain decomposition for efficient execution on a commodity cluster. The use of conjugate gradient posed particular challenges due to implicit non-local communication posing a hindrance to standard domain partitioning schemes; a number of techniques are discussed to address this in a feasible, computationally efficient manner. The parallel implementation demonstrates good scalability in combination with a novel domain partitioning scheme that specifically handles mixed water/land regions commonly found in coastal simulations. The approach presented here represents a practical methodology to rejuvenate legacy code on a commodity blade cluster with reasonable effort; our solution has direct application to other similar codes in the geosciences.

Wakefield computations for a corrugated pipe as a beam dechirper for FEL applications

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

Ng, C. K.; Bane, K. L.F.

A beam “dechirper” based on a corrugated, metallic vacuum chamber has been proposed recently to cancel residual energy chirp in a beam before it enters the undulator in a linac-based X-ray FEL. Rather than the round geometry that was originally proposed, we consider a pipe composed of two parallel plates with corrugations. The advantage is that the strength of the wake effect can be tuned by adjusting the separation of the plates. The separation of the plates is on the order of millimeters, and the corrugations are fractions of a millimeter in size. The dechirper needs to be meters longmore » in order to provide sufficient longitudinal wakefield to cancel the beam chirp. Considerable computation resources are required to determine accurately the wakefield for such a long structure with small corrugation gaps. Combining the moving window technique and parallel computing using multiple processors, the time domain module in the parallel finite-element electromagnetic suite ACE3P allows efficient determination of the wakefield through convergence studies. In this paper, we will calculate the longitudinal, dipole and quadrupole wakefields for the dechirper and compare the results with those of analytical and field matching approaches.« less

Serial Back-Plane Technologies in Advanced Avionics Architectures

NASA Technical Reports Server (NTRS)

Varnavas, Kosta

2005-01-01

Current back plane technologies such as VME, and current personal computer back planes such as PCI, are shared bus systems that can exhibit nondeterministic latencies. This means a card can take control of the bus and use resources indefinitely affecting the ability of other cards in the back plane to acquire the bus. This provides a real hit on the reliability of the system. Additionally, these parallel busses only have bandwidths in the 100s of megahertz range and EMI and noise effects get worse the higher the bandwidth goes. To provide scalable, fault-tolerant, advanced computing systems, more applicable to today s connected computing environment and to better meet the needs of future requirements for advanced space instruments and vehicles, serial back-plane technologies should be implemented in advanced avionics architectures. Serial backplane technologies eliminate the problem of one card getting the bus and never relinquishing it, or one minor problem on the backplane bringing the whole system down. Being serial instead of parallel improves the reliability by reducing many of the signal integrity issues associated with parallel back planes and thus significantly improves reliability. The increased speeds associated with a serial backplane are an added bonus.

Accelerating statistical image reconstruction algorithms for fan-beam x-ray CT using cloud computing

NASA Astrophysics Data System (ADS)

Srivastava, Somesh; Rao, A. Ravishankar; Sheinin, Vadim

2011-03-01

Statistical image reconstruction algorithms potentially offer many advantages to x-ray computed tomography (CT), e.g. lower radiation dose. But, their adoption in practical CT scanners requires extra computation power, which is traditionally provided by incorporating additional computing hardware (e.g. CPU-clusters, GPUs, FPGAs etc.) into a scanner. An alternative solution is to access the required computation power over the internet from a cloud computing service, which is orders-of-magnitude more cost-effective. This is because users only pay a small pay-as-you-go fee for the computation resources used (i.e. CPU time, storage etc.), and completely avoid purchase, maintenance and upgrade costs. In this paper, we investigate the benefits and shortcomings of using cloud computing for statistical image reconstruction. We parallelized the most time-consuming parts of our application, the forward and back projectors, using MapReduce, the standard parallelization library on clouds. From preliminary investigations, we found that a large speedup is possible at a very low cost. But, communication overheads inside MapReduce can limit the maximum speedup, and a better MapReduce implementation might become necessary in the future. All the experiments for this paper, including development and testing, were completed on the Amazon Elastic Compute Cloud (EC2) for less than $20.

Accelerating the Pace of Protein Functional Annotation With Intel Xeon Phi Coprocessors.

PubMed

Feinstein, Wei P; Moreno, Juana; Jarrell, Mark; Brylinski, Michal

2015-06-01

Intel Xeon Phi is a new addition to the family of powerful parallel accelerators. The range of its potential applications in computationally driven research is broad; however, at present, the repository of scientific codes is still relatively limited. In this study, we describe the development and benchmarking of a parallel version of eFindSite, a structural bioinformatics algorithm for the prediction of ligand-binding sites in proteins. Implemented for the Intel Xeon Phi platform, the parallelization of the structure alignment portion of eFindSite using pragma-based OpenMP brings about the desired performance improvements, which scale well with the number of computing cores. Compared to a serial version, the parallel code runs 11.8 and 10.1 times faster on the CPU and the coprocessor, respectively; when both resources are utilized simultaneously, the speedup is 17.6. For example, ligand-binding predictions for 501 benchmarking proteins are completed in 2.1 hours on a single Stampede node equipped with the Intel Xeon Phi card compared to 3.1 hours without the accelerator and 36.8 hours required by a serial version. In addition to the satisfactory parallel performance, porting existing scientific codes to the Intel Xeon Phi architecture is relatively straightforward with a short development time due to the support of common parallel programming models by the coprocessor. The parallel version of eFindSite is freely available to the academic community at www.brylinski.org/efindsite.

Perspectives on the Future of CFD

NASA Technical Reports Server (NTRS)

Kwak, Dochan

2000-01-01

This viewgraph presentation gives an overview of the future of computational fluid dynamics (CFD), which in the past has pioneered the field of flow simulation. Over time CFD has progressed as computing power. Numerical methods have been advanced as CPU and memory capacity increases. Complex configurations are routinely computed now and direct numerical simulations (DNS) and large eddy simulations (LES) are used to study turbulence. As the computing resources changed to parallel and distributed platforms, computer science aspects such as scalability (algorithmic and implementation) and portability and transparent codings have advanced. Examples of potential future (or current) challenges include risk assessment, limitations of the heuristic model, and the development of CFD and information technology (IT) tools.

Application of computational aero-acoustics to real world problems

NASA Technical Reports Server (NTRS)

Hardin, Jay C.

1996-01-01

The application of computational aeroacoustics (CAA) to real problems is discussed in relation to the analysis performed with the aim of assessing the application of the various techniques. It is considered that the applications are limited by the inability of the computational resources to resolve the large range of scales involved in high Reynolds number flows. Possible simplifications are discussed. It is considered that problems remain to be solved in relation to the efficient use of the power of parallel computers and in the development of turbulent modeling schemes. The goal of CAA is stated as being the implementation of acoustic design studies on a computer terminal with reasonable run times.

Distributed computations in a dynamic, heterogeneous Grid environment

NASA Astrophysics Data System (ADS)

Dramlitsch, Thomas

2003-06-01

In order to face the rapidly increasing need for computational resources of various scientific and engineering applications one has to think of new ways to make more efficient use of the worlds current computational resources. In this respect, the growing speed of wide area networks made a new kind of distributed computing possible: Metacomputing or (distributed) Grid computing. This is a rather new and uncharted field in computational science. The rapidly increasing speed of networks even outperforms the average increase of processor speed: Processor speeds double on average each 18 month whereas network bandwidths double every 9 months. Due to this development of local and wide area networks Grid computing will certainly play a key role in the future of parallel computing. This type of distributed computing, however, distinguishes from the traditional parallel computing in many ways since it has to deal with many problems not occurring in classical parallel computing. Those problems are for example heterogeneity, authentication and slow networks to mention only a few. Some of those problems, e.g. the allocation of distributed resources along with the providing of information about these resources to the application have been already attacked by the Globus software. Unfortunately, as far as we know, hardly any application or middle-ware software takes advantage of this information, since most parallelizing algorithms for finite differencing codes are implicitly designed for single supercomputer or cluster execution. We show that although it is possible to apply classical parallelizing algorithms in a Grid environment, in most cases the observed efficiency of the executed code is very poor. In this work we are closing this gap. In our thesis, we will - show that an execution of classical parallel codes in Grid environments is possible but very slow - analyze this situation of bad performance, nail down bottlenecks in communication, remove unnecessary overhead and other reasons for low performance - develop new and advanced algorithms for parallelisation that are aware of a Grid environment in order to generelize the traditional parallelization schemes - implement and test these new methods, replace and compare with the classical ones - introduce dynamic strategies that automatically adapt the running code to the nature of the underlying Grid environment. The higher the performance one can achieve for a single application by manual tuning for a Grid environment, the lower the chance that those changes are widely applicable to other programs. In our analysis as well as in our implementation we tried to keep the balance between high performance and generality. None of our changes directly affect code on the application level which makes our algorithms applicable to a whole class of real world applications. The implementation of our work is done within the Cactus framework using the Globus toolkit, since we think that these are the most reliable and advanced programming frameworks for supporting computations in Grid environments. On the other hand, however, we tried to be as general as possible, i.e. all methods and algorithms discussed in this thesis are independent of Cactus or Globus. Die immer dichtere und schnellere Vernetzung von Rechnern und Rechenzentren über Hochgeschwindigkeitsnetzwerke ermöglicht eine neue Art des wissenschaftlich verteilten Rechnens, bei der geographisch weit auseinanderliegende Rechenkapazitäten zu einer Gesamtheit zusammengefasst werden können. Dieser so entstehende virtuelle Superrechner, der selbst aus mehreren Grossrechnern besteht, kann dazu genutzt werden Probleme zu berechnen, für die die einzelnen Grossrechner zu klein sind. Die Probleme, die numerisch mit heutigen Rechenkapazitäten nicht lösbar sind, erstrecken sich durch sämtliche Gebiete der heutigen Wissenschaft, angefangen von Astrophysik, Molekülphysik, Bioinformatik, Meteorologie, bis hin zur Zahlentheorie und Fluiddynamik um nur einige Gebiete zu nennen. Je nach Art der Problemstellung und des Lösungsverfahrens gestalten sich solche "Meta-Berechnungen" mehr oder weniger schwierig. Allgemein kann man sagen, dass solche Berechnungen um so schwerer und auch um so uneffizienter werden, je mehr Kommunikation zwischen den einzelnen Prozessen (oder Prozessoren) herrscht. Dies ist dadurch begründet, dass die Bandbreiten bzw. Latenzzeiten zwischen zwei Prozessoren auf demselben Grossrechner oder Cluster um zwei bis vier Grössenordnungen höher bzw. niedriger liegen als zwischen Prozessoren, welche hunderte von Kilometern entfernt liegen. Dennoch bricht nunmehr eine Zeit an, in der es möglich ist Berechnungen auf solch virtuellen Supercomputern auch mit kommunikationsintensiven Programmen durchzuführen. Eine grosse Klasse von kommunikations- und berechnungsintensiven Programmen ist diejenige, die die Lösung von Differentialgleichungen mithilfe von finiten Differenzen zum Inhalt hat. Gerade diese Klasse von Programmen und deren Betrieb in einem virtuellen Superrechner wird in dieser vorliegenden Dissertation behandelt. Methoden zur effizienteren Durchführung von solch verteilten Berechnungen werden entwickelt, analysiert und implementiert. Der Schwerpunkt liegt darin vorhandene, klassische Parallelisierungsalgorithmen zu analysieren und so zu erweitern, dass sie vorhandene Informationen (z.B. verfügbar durch das Globus Toolkit) über Maschinen und Netzwerke zur effizienteren Parallelisierung nutzen. Soweit wir wissen werden solche Zusatzinformationen kaum in relevanten Programmen genutzt, da der Grossteil aller Parallelisierungsalgorithmen implizit für die Ausführung auf Grossrechnern oder Clustern entwickelt wurde.

Short-term Temperature Prediction Using Adaptive Computing on Dynamic Scales

NASA Astrophysics Data System (ADS)

Hu, W.; Cervone, G.; Jha, S.; Balasubramanian, V.; Turilli, M.

2017-12-01

When predicting temperature, there are specific places and times when high accuracy predictions are harder. For example, not all the sub-regions in the domain require the same amount of computing resources to generate an accurate prediction. Plateau areas might require less computing resources than mountainous areas because of the steeper gradient of temperature change in the latter. However, it is difficult to estimate beforehand the optimal allocation of computational resources because several parameters play a role in determining the accuracy of the forecasts, in addition to orography. The allocation of resources to perform simulations can become a bottleneck because it requires human intervention to stop jobs or start new ones. The goal of this project is to design and develop a dynamic approach to generate short-term temperature predictions that can automatically determines the required computing resources and the geographic scales of the predictions based on the spatial and temporal uncertainties. The predictions and the prediction quality metrics are computed using a numeric weather prediction model, Analog Ensemble (AnEn), and the parallelization on high performance computing systems is accomplished using Ensemble Toolkit, one component of the RADICAL-Cybertools family of tools. RADICAL-Cybertools decouple the science needs from the computational capabilities by building an intermediate layer to run general ensemble patterns, regardless of the science. In this research, we show how the ensemble toolkit allows generating high resolution temperature forecasts at different spatial and temporal resolution. The AnEn algorithm is run using NAM analysis and forecasts data for the continental United States for a period of 2 years. AnEn results show that temperature forecasts perform well according to different probabilistic and deterministic statistical tests.

Density-based parallel skin lesion border detection with webCL

PubMed Central

2015-01-01

Background Dermoscopy is a highly effective and noninvasive imaging technique used in diagnosis of melanoma and other pigmented skin lesions. Many aspects of the lesion under consideration are defined in relation to the lesion border. This makes border detection one of the most important steps in dermoscopic image analysis. In current practice, dermatologists often delineate borders through a hand drawn representation based upon visual inspection. Due to the subjective nature of this technique, intra- and inter-observer variations are common. Because of this, the automated assessment of lesion borders in dermoscopic images has become an important area of study. Methods Fast density based skin lesion border detection method has been implemented in parallel with a new parallel technology called WebCL. WebCL utilizes client side computing capabilities to use available hardware resources such as multi cores and GPUs. Developed WebCL-parallel density based skin lesion border detection method runs efficiently from internet browsers. Results Previous research indicates that one of the highest accuracy rates can be achieved using density based clustering techniques for skin lesion border detection. While these algorithms do have unfavorable time complexities, this effect could be mitigated when implemented in parallel. In this study, density based clustering technique for skin lesion border detection is parallelized and redesigned to run very efficiently on the heterogeneous platforms (e.g. tablets, SmartPhones, multi-core CPUs, GPUs, and fully-integrated Accelerated Processing Units) by transforming the technique into a series of independent concurrent operations. Heterogeneous computing is adopted to support accessibility, portability and multi-device use in the clinical settings. For this, we used WebCL, an emerging technology that enables a HTML5 Web browser to execute code in parallel for heterogeneous platforms. We depicted WebCL and our parallel algorithm design. In addition, we tested parallel code on 100 dermoscopy images and showed the execution speedups with respect to the serial version. Results indicate that parallel (WebCL) version and serial version of density based lesion border detection methods generate the same accuracy rates for 100 dermoscopy images, in which mean of border error is 6.94%, mean of recall is 76.66%, and mean of precision is 99.29% respectively. Moreover, WebCL version's speedup factor for 100 dermoscopy images' lesion border detection averages around ~491.2. Conclusions When large amount of high resolution dermoscopy images considered in a usual clinical setting along with the critical importance of early detection and diagnosis of melanoma before metastasis, the importance of fast processing dermoscopy images become obvious. In this paper, we introduce WebCL and the use of it for biomedical image processing applications. WebCL is a javascript binding of OpenCL, which takes advantage of GPU computing from a web browser. Therefore, WebCL parallel version of density based skin lesion border detection introduced in this study can supplement expert dermatologist, and aid them in early diagnosis of skin lesions. While WebCL is currently an emerging technology, a full adoption of WebCL into the HTML5 standard would allow for this implementation to run on a very large set of hardware and software systems. WebCL takes full advantage of parallel computational resources including multi-cores and GPUs on a local machine, and allows for compiled code to run directly from the Web Browser. PMID:26423836

Density-based parallel skin lesion border detection with webCL.

PubMed

Lemon, James; Kockara, Sinan; Halic, Tansel; Mete, Mutlu

2015-01-01

Dermoscopy is a highly effective and noninvasive imaging technique used in diagnosis of melanoma and other pigmented skin lesions. Many aspects of the lesion under consideration are defined in relation to the lesion border. This makes border detection one of the most important steps in dermoscopic image analysis. In current practice, dermatologists often delineate borders through a hand drawn representation based upon visual inspection. Due to the subjective nature of this technique, intra- and inter-observer variations are common. Because of this, the automated assessment of lesion borders in dermoscopic images has become an important area of study. Fast density based skin lesion border detection method has been implemented in parallel with a new parallel technology called WebCL. WebCL utilizes client side computing capabilities to use available hardware resources such as multi cores and GPUs. Developed WebCL-parallel density based skin lesion border detection method runs efficiently from internet browsers. Previous research indicates that one of the highest accuracy rates can be achieved using density based clustering techniques for skin lesion border detection. While these algorithms do have unfavorable time complexities, this effect could be mitigated when implemented in parallel. In this study, density based clustering technique for skin lesion border detection is parallelized and redesigned to run very efficiently on the heterogeneous platforms (e.g. tablets, SmartPhones, multi-core CPUs, GPUs, and fully-integrated Accelerated Processing Units) by transforming the technique into a series of independent concurrent operations. Heterogeneous computing is adopted to support accessibility, portability and multi-device use in the clinical settings. For this, we used WebCL, an emerging technology that enables a HTML5 Web browser to execute code in parallel for heterogeneous platforms. We depicted WebCL and our parallel algorithm design. In addition, we tested parallel code on 100 dermoscopy images and showed the execution speedups with respect to the serial version. Results indicate that parallel (WebCL) version and serial version of density based lesion border detection methods generate the same accuracy rates for 100 dermoscopy images, in which mean of border error is 6.94%, mean of recall is 76.66%, and mean of precision is 99.29% respectively. Moreover, WebCL version's speedup factor for 100 dermoscopy images' lesion border detection averages around ~491.2. When large amount of high resolution dermoscopy images considered in a usual clinical setting along with the critical importance of early detection and diagnosis of melanoma before metastasis, the importance of fast processing dermoscopy images become obvious. In this paper, we introduce WebCL and the use of it for biomedical image processing applications. WebCL is a javascript binding of OpenCL, which takes advantage of GPU computing from a web browser. Therefore, WebCL parallel version of density based skin lesion border detection introduced in this study can supplement expert dermatologist, and aid them in early diagnosis of skin lesions. While WebCL is currently an emerging technology, a full adoption of WebCL into the HTML5 standard would allow for this implementation to run on a very large set of hardware and software systems. WebCL takes full advantage of parallel computational resources including multi-cores and GPUs on a local machine, and allows for compiled code to run directly from the Web Browser.

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

Teixeira De Queiroz, F.

There is a game called languages and dreams. It is a game for a programmer alone. The sole objective is to determine which computing resources a programmer would most like to see become part of the language that he uses. Obviously every programmer wants new possibilities, but normally they do not get put down on paper. The author proposes a nesting system for parallel processing. 4 references.

CUDAMPF: a multi-tiered parallel framework for accelerating protein sequence search in HMMER on CUDA-enabled GPU.

PubMed

Jiang, Hanyu; Ganesan, Narayan

2016-02-27

HMMER software suite is widely used for analysis of homologous protein and nucleotide sequences with high sensitivity. The latest version of hmmsearch in HMMER 3.x, utilizes heuristic-pipeline which consists of MSV/SSV (Multiple/Single ungapped Segment Viterbi) stage, P7Viterbi stage and the Forward scoring stage to accelerate homology detection. Since the latest version is highly optimized for performance on modern multi-core CPUs with SSE capabilities, only a few acceleration attempts report speedup. However, the most compute intensive tasks within the pipeline (viz., MSV/SSV and P7Viterbi stages) still stand to benefit from the computational capabilities of massively parallel processors. A Multi-Tiered Parallel Framework (CUDAMPF) implemented on CUDA-enabled GPUs presented here, offers a finer-grained parallelism for MSV/SSV and Viterbi algorithms. We couple SIMT (Single Instruction Multiple Threads) mechanism with SIMD (Single Instructions Multiple Data) video instructions with warp-synchronism to achieve high-throughput processing and eliminate thread idling. We also propose a hardware-aware optimal allocation scheme of scarce resources like on-chip memory and caches in order to boost performance and scalability of CUDAMPF. In addition, runtime compilation via NVRTC available with CUDA 7.0 is incorporated into the presented framework that not only helps unroll innermost loop to yield upto 2 to 3-fold speedup than static compilation but also enables dynamic loading and switching of kernels depending on the query model size, in order to achieve optimal performance. CUDAMPF is designed as a hardware-aware parallel framework for accelerating computational hotspots within the hmmsearch pipeline as well as other sequence alignment applications. It achieves significant speedup by exploiting hierarchical parallelism on single GPU and takes full advantage of limited resources based on their own performance features. In addition to exceeding performance of other acceleration attempts, comprehensive evaluations against high-end CPUs (Intel i5, i7 and Xeon) shows that CUDAMPF yields upto 440 GCUPS for SSV, 277 GCUPS for MSV and 14.3 GCUPS for P7Viterbi all with 100 % accuracy, which translates to a maximum speedup of 37.5, 23.1 and 11.6-fold for MSV, SSV and P7Viterbi respectively. The source code is available at https://github.com/Super-Hippo/CUDAMPF.

Experimental Realization of a Quantum Support Vector Machine

NASA Astrophysics Data System (ADS)

Li, Zhaokai; Liu, Xiaomei; Xu, Nanyang; Du, Jiangfeng

2015-04-01

The fundamental principle of artificial intelligence is the ability of machines to learn from previous experience and do future work accordingly. In the age of big data, classical learning machines often require huge computational resources in many practical cases. Quantum machine learning algorithms, on the other hand, could be exponentially faster than their classical counterparts by utilizing quantum parallelism. Here, we demonstrate a quantum machine learning algorithm to implement handwriting recognition on a four-qubit NMR test bench. The quantum machine learns standard character fonts and then recognizes handwritten characters from a set with two candidates. Because of the wide spread importance of artificial intelligence and its tremendous consumption of computational resources, quantum speedup would be extremely attractive against the challenges of big data.

CoreTSAR: Core Task-Size Adapting Runtime

DOE PAGES

Scogland, Thomas R. W.; Feng, Wu-chun; Rountree, Barry; ...

2014-10-27

Heterogeneity continues to increase at all levels of computing, with the rise of accelerators such as GPUs, FPGAs, and other co-processors into everything from desktops to supercomputers. As a consequence, efficiently managing such disparate resources has become increasingly complex. CoreTSAR seeks to reduce this complexity by adaptively worksharing parallel-loop regions across compute resources without requiring any transformation of the code within the loop. Lastly, our results show performance improvements of up to three-fold over a current state-of-the-art heterogeneous task scheduler as well as linear performance scaling from a single GPU to four GPUs for many codes. In addition, CoreTSAR demonstratesmore » a robust ability to adapt to both a variety of workloads and underlying system configurations.« less

A CPU/MIC Collaborated Parallel Framework for GROMACS on Tianhe-2 Supercomputer.

PubMed

Peng, Shaoliang; Yang, Shunyun; Su, Wenhe; Zhang, Xiaoyu; Zhang, Tenglilang; Liu, Weiguo; Zhao, Xingming

2017-06-16

Molecular Dynamics (MD) is the simulation of the dynamic behavior of atoms and molecules. As the most popular software for molecular dynamics, GROMACS cannot work on large-scale data because of limit computing resources. In this paper, we propose a CPU and Intel® Xeon Phi Many Integrated Core (MIC) collaborated parallel framework to accelerate GROMACS using the offload mode on a MIC coprocessor, with which the performance of GROMACS is improved significantly, especially with the utility of Tianhe-2 supercomputer. Furthermore, we optimize GROMACS so that it can run on both the CPU and MIC at the same time. In addition, we accelerate multi-node GROMACS so that it can be used in practice. Benchmarking on real data, our accelerated GROMACS performs very well and reduces computation time significantly. Source code: https://github.com/tianhe2/gromacs-mic.

Accelerating electron tomography reconstruction algorithm ICON with GPU.

PubMed

Chen, Yu; Wang, Zihao; Zhang, Jingrong; Li, Lun; Wan, Xiaohua; Sun, Fei; Zhang, Fa

2017-01-01

Electron tomography (ET) plays an important role in studying in situ cell ultrastructure in three-dimensional space. Due to limited tilt angles, ET reconstruction always suffers from the "missing wedge" problem. With a validation procedure, iterative compressed-sensing optimized NUFFT reconstruction (ICON) demonstrates its power in the restoration of validated missing information for low SNR biological ET dataset. However, the huge computational demand has become a major problem for the application of ICON. In this work, we analyzed the framework of ICON and classified the operations of major steps of ICON reconstruction into three types. Accordingly, we designed parallel strategies and implemented them on graphics processing units (GPU) to generate a parallel program ICON-GPU. With high accuracy, ICON-GPU has a great acceleration compared to its CPU version, up to 83.7×, greatly relieving ICON's dependence on computing resource.

PARALLEL HOP: A SCALABLE HALO FINDER FOR MASSIVE COSMOLOGICAL DATA SETS

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

Skory, Stephen; Turk, Matthew J.; Norman, Michael L.

2010-11-15

Modern N-body cosmological simulations contain billions (10{sup 9}) of dark matter particles. These simulations require hundreds to thousands of gigabytes of memory and employ hundreds to tens of thousands of processing cores on many compute nodes. In order to study the distribution of dark matter in a cosmological simulation, the dark matter halos must be identified using a halo finder, which establishes the halo membership of every particle in the simulation. The resources required for halo finding are similar to the requirements for the simulation itself. In particular, simulations have become too extensive to use commonly employed halo finders, suchmore » that the computational requirements to identify halos must now be spread across multiple nodes and cores. Here, we present a scalable-parallel halo finding method called Parallel HOP for large-scale cosmological simulation data. Based on the halo finder HOP, it utilizes message passing interface and domain decomposition to distribute the halo finding workload across multiple compute nodes, enabling analysis of much larger data sets than is possible with the strictly serial or previous parallel implementations of HOP. We provide a reference implementation of this method as a part of the toolkit {sup yt}, an analysis toolkit for adaptive mesh refinement data that include complementary analysis modules. Additionally, we discuss a suite of benchmarks that demonstrate that this method scales well up to several hundred tasks and data sets in excess of 2000{sup 3} particles. The Parallel HOP method and our implementation can be readily applied to any kind of N-body simulation data and is therefore widely applicable.« less

Efficient Machine Learning Approach for Optimizing Scientific Computing Applications on Emerging HPC Architectures

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

Arumugam, Kamesh

Efficient parallel implementations of scientific applications on multi-core CPUs with accelerators such as GPUs and Xeon Phis is challenging. This requires - exploiting the data parallel architecture of the accelerator along with the vector pipelines of modern x86 CPU architectures, load balancing, and efficient memory transfer between different devices. It is relatively easy to meet these requirements for highly structured scientific applications. In contrast, a number of scientific and engineering applications are unstructured. Getting performance on accelerators for these applications is extremely challenging because many of these applications employ irregular algorithms which exhibit data-dependent control-ow and irregular memory accesses. Furthermore,more » these applications are often iterative with dependency between steps, and thus making it hard to parallelize across steps. As a result, parallelism in these applications is often limited to a single step. Numerical simulation of charged particles beam dynamics is one such application where the distribution of work and memory access pattern at each time step is irregular. Applications with these properties tend to present significant branch and memory divergence, load imbalance between different processor cores, and poor compute and memory utilization. Prior research on parallelizing such irregular applications have been focused around optimizing the irregular, data-dependent memory accesses and control-ow during a single step of the application independent of the other steps, with the assumption that these patterns are completely unpredictable. We observed that the structure of computation leading to control-ow divergence and irregular memory accesses in one step is similar to that in the next step. It is possible to predict this structure in the current step by observing the computation structure of previous steps. In this dissertation, we present novel machine learning based optimization techniques to address the parallel implementation challenges of such irregular applications on different HPC architectures. In particular, we use supervised learning to predict the computation structure and use it to address the control-ow and memory access irregularities in the parallel implementation of such applications on GPUs, Xeon Phis, and heterogeneous architectures composed of multi-core CPUs with GPUs or Xeon Phis. We use numerical simulation of charged particles beam dynamics simulation as a motivating example throughout the dissertation to present our new approach, though they should be equally applicable to a wide range of irregular applications. The machine learning approach presented here use predictive analytics and forecasting techniques to adaptively model and track the irregular memory access pattern at each time step of the simulation to anticipate the future memory access pattern. Access pattern forecasts can then be used to formulate optimization decisions during application execution which improves the performance of the application at a future time step based on the observations from earlier time steps. In heterogeneous architectures, forecasts can also be used to improve the memory performance and resource utilization of all the processing units to deliver a good aggregate performance. We used these optimization techniques and anticipation strategy to design a cache-aware, memory efficient parallel algorithm to address the irregularities in the parallel implementation of charged particles beam dynamics simulation on different HPC architectures. Experimental result using a diverse mix of HPC architectures shows that our approach in using anticipation strategy is effective in maximizing data reuse, ensuring workload balance, minimizing branch and memory divergence, and in improving resource utilization.« less

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.

High-performance parallel computing in the classroom using the public goods game as an example

NASA Astrophysics Data System (ADS)

Perc, Matjaž

2017-07-01

The use of computers in statistical physics is common because the sheer number of equations that describe the behaviour of an entire system particle by particle often makes it impossible to solve them exactly. Monte Carlo methods form a particularly important class of numerical methods for solving problems in statistical physics. Although these methods are simple in principle, their proper use requires a good command of statistical mechanics, as well as considerable computational resources. The aim of this paper is to demonstrate how the usage of widely accessible graphics cards on personal computers can elevate the computing power in Monte Carlo simulations by orders of magnitude, thus allowing live classroom demonstration of phenomena that would otherwise be out of reach. As an example, we use the public goods game on a square lattice where two strategies compete for common resources in a social dilemma situation. We show that the second-order phase transition to an absorbing phase in the system belongs to the directed percolation universality class, and we compare the time needed to arrive at this result by means of the main processor and by means of a suitable graphics card. Parallel computing on graphics processing units has been developed actively during the last decade, to the point where today the learning curve for entry is anything but steep for those familiar with programming. The subject is thus ripe for inclusion in graduate and advanced undergraduate curricula, and we hope that this paper will facilitate this process in the realm of physics education. To that end, we provide a documented source code for an easy reproduction of presented results and for further development of Monte Carlo simulations of similar systems.

A tool for simulating parallel branch-and-bound methods

NASA Astrophysics Data System (ADS)

Golubeva, Yana; Orlov, Yury; Posypkin, Mikhail

2016-01-01

The Branch-and-Bound method is known as one of the most powerful but very resource consuming global optimization methods. Parallel and distributed computing can efficiently cope with this issue. The major difficulty in parallel B&B method is the need for dynamic load redistribution. Therefore design and study of load balancing algorithms is a separate and very important research topic. This paper presents a tool for simulating parallel Branchand-Bound method. The simulator allows one to run load balancing algorithms with various numbers of processors, sizes of the search tree, the characteristics of the supercomputer's interconnect thereby fostering deep study of load distribution strategies. The process of resolution of the optimization problem by B&B method is replaced by a stochastic branching process. Data exchanges are modeled using the concept of logical time. The user friendly graphical interface to the simulator provides efficient visualization and convenient performance analysis.

Parallel computational fluid dynamics '91; Conference Proceedings, Stuttgart, Germany, Jun. 10-12, 1991

NASA Technical Reports Server (NTRS)

Reinsch, K. G. (Editor); Schmidt, W. (Editor); Ecer, A. (Editor); Haeuser, Jochem (Editor); Periaux, J. (Editor)

1992-01-01

A conference was held on parallel computational fluid dynamics and produced related papers. Topics discussed in these papers include: parallel implicit and explicit solvers for compressible flow, parallel computational techniques for Euler and Navier-Stokes equations, grid generation techniques for parallel computers, and aerodynamic simulation om massively parallel systems.

A bioinformatics knowledge discovery in text application for grid computing

PubMed Central

Castellano, Marcello; Mastronardi, Giuseppe; Bellotti, Roberto; Tarricone, Gianfranco

2009-01-01

Background A fundamental activity in biomedical research is Knowledge Discovery which has the ability to search through large amounts of biomedical information such as documents and data. High performance computational infrastructures, such as Grid technologies, are emerging as a possible infrastructure to tackle the intensive use of Information and Communication resources in life science. The goal of this work was to develop a software middleware solution in order to exploit the many knowledge discovery applications on scalable and distributed computing systems to achieve intensive use of ICT resources. Methods The development of a grid application for Knowledge Discovery in Text using a middleware solution based methodology is presented. The system must be able to: perform a user application model, process the jobs with the aim of creating many parallel jobs to distribute on the computational nodes. Finally, the system must be aware of the computational resources available, their status and must be able to monitor the execution of parallel jobs. These operative requirements lead to design a middleware to be specialized using user application modules. It included a graphical user interface in order to access to a node search system, a load balancing system and a transfer optimizer to reduce communication costs. Results A middleware solution prototype and the performance evaluation of it in terms of the speed-up factor is shown. It was written in JAVA on Globus Toolkit 4 to build the grid infrastructure based on GNU/Linux computer grid nodes. A test was carried out and the results are shown for the named entity recognition search of symptoms and pathologies. The search was applied to a collection of 5,000 scientific documents taken from PubMed. Conclusion In this paper we discuss the development of a grid application based on a middleware solution. It has been tested on a knowledge discovery in text process to extract new and useful information about symptoms and pathologies from a large collection of unstructured scientific documents. As an example a computation of Knowledge Discovery in Database was applied on the output produced by the KDT user module to extract new knowledge about symptom and pathology bio-entities. PMID:19534749

A bioinformatics knowledge discovery in text application for grid computing.

PubMed

Castellano, Marcello; Mastronardi, Giuseppe; Bellotti, Roberto; Tarricone, Gianfranco

2009-06-16

A fundamental activity in biomedical research is Knowledge Discovery which has the ability to search through large amounts of biomedical information such as documents and data. High performance computational infrastructures, such as Grid technologies, are emerging as a possible infrastructure to tackle the intensive use of Information and Communication resources in life science. The goal of this work was to develop a software middleware solution in order to exploit the many knowledge discovery applications on scalable and distributed computing systems to achieve intensive use of ICT resources. The development of a grid application for Knowledge Discovery in Text using a middleware solution based methodology is presented. The system must be able to: perform a user application model, process the jobs with the aim of creating many parallel jobs to distribute on the computational nodes. Finally, the system must be aware of the computational resources available, their status and must be able to monitor the execution of parallel jobs. These operative requirements lead to design a middleware to be specialized using user application modules. It included a graphical user interface in order to access to a node search system, a load balancing system and a transfer optimizer to reduce communication costs. A middleware solution prototype and the performance evaluation of it in terms of the speed-up factor is shown. It was written in JAVA on Globus Toolkit 4 to build the grid infrastructure based on GNU/Linux computer grid nodes. A test was carried out and the results are shown for the named entity recognition search of symptoms and pathologies. The search was applied to a collection of 5,000 scientific documents taken from PubMed. In this paper we discuss the development of a grid application based on a middleware solution. It has been tested on a knowledge discovery in text process to extract new and useful information about symptoms and pathologies from a large collection of unstructured scientific documents. As an example a computation of Knowledge Discovery in Database was applied on the output produced by the KDT user module to extract new knowledge about symptom and pathology bio-entities.

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.

A Cyber-ITS Framework for Massive Traffic Data Analysis Using Cyber Infrastructure

PubMed Central

Fontaine, Michael D.

2013-01-01

Traffic data is commonly collected from widely deployed sensors in urban areas. This brings up a new research topic, data-driven intelligent transportation systems (ITSs), which means to integrate heterogeneous traffic data from different kinds of sensors and apply it for ITS applications. This research, taking into consideration the significant increase in the amount of traffic data and the complexity of data analysis, focuses mainly on the challenge of solving data-intensive and computation-intensive problems. As a solution to the problems, this paper proposes a Cyber-ITS framework to perform data analysis on Cyber Infrastructure (CI), by nature parallel-computing hardware and software systems, in the context of ITS. The techniques of the framework include data representation, domain decomposition, resource allocation, and parallel processing. All these techniques are based on data-driven and application-oriented models and are organized as a component-and-workflow-based model in order to achieve technical interoperability and data reusability. A case study of the Cyber-ITS framework is presented later based on a traffic state estimation application that uses the fusion of massive Sydney Coordinated Adaptive Traffic System (SCATS) data and GPS data. The results prove that the Cyber-ITS-based implementation can achieve a high accuracy rate of traffic state estimation and provide a significant computational speedup for the data fusion by parallel computing. PMID:23766690

A Cyber-ITS framework for massive traffic data analysis using cyber infrastructure.

PubMed

Xia, Yingjie; Hu, Jia; Fontaine, Michael D

2013-01-01

Traffic data is commonly collected from widely deployed sensors in urban areas. This brings up a new research topic, data-driven intelligent transportation systems (ITSs), which means to integrate heterogeneous traffic data from different kinds of sensors and apply it for ITS applications. This research, taking into consideration the significant increase in the amount of traffic data and the complexity of data analysis, focuses mainly on the challenge of solving data-intensive and computation-intensive problems. As a solution to the problems, this paper proposes a Cyber-ITS framework to perform data analysis on Cyber Infrastructure (CI), by nature parallel-computing hardware and software systems, in the context of ITS. The techniques of the framework include data representation, domain decomposition, resource allocation, and parallel processing. All these techniques are based on data-driven and application-oriented models and are organized as a component-and-workflow-based model in order to achieve technical interoperability and data reusability. A case study of the Cyber-ITS framework is presented later based on a traffic state estimation application that uses the fusion of massive Sydney Coordinated Adaptive Traffic System (SCATS) data and GPS data. The results prove that the Cyber-ITS-based implementation can achieve a high accuracy rate of traffic state estimation and provide a significant computational speedup for the data fusion by parallel computing.

Computational Science in Armenia (Invited Talk)

NASA Astrophysics Data System (ADS)

Marandjian, H.; Shoukourian, Yu.

This survey is devoted to the development of informatics and computer science in Armenia. The results in theoretical computer science (algebraic models, solutions to systems of general form recursive equations, the methods of coding theory, pattern recognition and image processing), constitute the theoretical basis for developing problem-solving-oriented environments. As examples can be mentioned: a synthesizer of optimized distributed recursive programs, software tools for cluster-oriented implementations of two-dimensional cellular automata, a grid-aware web interface with advanced service trading for linear algebra calculations. In the direction of solving scientific problems that require high-performance computing resources, examples of completed projects include the field of physics (parallel computing of complex quantum systems), astrophysics (Armenian virtual laboratory), biology (molecular dynamics study of human red blood cell membrane), meteorology (implementing and evaluating the Weather Research and Forecast Model for the territory of Armenia). The overview also notes that the Institute for Informatics and Automation Problems of the National Academy of Sciences of Armenia has established a scientific and educational infrastructure, uniting computing clusters of scientific and educational institutions of the country and provides the scientific community with access to local and international computational resources, that is a strong support for computational science in Armenia.

HPC Insights, Fall 2011

DTIC Science & Technology

2011-01-01

Simulating Satellite Tracking Using Parallel Computing By Andrew Lindstrom ,University of Hawaii at Hilo — Mentors: Carl Holmberg, Maui High Performance...RDECOM) and his management team, RDECOM Deputy Director Gary Martin ; ARL Director John Miller; Communications- Electronics Research, Development...Saves Resources By Mike Knowles, ARL DSRC Site Lead, Lockheed Martin mode instead of full power down. The first phase of the EAS effort is an attempt

Final Report: PAGE: Policy Analytics Generation Engine

DTIC Science & Technology

2016-08-12

develop a parallel framework for it. We also developed policies and methods by which a group of defensive resources (e.g. checkpoints) could be...Sarit Kraus. Learning to Reveal Information in Repeated Human -Computer Negotiation, Human -Agent Interaction Design and Models Workshop 2012. 04-JUN...Joseph Keshet, Sarit Kraus. Predicting Human Strategic Decisions Using Facial Expressions, International Joint Conference on Artificial

Radiotherapy Monte Carlo simulation using cloud computing technology.

PubMed

Poole, C M; Cornelius, I; Trapp, J V; Langton, C M

2012-12-01

Cloud computing allows for vast computational resources to be leveraged quickly and easily in bursts as and when required. Here we describe a technique that allows for Monte Carlo radiotherapy dose calculations to be performed using GEANT4 and executed in the cloud, with relative simulation cost and completion time evaluated as a function of machine count. As expected, simulation completion time decreases as 1/n for n parallel machines, and relative simulation cost is found to be optimal where n is a factor of the total simulation time in hours. Using the technique, we demonstrate the potential usefulness of cloud computing as a solution for rapid Monte Carlo simulation for radiotherapy dose calculation without the need for dedicated local computer hardware as a proof of principal.

Integrating Grid Services into the Cray XT4 Environment

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

NERSC; Cholia, Shreyas; Lin, Hwa-Chun Wendy

2009-05-01

The 38640 core Cray XT4"Franklin" system at the National Energy Research Scientific Computing Center (NERSC) is a massively parallel resource available to Department of Energy researchers that also provides on-demand grid computing to the Open Science Grid. The integration of grid services on Franklin presented various challenges, including fundamental differences between the interactive and compute nodes, a stripped down compute-node operating system without dynamic library support, a shared-root environment and idiosyncratic application launching. Inour work, we describe how we resolved these challenges on a running, general-purpose production system to provide on-demand compute, storage, accounting and monitoring services through generic gridmore » interfaces that mask the underlying system-specific details for the end user.« less

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.

Fast MPEG-CDVS Encoder With GPU-CPU Hybrid Computing

NASA Astrophysics Data System (ADS)

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

2018-05-01

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

Models@Home: distributed computing in bioinformatics using a screensaver based approach.

PubMed

Krieger, Elmar; Vriend, Gert

2002-02-01

Due to the steadily growing computational demands in bioinformatics and related scientific disciplines, one is forced to make optimal use of the available resources. A straightforward solution is to build a network of idle computers and let each of them work on a small piece of a scientific challenge, as done by Seti@Home (http://setiathome.berkeley.edu), the world's largest distributed computing project. We developed a generally applicable distributed computing solution that uses a screensaver system similar to Seti@Home. The software exploits the coarse-grained nature of typical bioinformatics projects. Three major considerations for the design were: (1) often, many different programs are needed, while the time is lacking to parallelize them. Models@Home can run any program in parallel without modifications to the source code; (2) in contrast to the Seti project, bioinformatics applications are normally more sensitive to lost jobs. Models@Home therefore includes stringent control over job scheduling; (3) to allow use in heterogeneous environments, Linux and Windows based workstations can be combined with dedicated PCs to build a homogeneous cluster. We present three practical applications of Models@Home, running the modeling programs WHAT IF and YASARA on 30 PCs: force field parameterization, molecular dynamics docking, and database maintenance.

The Research of the Parallel Computing Development from the Angle of Cloud Computing

NASA Astrophysics Data System (ADS)

Peng, Zhensheng; Gong, Qingge; Duan, Yanyu; Wang, Yun

2017-10-01

Cloud computing is the development of parallel computing, distributed computing and grid computing. The development of cloud computing makes parallel computing come into people’s lives. Firstly, this paper expounds the concept of cloud computing and introduces two several traditional parallel programming model. Secondly, it analyzes and studies the principles, advantages and disadvantages of OpenMP, MPI and Map Reduce respectively. Finally, it takes MPI, OpenMP models compared to Map Reduce from the angle of cloud computing. The results of this paper are intended to provide a reference for the development of parallel computing.

Spatial data analytics on heterogeneous multi- and many-core parallel architectures using python

USGS Publications Warehouse

Laura, Jason R.; Rey, Sergio J.

2017-01-01

Parallel vector spatial analysis concerns the application of parallel computational methods to facilitate vector-based spatial analysis. The history of parallel computation in spatial analysis is reviewed, and this work is placed into the broader context of high-performance computing (HPC) and parallelization research. The rise of cyber infrastructure and its manifestation in spatial analysis as CyberGIScience is seen as a main driver of renewed interest in parallel computation in the spatial sciences. Key problems in spatial analysis that have been the focus of parallel computing are covered. Chief among these are spatial optimization problems, computational geometric problems including polygonization and spatial contiguity detection, the use of Monte Carlo Markov chain simulation in spatial statistics, and parallel implementations of spatial econometric methods. Future directions for research on parallelization in computational spatial analysis are outlined.

Execution models for mapping programs onto distributed memory parallel computers

NASA Technical Reports Server (NTRS)

Sussman, Alan

1992-01-01

The problem of exploiting the parallelism available in a program to efficiently employ the resources of the target machine is addressed. The problem is discussed in the context of building a mapping compiler for a distributed memory parallel machine. The paper describes using execution models to drive the process of mapping a program in the most efficient way onto a particular machine. Through analysis of the execution models for several mapping techniques for one class of programs, we show that the selection of the best technique for a particular program instance can make a significant difference in performance. On the other hand, the results of benchmarks from an implementation of a mapping compiler show that our execution models are accurate enough to select the best mapping technique for a given program.

Massively parallel electrical conductivity imaging of the subsurface: Applications to hydrocarbon exploration

NASA Astrophysics Data System (ADS)

Newman, Gregory A.; Commer, Michael

2009-07-01

Three-dimensional (3D) geophysical imaging is now receiving considerable attention for electrical conductivity mapping of potential offshore oil and gas reservoirs. The imaging technology employs controlled source electromagnetic (CSEM) and magnetotelluric (MT) fields and treats geological media exhibiting transverse anisotropy. Moreover when combined with established seismic methods, direct imaging of reservoir fluids is possible. Because of the size of the 3D conductivity imaging problem, strategies are required exploiting computational parallelism and optimal meshing. The algorithm thus developed has been shown to scale to tens of thousands of processors. In one imaging experiment, 32,768 tasks/processors on the IBM Watson Research Blue Gene/L supercomputer were successfully utilized. Over a 24 hour period we were able to image a large scale field data set that previously required over four months of processing time on distributed clusters based on Intel or AMD processors utilizing 1024 tasks on an InfiniBand fabric. Electrical conductivity imaging using massively parallel computational resources produces results that cannot be obtained otherwise and are consistent with timeframes required for practical exploration problems.

Block-Level Added Redundancy Explicit Authentication for Parallelized Encryption and Integrity Checking of Processor-Memory Transactions

NASA Astrophysics Data System (ADS)

Elbaz, Reouven; Torres, Lionel; Sassatelli, Gilles; Guillemin, Pierre; Bardouillet, Michel; Martinez, Albert

The bus between the System on Chip (SoC) and the external memory is one of the weakest points of computer systems: an adversary can easily probe this bus in order to read private data (data confidentiality concern) or to inject data (data integrity concern). The conventional way to protect data against such attacks and to ensure data confidentiality and integrity is to implement two dedicated engines: one performing data encryption and another data authentication. This approach, while secure, prevents parallelizability of the underlying computations. In this paper, we introduce the concept of Block-Level Added Redundancy Explicit Authentication (BL-AREA) and we describe a Parallelized Encryption and Integrity Checking Engine (PE-ICE) based on this concept. BL-AREA and PE-ICE have been designed to provide an effective solution to ensure both security services while allowing for full parallelization on processor read and write operations and optimizing the hardware resources. Compared to standard encryption which ensures only confidentiality, we show that PE-ICE additionally guarantees code and data integrity for less than 4% of run-time performance overhead.

Efficient operating system level virtualization techniques for cloud resources

NASA Astrophysics Data System (ADS)

Ansu, R.; Samiksha; Anju, S.; Singh, K. John

2017-11-01

Cloud computing is an advancing technology which provides the servcies of Infrastructure, Platform and Software. Virtualization and Computer utility are the keys of Cloud computing. The numbers of cloud users are increasing day by day. So it is the need of the hour to make resources available on demand to satisfy user requirements. The technique in which resources namely storage, processing power, memory and network or I/O are abstracted is known as Virtualization. For executing the operating systems various virtualization techniques are available. They are: Full System Virtualization and Para Virtualization. In Full Virtualization, the whole architecture of hardware is duplicated virtually. No modifications are required in Guest OS as the OS deals with the VM hypervisor directly. In Para Virtualization, modifications of OS is required to run in parallel with other OS. For the Guest OS to access the hardware, the host OS must provide a Virtual Machine Interface. OS virtualization has many advantages such as migrating applications transparently, consolidation of server, online maintenance of OS and providing security. This paper briefs both the virtualization techniques and discusses the issues in OS level virtualization.

Malleable architecture generator for FPGA computing

NASA Astrophysics Data System (ADS)

Gokhale, Maya; Kaba, James; Marks, Aaron; Kim, Jang

1996-10-01

The malleable architecture generator (MARGE) is a tool set that translates high-level parallel C to configuration bit streams for field-programmable logic based computing systems. MARGE creates an application-specific instruction set and generates the custom hardware components required to perform exactly those computations specified by the C program. In contrast to traditional fixed-instruction processors, MARGE's dynamic instruction set creation provides for efficient use of hardware resources. MARGE processes intermediate code in which each operation is annotated by the bit lengths of the operands. Each basic block (sequence of straight line code) is mapped into a single custom instruction which contains all the operations and logic inherent in the block. A synthesis phase maps the operations comprising the instructions into register transfer level structural components and control logic which have been optimized to exploit functional parallelism and function unit reuse. As a final stage, commercial technology-specific tools are used to generate configuration bit streams for the desired target hardware. Technology- specific pre-placed, pre-routed macro blocks are utilized to implement as much of the hardware as possible. MARGE currently supports the Xilinx-based Splash-2 reconfigurable accelerator and National Semiconductor's CLAy-based parallel accelerator, MAPA. The MARGE approach has been demonstrated on systolic applications such as DNA sequence comparison.

Broadcasting collective operation contributions throughout a parallel computer

DOEpatents

Faraj, Ahmad [Rochester, MN

2012-02-21

Methods, systems, and products are disclosed for broadcasting collective operation contributions throughout a parallel computer. The parallel computer includes a plurality of compute nodes connected together through a data communications network. Each compute node has a plurality of processors for use in collective parallel operations on the parallel computer. Broadcasting collective operation contributions throughout a parallel computer according to embodiments of the present invention includes: transmitting, by each processor on each compute node, that processor's collective operation contribution to the other processors on that compute node using intra-node communications; and transmitting on a designated network link, by each processor on each compute node according to a serial processor transmission sequence, that processor's collective operation contribution to the other processors on the other compute nodes using inter-node communications.

Application Portable Parallel Library

NASA Technical Reports Server (NTRS)

Cole, Gary L.; Blech, Richard A.; Quealy, Angela; Townsend, Scott

1995-01-01

Application Portable Parallel Library (APPL) computer program is subroutine-based message-passing software library intended to provide consistent interface to variety of multiprocessor computers on market today. Minimizes effort needed to move application program from one computer to another. User develops application program once and then easily moves application program from parallel computer on which created to another parallel computer. ("Parallel computer" also include heterogeneous collection of networked computers). Written in C language with one FORTRAN 77 subroutine for UNIX-based computers and callable from application programs written in C language or FORTRAN 77.

Computing effective properties of random heterogeneous materials on heterogeneous parallel processors

NASA Astrophysics Data System (ADS)

Leidi, Tiziano; Scocchi, Giulio; Grossi, Loris; Pusterla, Simone; D'Angelo, Claudio; Thiran, Jean-Philippe; Ortona, Alberto

2012-11-01

In recent decades, finite element (FE) techniques have been extensively used for predicting effective properties of random heterogeneous materials. In the case of very complex microstructures, the choice of numerical methods for the solution of this problem can offer some advantages over classical analytical approaches, and it allows the use of digital images obtained from real material samples (e.g., using computed tomography). On the other hand, having a large number of elements is often necessary for properly describing complex microstructures, ultimately leading to extremely time-consuming computations and high memory requirements. With the final objective of reducing these limitations, we improved an existing freely available FE code for the computation of effective conductivity (electrical and thermal) of microstructure digital models. To allow execution on hardware combining multi-core CPUs and a GPU, we first translated the original algorithm from Fortran to C, and we subdivided it into software components. Then, we enhanced the C version of the algorithm for parallel processing with heterogeneous processors. With the goal of maximizing the obtained performances and limiting resource consumption, we utilized a software architecture based on stream processing, event-driven scheduling, and dynamic load balancing. The parallel processing version of the algorithm has been validated using a simple microstructure consisting of a single sphere located at the centre of a cubic box, yielding consistent results. Finally, the code was used for the calculation of the effective thermal conductivity of a digital model of a real sample (a ceramic foam obtained using X-ray computed tomography). On a computer equipped with dual hexa-core Intel Xeon X5670 processors and an NVIDIA Tesla C2050, the parallel application version features near to linear speed-up progression when using only the CPU cores. It executes more than 20 times faster when additionally using the GPU.

Toward real-time Monte Carlo simulation using a commercial cloud computing infrastructure

NASA Astrophysics Data System (ADS)

Wang, Henry; Ma, Yunzhi; Pratx, Guillem; Xing, Lei

2011-09-01

Monte Carlo (MC) methods are the gold standard for modeling photon and electron transport in a heterogeneous medium; however, their computational cost prohibits their routine use in the clinic. Cloud computing, wherein computing resources are allocated on-demand from a third party, is a new approach for high performance computing and is implemented to perform ultra-fast MC calculation in radiation therapy. We deployed the EGS5 MC package in a commercial cloud environment. Launched from a single local computer with Internet access, a Python script allocates a remote virtual cluster. A handshaking protocol designates master and worker nodes. The EGS5 binaries and the simulation data are initially loaded onto the master node. The simulation is then distributed among independent worker nodes via the message passing interface, and the results aggregated on the local computer for display and data analysis. The described approach is evaluated for pencil beams and broad beams of high-energy electrons and photons. The output of cloud-based MC simulation is identical to that produced by single-threaded implementation. For 1 million electrons, a simulation that takes 2.58 h on a local computer can be executed in 3.3 min on the cloud with 100 nodes, a 47× speed-up. Simulation time scales inversely with the number of parallel nodes. The parallelization overhead is also negligible for large simulations. Cloud computing represents one of the most important recent advances in supercomputing technology and provides a promising platform for substantially improved MC simulation. In addition to the significant speed up, cloud computing builds a layer of abstraction for high performance parallel computing, which may change the way dose calculations are performed and radiation treatment plans are completed. This work was presented in part at the 2010 Annual Meeting of the American Association of Physicists in Medicine (AAPM), Philadelphia, PA.

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.

LB3D: A parallel implementation of the Lattice-Boltzmann method for simulation of interacting amphiphilic fluids

NASA Astrophysics Data System (ADS)

Schmieschek, S.; Shamardin, L.; Frijters, S.; Krüger, T.; Schiller, U. D.; Harting, J.; Coveney, P. V.

2017-08-01

We introduce the lattice-Boltzmann code LB3D, version 7.1. Building on a parallel program and supporting tools which have enabled research utilising high performance computing resources for nearly two decades, LB3D version 7 provides a subset of the research code functionality as an open source project. Here, we describe the theoretical basis of the algorithm as well as computational aspects of the implementation. The software package is validated against simulations of meso-phases resulting from self-assembly in ternary fluid mixtures comprising immiscible and amphiphilic components such as water-oil-surfactant systems. The impact of the surfactant species on the dynamics of spinodal decomposition are tested and quantitative measurement of the permeability of a body centred cubic (BCC) model porous medium for a simple binary mixture is described. Single-core performance and scaling behaviour of the code are reported for simulations on current supercomputer architectures.

Extensible Computational Chemistry Environment

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

2012-08-09

ECCE provides a sophisticated graphical user interface, scientific visualization tools, and the underlying data management framework enabling scientists to efficiently set up calculations and store, retrieve, and analyze the rapidly growing volumes of data produced by computational chemistry studies. ECCE was conceived as part of the Environmental Molecular Sciences Laboratory construction to solve the problem of researchers being able to effectively utilize complex computational chemistry codes and massively parallel high performance compute resources. Bringing the power of these codes and resources to the desktops of researcher and thus enabling world class research without users needing a detailed understanding of themore » inner workings of either the theoretical codes or the supercomputers needed to run them was a grand challenge problem in the original version of the EMSL. ECCE allows collaboration among researchers using a web-based data repository where the inputs and results for all calculations done within ECCE are organized. ECCE is a first of kind end-to-end problem solving environment for all phases of computational chemistry research: setting up calculations with sophisticated GUI and direct manipulation visualization tools, submitting and monitoring calculations on remote high performance supercomputers without having to be familiar with the details of using these compute resources, and performing results visualization and analysis including creating publication quality images. ECCE is a suite of tightly integrated applications that are employed as the user moves through the modeling process.« less

SediFoam: A general-purpose, open-source CFD-DEM solver for particle-laden flow with emphasis on sediment transport

NASA Astrophysics Data System (ADS)

Sun, Rui; Xiao, Heng

2016-04-01

With the growth of available computational resource, CFD-DEM (computational fluid dynamics-discrete element method) becomes an increasingly promising and feasible approach for the study of sediment transport. Several existing CFD-DEM solvers are applied in chemical engineering and mining industry. However, a robust CFD-DEM solver for the simulation of sediment transport is still desirable. In this work, the development of a three-dimensional, massively parallel, and open-source CFD-DEM solver SediFoam is detailed. This solver is built based on open-source solvers OpenFOAM and LAMMPS. OpenFOAM is a CFD toolbox that can perform three-dimensional fluid flow simulations on unstructured meshes; LAMMPS is a massively parallel DEM solver for molecular dynamics. Several validation tests of SediFoam are performed using cases of a wide range of complexities. The results obtained in the present simulations are consistent with those in the literature, which demonstrates the capability of SediFoam for sediment transport applications. In addition to the validation test, the parallel efficiency of SediFoam is studied to test the performance of the code for large-scale and complex simulations. The parallel efficiency tests show that the scalability of SediFoam is satisfactory in the simulations using up to O(107) particles.

Broadcasting a message in a parallel computer

DOEpatents

Berg, Jeremy E [Rochester, MN; Faraj, Ahmad A [Rochester, MN

2011-08-02

Methods, systems, and products are disclosed for broadcasting a message in a parallel computer. The parallel computer includes a plurality of compute nodes connected together using a data communications network. The data communications network optimized for point to point data communications and is characterized by at least two dimensions. The compute nodes are organized into at least one operational group of compute nodes for collective parallel operations of the parallel computer. One compute node of the operational group assigned to be a logical root. Broadcasting a message in a parallel computer includes: establishing a Hamiltonian path along all of the compute nodes in at least one plane of the data communications network and in the operational group; and broadcasting, by the logical root to the remaining compute nodes, the logical root's message along the established Hamiltonian path.

Force user's manual: A portable, parallel FORTRAN

NASA Technical Reports Server (NTRS)

Jordan, Harry F.; Benten, Muhammad S.; Arenstorf, Norbert S.; Ramanan, Aruna V.

1990-01-01

The use of Force, a parallel, portable FORTRAN on shared memory parallel computers is described. Force simplifies writing code for parallel computers and, once the parallel code is written, it is easily ported to computers on which Force is installed. Although Force is nearly the same for all computers, specific details are included for the Cray-2, Cray-YMP, Convex 220, Flex/32, Encore, Sequent, Alliant computers on which it is installed.

cuTauLeaping: A GPU-Powered Tau-Leaping Stochastic Simulator for Massive Parallel Analyses of Biological Systems

PubMed Central

Besozzi, Daniela; Pescini, Dario; Mauri, Giancarlo

2014-01-01

Tau-leaping is a stochastic simulation algorithm that efficiently reconstructs the temporal evolution of biological systems, modeled according to the stochastic formulation of chemical kinetics. The analysis of dynamical properties of these systems in physiological and perturbed conditions usually requires the execution of a large number of simulations, leading to high computational costs. Since each simulation can be executed independently from the others, a massive parallelization of tau-leaping can bring to relevant reductions of the overall running time. The emerging field of General Purpose Graphic Processing Units (GPGPU) provides power-efficient high-performance computing at a relatively low cost. In this work we introduce cuTauLeaping, a stochastic simulator of biological systems that makes use of GPGPU computing to execute multiple parallel tau-leaping simulations, by fully exploiting the Nvidia's Fermi GPU architecture. We show how a considerable computational speedup is achieved on GPU by partitioning the execution of tau-leaping into multiple separated phases, and we describe how to avoid some implementation pitfalls related to the scarcity of memory resources on the GPU streaming multiprocessors. Our results show that cuTauLeaping largely outperforms the CPU-based tau-leaping implementation when the number of parallel simulations increases, with a break-even directly depending on the size of the biological system and on the complexity of its emergent dynamics. In particular, cuTauLeaping is exploited to investigate the probability distribution of bistable states in the Schlögl model, and to carry out a bidimensional parameter sweep analysis to study the oscillatory regimes in the Ras/cAMP/PKA pathway in S. cerevisiae. PMID:24663957

Facilitating higher-fidelity simulations of axial compressor instability and other turbomachinery flow conditions

NASA Astrophysics Data System (ADS)

Herrick, Gregory Paul

The quest to accurately capture flow phenomena with length-scales both short and long and to accurately represent complex flow phenomena within disparately sized geometry inspires a need for an efficient, high-fidelity, multi-block structured computational fluid dynamics (CFD) parallel computational scheme. This research presents and demonstrates a more efficient computational method by which to perform multi-block structured CFD parallel computational simulations, thus facilitating higher-fidelity solutions of complicated geometries (due to the inclusion of grids for "small'' flow areas which are often merely modeled) and their associated flows. This computational framework offers greater flexibility and user-control in allocating the resource balance between process count and wall-clock computation time. The principal modifications implemented in this revision consist of a "multiple grid block per processing core'' software infrastructure and an analytic computation of viscous flux Jacobians. The development of this scheme is largely motivated by the desire to simulate axial compressor stall inception with more complete gridding of the flow passages (including rotor tip clearance regions) than has been previously done while maintaining high computational efficiency (i.e., minimal consumption of computational resources), and thus this paradigm shall be demonstrated with an examination of instability in a transonic axial compressor. However, the paradigm presented herein facilitates CFD simulation of myriad previously impractical geometries and flows and is not limited to detailed analyses of axial compressor flows. While the simulations presented herein were technically possible under the previous structure of the subject software, they were much less computationally efficient and thus not pragmatically feasible; the previous research using this software to perform three-dimensional, full-annulus, time-accurate, unsteady, full-stage (with sliding-interface) simulations of rotating stall inception in axial compressors utilized tip clearance periodic models, while the scheme here is demonstrated by a simulation of axial compressor stall inception utilizing gridded rotor tip clearance regions. As will be discussed, much previous research---experimental, theoretical, and computational---has suggested that understanding clearance flow behavior is critical to understanding stall inception, and previous computational research efforts which have used tip clearance models have begged the question, "What about the clearance flows?''. This research begins to address that question.

Architecture Adaptive Computing Environment

NASA Technical Reports Server (NTRS)

Dorband, John E.

2006-01-01

Architecture Adaptive Computing Environment (aCe) is a software system that includes a language, compiler, and run-time library for parallel computing. aCe was developed to enable programmers to write programs, more easily than was previously possible, for a variety of parallel computing architectures. Heretofore, it has been perceived to be difficult to write parallel programs for parallel computers and more difficult to port the programs to different parallel computing architectures. In contrast, aCe is supportable on all high-performance computing architectures. Currently, it is supported on LINUX clusters. aCe uses parallel programming constructs that facilitate writing of parallel programs. Such constructs were used in single-instruction/multiple-data (SIMD) programming languages of the 1980s, including Parallel Pascal, Parallel Forth, C*, *LISP, and MasPar MPL. In aCe, these constructs are extended and implemented for both SIMD and multiple- instruction/multiple-data (MIMD) architectures. Two new constructs incorporated in aCe are those of (1) scalar and virtual variables and (2) pre-computed paths. The scalar-and-virtual-variables construct increases flexibility in optimizing memory utilization in various architectures. The pre-computed-paths construct enables the compiler to pre-compute part of a communication operation once, rather than computing it every time the communication operation is performed.

Using Apex To Construct CPM-GOMS Models

NASA Technical Reports Server (NTRS)

John, Bonnie; Vera, Alonso; Matessa, Michael; Freed, Michael; Remington, Roger

2006-01-01

process for automatically generating computational models of human/computer interactions as well as graphical and textual representations of the models has been built on the conceptual foundation of a method known in the art as CPM-GOMS. This method is so named because it combines (1) the task decomposition of analysis according to an underlying method known in the art as the goals, operators, methods, and selection (GOMS) method with (2) a model of human resource usage at the level of cognitive, perceptual, and motor (CPM) operations. CPM-GOMS models have made accurate predictions about behaviors of skilled computer users in routine tasks, but heretofore, such models have been generated in a tedious, error-prone manual process. In the present process, CPM-GOMS models are generated automatically from a hierarchical task decomposition expressed by use of a computer program, known as Apex, designed previously to be used to model human behavior in complex, dynamic tasks. An inherent capability of Apex for scheduling of resources automates the difficult task of interleaving the cognitive, perceptual, and motor resources that underlie common task operators (e.g., move and click mouse). The user interface of Apex automatically generates Program Evaluation Review Technique (PERT) charts, which enable modelers to visualize the complex parallel behavior represented by a model. Because interleaving and the generation of displays to aid visualization are automated, it is now feasible to construct arbitrarily long sequences of behaviors. The process was tested by using Apex to create a CPM-GOMS model of a relatively simple human/computer-interaction task and comparing the time predictions of the model and measurements of the times taken by human users in performing the various steps of the task. The task was to withdraw $80 in cash from an automated teller machine (ATM). For the test, a Visual Basic mockup of an ATM was created, with a provision for input from (and measurement of the performance of) the user via a mouse. The times predicted by the automatically generated model turned out to approximate the measured times fairly well (see figure). While these results are promising, there is need for further development of the process. Moreover, it will also be necessary to test other, more complex models: The actions required of the user in the ATM task are too sequential to involve substantial parallelism and interleaving and, hence, do not serve as an adequate test of the unique strength of CPM-GOMS models to accommodate parallelism and interleaving.

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

Distributing an executable job load file to compute nodes in a parallel computer

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

Gooding, Thomas M.

Distributing an executable job load file to compute nodes in a parallel computer, the parallel computer comprising a plurality of compute nodes, including: determining, by a compute node in the parallel computer, whether the compute node is participating in a job; determining, by the compute node in the parallel computer, whether a descendant compute node is participating in the job; responsive to determining that the compute node is participating in the job or that the descendant compute node is participating in the job, communicating, by the compute node to a parent compute node, an identification of a data communications linkmore » over which the compute node receives data from the parent compute node; constructing a class route for the job, wherein the class route identifies all compute nodes participating in the job; and broadcasting the executable load file for the job along the class route for the job.« less

Informatics for RNA Sequencing: A Web Resource for Analysis on the Cloud

PubMed Central

Griffith, Malachi; Walker, Jason R.; Spies, Nicholas C.; Ainscough, Benjamin J.; Griffith, Obi L.

2015-01-01

Massively parallel RNA sequencing (RNA-seq) has rapidly become the assay of choice for interrogating RNA transcript abundance and diversity. This article provides a detailed introduction to fundamental RNA-seq molecular biology and informatics concepts. We make available open-access RNA-seq tutorials that cover cloud computing, tool installation, relevant file formats, reference genomes, transcriptome annotations, quality-control strategies, expression, differential expression, and alternative splicing analysis methods. These tutorials and additional training resources are accompanied by complete analysis pipelines and test datasets made available without encumbrance at www.rnaseq.wiki. PMID:26248053

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

GASPRNG: GPU accelerated scalable parallel random number generator library

NASA Astrophysics Data System (ADS)

Gao, Shuang; Peterson, Gregory D.

2013-04-01

Graphics processors represent a promising technology for accelerating computational science applications. Many computational science applications require fast and scalable random number generation with good statistical properties, so they use the Scalable Parallel Random Number Generators library (SPRNG). We present the GPU Accelerated SPRNG library (GASPRNG) to accelerate SPRNG in GPU-based high performance computing systems. GASPRNG includes code for a host CPU and CUDA code for execution on NVIDIA graphics processing units (GPUs) along with a programming interface to support various usage models for pseudorandom numbers and computational science applications executing on the CPU, GPU, or both. This paper describes the implementation approach used to produce high performance and also describes how to use the programming interface. The programming interface allows a user to be able to use GASPRNG the same way as SPRNG on traditional serial or parallel computers as well as to develop tightly coupled programs executing primarily on the GPU. We also describe how to install GASPRNG and use it. To help illustrate linking with GASPRNG, various demonstration codes are included for the different usage models. GASPRNG on a single GPU shows up to 280x speedup over SPRNG on a single CPU core and is able to scale for larger systems in the same manner as SPRNG. Because GASPRNG generates identical streams of pseudorandom numbers as SPRNG, users can be confident about the quality of GASPRNG for scalable computational science applications. Catalogue identifier: AEOI_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEOI_v1_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: UTK license. No. of lines in distributed program, including test data, etc.: 167900 No. of bytes in distributed program, including test data, etc.: 1422058 Distribution format: tar.gz Programming language: C and CUDA. Computer: Any PC or workstation with NVIDIA GPU (Tested on Fermi GTX480, Tesla C1060, Tesla M2070). Operating system: Linux with CUDA version 4.0 or later. Should also run on MacOS, Windows, or UNIX. Has the code been vectorized or parallelized?: Yes. Parallelized using MPI directives. RAM: 512 MB˜ 732 MB (main memory on host CPU, depending on the data type of random numbers.) / 512 MB (GPU global memory) Classification: 4.13, 6.5. Nature of problem: Many computational science applications are able to consume large numbers of random numbers. For example, Monte Carlo simulations are able to consume limitless random numbers for the computation as long as resources for the computing are supported. Moreover, parallel computational science applications require independent streams of random numbers to attain statistically significant results. The SPRNG library provides this capability, but at a significant computational cost. The GASPRNG library presented here accelerates the generators of independent streams of random numbers using graphical processing units (GPUs). Solution method: Multiple copies of random number generators in GPUs allow a computational science application to consume large numbers of random numbers from independent, parallel streams. GASPRNG is a random number generators library to allow a computational science application to employ multiple copies of random number generators to boost performance. Users can interface GASPRNG with software code executing on microprocessors and/or GPUs. Running time: The tests provided take a few minutes to run.

Investigation of the applicability of a functional programming model to fault-tolerant parallel processing for knowledge-based systems

NASA Technical Reports Server (NTRS)

Harper, Richard

1989-01-01

In a fault-tolerant parallel computer, a functional programming model can facilitate distributed checkpointing, error recovery, load balancing, and graceful degradation. Such a model has been implemented on the Draper Fault-Tolerant Parallel Processor (FTPP). When used in conjunction with the FTPP's fault detection and masking capabilities, this implementation results in a graceful degradation of system performance after faults. Three graceful degradation algorithms have been implemented and are presented. A user interface has been implemented which requires minimal cognitive overhead by the application programmer, masking such complexities as the system's redundancy, distributed nature, variable complement of processing resources, load balancing, fault occurrence and recovery. This user interface is described and its use demonstrated. The applicability of the functional programming style to the Activation Framework, a paradigm for intelligent systems, is then briefly described.

A high performance scientific cloud computing environment for materials simulations

NASA Astrophysics Data System (ADS)

Jorissen, K.; Vila, F. D.; Rehr, J. J.

2012-09-01

We describe the development of a scientific cloud computing (SCC) platform that offers high performance computation capability. The platform consists of a scientific virtual machine prototype containing a UNIX operating system and several materials science codes, together with essential interface tools (an SCC toolset) that offers functionality comparable to local compute clusters. In particular, our SCC toolset provides automatic creation of virtual clusters for parallel computing, including tools for execution and monitoring performance, as well as efficient I/O utilities that enable seamless connections to and from the cloud. Our SCC platform is optimized for the Amazon Elastic Compute Cloud (EC2). We present benchmarks for prototypical scientific applications and demonstrate performance comparable to local compute clusters. To facilitate code execution and provide user-friendly access, we have also integrated cloud computing capability in a JAVA-based GUI. Our SCC platform may be an alternative to traditional HPC resources for materials science or quantum chemistry applications.

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.

The Automated Instrumentation and Monitoring System (AIMS): Design and Architecture. 3.2

NASA Technical Reports Server (NTRS)

Yan, Jerry C.; Schmidt, Melisa; Schulbach, Cathy; Bailey, David (Technical Monitor)

1997-01-01

Whether a researcher is designing the 'next parallel programming paradigm', another 'scalable multiprocessor' or investigating resource allocation algorithms for multiprocessors, a facility that enables parallel program execution to be captured and displayed is invaluable. Careful analysis of such information can help computer and software architects to capture, and therefore, exploit behavioral variations among/within various parallel programs to take advantage of specific hardware characteristics. A software tool-set that facilitates performance evaluation of parallel applications on multiprocessors has been put together at NASA Ames Research Center under the sponsorship of NASA's High Performance Computing and Communications Program over the past five years. The Automated Instrumentation and Monitoring Systematic has three major software components: a source code instrumentor which automatically inserts active event recorders into program source code before compilation; a run-time performance monitoring library which collects performance data; and a visualization tool-set which reconstructs program execution based on the data collected. Besides being used as a prototype for developing new techniques for instrumenting, monitoring and presenting parallel program execution, AIMS is also being incorporated into the run-time environments of various hardware testbeds to evaluate their impact on user productivity. Currently, the execution of FORTRAN and C programs on the Intel Paragon and PALM workstations can be automatically instrumented and monitored. Performance data thus collected can be displayed graphically on various workstations. The process of performance tuning with AIMS will be illustrated using various NAB Parallel Benchmarks. This report includes a description of the internal architecture of AIMS and a listing of the source code.

Computing with Beowulf

NASA Technical Reports Server (NTRS)

Cohen, Jarrett

1999-01-01

Parallel computers built out of mass-market parts are cost-effectively performing data processing and simulation tasks. The Supercomputing (now known as "SC") series of conferences celebrated its 10th anniversary last November. While vendors have come and gone, the dominant paradigm for tackling big problems still is a shared-resource, commercial supercomputer. Growing numbers of users needing a cheaper or dedicated-access alternative are building their own supercomputers out of mass-market parts. Such machines are generally called Beowulf-class systems after the 11th century epic. This modern-day Beowulf story began in 1994 at NASA's Goddard Space Flight Center. A laboratory for the Earth and space sciences, computing managers there threw down a gauntlet to develop a $50,000 gigaFLOPS workstation for processing satellite data sets. Soon, Thomas Sterling and Don Becker were working on the Beowulf concept at the University Space Research Association (USRA)-run Center of Excellence in Space Data and Information Sciences (CESDIS). Beowulf clusters mix three primary ingredients: commodity personal computers or workstations, low-cost Ethernet networks, and the open-source Linux operating system. One of the larger Beowulfs is Goddard's Highly-parallel Integrated Virtual Environment, or HIVE for short.

Matching pursuit parallel decomposition of seismic data

NASA Astrophysics Data System (ADS)

Li, Chuanhui; Zhang, Fanchang

2017-07-01

In order to improve the computation speed of matching pursuit decomposition of seismic data, a matching pursuit parallel algorithm is designed in this paper. We pick a fixed number of envelope peaks from the current signal in every iteration according to the number of compute nodes and assign them to the compute nodes on average to search the optimal Morlet wavelets in parallel. With the help of parallel computer systems and Message Passing Interface, the parallel algorithm gives full play to the advantages of parallel computing to significantly improve the computation speed of the matching pursuit decomposition and also has good expandability. Besides, searching only one optimal Morlet wavelet by every compute node in every iteration is the most efficient implementation.

Computer hardware fault administration

DOEpatents

Archer, Charles J.; Megerian, Mark G.; Ratterman, Joseph D.; Smith, Brian E.

2010-09-14

Computer hardware fault administration carried out in a parallel computer, where the parallel computer includes a plurality of compute nodes. The compute nodes are coupled for data communications by at least two independent data communications networks, where each data communications network includes data communications links connected to the compute nodes. Typical embodiments carry out hardware fault administration by identifying a location of a defective link in the first data communications network of the parallel computer and routing communications data around the defective link through the second data communications network of the parallel computer.

Scalable and fast heterogeneous molecular simulation with predictive parallelization schemes

NASA Astrophysics Data System (ADS)

Guzman, Horacio V.; Junghans, Christoph; Kremer, Kurt; Stuehn, Torsten

2017-11-01

Multiscale and inhomogeneous molecular systems are challenging topics in the field of molecular simulation. In particular, modeling biological systems in the context of multiscale simulations and exploring material properties are driving a permanent development of new simulation methods and optimization algorithms. In computational terms, those methods require parallelization schemes that make a productive use of computational resources for each simulation and from its genesis. Here, we introduce the heterogeneous domain decomposition approach, which is a combination of an heterogeneity-sensitive spatial domain decomposition with an a priori rearrangement of subdomain walls. Within this approach, the theoretical modeling and scaling laws for the force computation time are proposed and studied as a function of the number of particles and the spatial resolution ratio. We also show the new approach capabilities, by comparing it to both static domain decomposition algorithms and dynamic load-balancing schemes. Specifically, two representative molecular systems have been simulated and compared to the heterogeneous domain decomposition proposed in this work. These two systems comprise an adaptive resolution simulation of a biomolecule solvated in water and a phase-separated binary Lennard-Jones fluid.

Large Scale Computing and Storage Requirements for High Energy Physics

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

Gerber, Richard A.; Wasserman, Harvey

2010-11-24

The National Energy Research Scientific Computing Center (NERSC) is the leading scientific computing facility for the Department of Energy's Office of Science, providing high-performance computing (HPC) resources to more than 3,000 researchers working on about 400 projects. NERSC provides large-scale computing resources and, crucially, the support and expertise needed for scientists to make effective use of them. In November 2009, NERSC, DOE's Office of Advanced Scientific Computing Research (ASCR), and DOE's Office of High Energy Physics (HEP) held a workshop to characterize the HPC resources needed at NERSC to support HEP research through the next three to five years. Themore » effort is part of NERSC's legacy of anticipating users needs and deploying resources to meet those demands. The workshop revealed several key points, in addition to achieving its goal of collecting and characterizing computing requirements. The chief findings: (1) Science teams need access to a significant increase in computational resources to meet their research goals; (2) Research teams need to be able to read, write, transfer, store online, archive, analyze, and share huge volumes of data; (3) Science teams need guidance and support to implement their codes on future architectures; and (4) Projects need predictable, rapid turnaround of their computational jobs to meet mission-critical time constraints. This report expands upon these key points and includes others. It also presents a number of case studies as representative of the research conducted within HEP. Workshop participants were asked to codify their requirements in this case study format, summarizing their science goals, methods of solution, current and three-to-five year computing requirements, and software and support needs. Participants were also asked to describe their strategy for computing in the highly parallel, multi-core environment that is expected to dominate HPC architectures over the next few years. The report includes a section that describes efforts already underway or planned at NERSC that address requirements collected at the workshop. NERSC has many initiatives in progress that address key workshop findings and are aligned with NERSC's strategic plans.« less

Data communications in a parallel active messaging interface of a parallel computer

DOEpatents

Archer, Charles J; Blocksome, Michael A; Ratterman, Joseph D; Smith, Brian E

2014-02-11

Data communications in a parallel active messaging interface ('PAMI') or a parallel computer, the parallel computer including a plurality of compute nodes that execute a parallel application, the PAMI composed of data communications endpoints, each endpoint including a specification of data communications parameters for a thread of execution of a compute node, including specification of a client, a context, and a task, the compute nodes and the endpoints coupled for data communications instruction, the instruction characterized by instruction type, the instruction specifying a transmission of transfer data from the origin endpoint to a target endpoint and transmitting, in accordance witht the instruction type, the transfer data from the origin endpoin to the target endpoint.

Towards optimizing server performance in an educational MMORPG for teaching computer programming

NASA Astrophysics Data System (ADS)

Malliarakis, Christos; Satratzemi, Maya; Xinogalos, Stelios

2013-10-01

Web-based games have become significantly popular during the last few years. This is due to the gradual increase of internet speed, which has led to the ongoing multiplayer games development and more importantly the emergence of the Massive Multiplayer Online Role Playing Games (MMORPG) field. In parallel, similar technologies called educational games have started to be developed in order to be put into practice in various educational contexts, resulting in the field of Game Based Learning. However, these technologies require significant amounts of resources, such as bandwidth, RAM and CPU capacity etc. These amounts may be even larger in an educational MMORPG game that supports computer programming education, due to the usual inclusion of a compiler and the constant client/server data transmissions that occur during program coding, possibly leading to technical issues that could cause malfunctions during learning. Thus, the determination of the elements that affect the overall games resources' load is essential so that server administrators can configure them and ensure educational games' proper operation during computer programming education. In this paper, we propose a new methodology with which we can achieve monitoring and optimization of the load balancing, so that the essential resources for the creation and proper execution of an educational MMORPG for computer programming can be foreseen and bestowed without overloading the system.

Uncertainty quantification of seabed parameters for large data volumes along survey tracks with a tempered particle filter

NASA Astrophysics Data System (ADS)

Dettmer, J.; Quijano, J. E.; Dosso, S. E.; Holland, C. W.; Mandolesi, E.

2016-12-01

Geophysical seabed properties are important for the detection and classification of unexploded ordnance. However, current surveying methods such as vertical seismic profiling, coring, or inversion are of limited use when surveying large areas with high spatial sampling density. We consider surveys based on a source and receiver array towed by an autonomous vehicle which produce large volumes of seabed reflectivity data that contain unprecedented and detailed seabed information. The data are analyzed with a particle filter, which requires efficient reflection-coefficient computation, efficient inversion algorithms and efficient use of computer resources. The filter quantifies information content of multiple sequential data sets by considering results from previous data along the survey track to inform the importance sampling at the current point. Challenges arise from environmental changes along the track where the number of sediment layers and their properties change. This is addressed by a trans-dimensional model in the filter which allows layering complexity to change along a track. Efficiency is improved by likelihood tempering of various particle subsets and including exchange moves (parallel tempering). The filter is implemented on a hybrid computer that combines central processing units (CPUs) and graphics processing units (GPUs) to exploit three levels of parallelism: (1) fine-grained parallel computation of spherical reflection coefficients with a GPU implementation of Levin integration; (2) updating particles by concurrent CPU processes which exchange information using automatic load balancing (coarse grained parallelism); (3) overlapping CPU-GPU communication (a major bottleneck) with GPU computation by staggering CPU access to the multiple GPUs. The algorithm is applied to spherical reflection coefficients for data sets along a 14-km track on the Malta Plateau, Mediterranean Sea. We demonstrate substantial efficiency gains over previous methods. [This research was supported in part by the U.S. Dept of Defense, thought the Strategic Environmental Research and Development Program (SERDP).

Password Cracking Using Sony Playstations

NASA Astrophysics Data System (ADS)

Kleinhans, Hugo; Butts, Jonathan; Shenoi, Sujeet

Law enforcement agencies frequently encounter encrypted digital evidence for which the cryptographic keys are unknown or unavailable. Password cracking - whether it employs brute force or sophisticated cryptanalytic techniques - requires massive computational resources. This paper evaluates the benefits of using the Sony PlayStation 3 (PS3) to crack passwords. The PS3 offers massive computational power at relatively low cost. Moreover, multiple PS3 systems can be introduced easily to expand parallel processing when additional power is needed. This paper also describes a distributed framework designed to enable law enforcement agents to crack encrypted archives and applications in an efficient and cost-effective manner.

Evolutionary computing for the design search and optimization of space vehicle power subsystems

NASA Technical Reports Server (NTRS)

Kordon, Mark; Klimeck, Gerhard; Hanks, David; Hua, Hook

2004-01-01

Evolutionary computing has proven to be a straightforward and robust approach for optimizing a wide range of difficult analysis and design problems. This paper discusses the application of these techniques to an existing space vehicle power subsystem resource and performance analysis simulation in a parallel processing environment. Out preliminary results demonstrate that this approach has the potential to improve the space system trade study process by allowing engineers to statistically weight subsystem goals of mass, cost and performance then automatically size power elements based on anticipated performance of the subsystem rather than on worst-case estimates.

High-performance computing — an overview

NASA Astrophysics Data System (ADS)

Marksteiner, Peter

1996-08-01

An overview of high-performance computing (HPC) is given. Different types of computer architectures used in HPC are discussed: vector supercomputers, high-performance RISC processors, various parallel computers like symmetric multiprocessors, workstation clusters, massively parallel processors. Software tools and programming techniques used in HPC are reviewed: vectorizing compilers, optimization and vector tuning, optimization for RISC processors; parallel programming techniques like shared-memory parallelism, message passing and data parallelism; and numerical libraries.

Significantly reducing the processing times of high-speed photometry data sets using a distributed computing model

NASA Astrophysics Data System (ADS)

Doyle, Paul; Mtenzi, Fred; Smith, Niall; Collins, Adrian; O'Shea, Brendan

2012-09-01

The scientific community is in the midst of a data analysis crisis. The increasing capacity of scientific CCD instrumentation and their falling costs is contributing to an explosive generation of raw photometric data. This data must go through a process of cleaning and reduction before it can be used for high precision photometric analysis. Many existing data processing pipelines either assume a relatively small dataset or are batch processed by a High Performance Computing centre. A radical overhaul of these processing pipelines is required to allow reduction and cleaning rates to process terabyte sized datasets at near capture rates using an elastic processing architecture. The ability to access computing resources and to allow them to grow and shrink as demand fluctuates is essential, as is exploiting the parallel nature of the datasets. A distributed data processing pipeline is required. It should incorporate lossless data compression, allow for data segmentation and support processing of data segments in parallel. Academic institutes can collaborate and provide an elastic computing model without the requirement for large centralized high performance computing data centers. This paper demonstrates how a base 10 order of magnitude improvement in overall processing time has been achieved using the "ACN pipeline", a distributed pipeline spanning multiple academic institutes.

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

PubMed

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

2018-05-01

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

Automatic Management of Parallel and Distributed System Resources

NASA Technical Reports Server (NTRS)

Yan, Jerry; Ngai, Tin Fook; Lundstrom, Stephen F.

1990-01-01

Viewgraphs on automatic management of parallel and distributed system resources are presented. Topics covered include: parallel applications; intelligent management of multiprocessing systems; performance evaluation of parallel architecture; dynamic concurrent programs; compiler-directed system approach; lattice gaseous cellular automata; and sparse matrix Cholesky factorization.

GRAVIDY, a GPU modular, parallel direct-summation N-body integrator: dynamics with softening

NASA Astrophysics Data System (ADS)

Maureira-Fredes, Cristián; Amaro-Seoane, Pau

2018-01-01

A wide variety of outstanding problems in astrophysics involve the motion of a large number of particles under the force of gravity. These include the global evolution of globular clusters, tidal disruptions of stars by a massive black hole, the formation of protoplanets and sources of gravitational radiation. The direct-summation of N gravitational forces is a complex problem with no analytical solution and can only be tackled with approximations and numerical methods. To this end, the Hermite scheme is a widely used integration method. With different numerical techniques and special-purpose hardware, it can be used to speed up the calculations. But these methods tend to be computationally slow and cumbersome to work with. We present a new graphics processing unit (GPU), direct-summation N-body integrator written from scratch and based on this scheme, which includes relativistic corrections for sources of gravitational radiation. GRAVIDY has high modularity, allowing users to readily introduce new physics, it exploits available computational resources and will be maintained by regular updates. GRAVIDY can be used in parallel on multiple CPUs and GPUs, with a considerable speed-up benefit. The single-GPU version is between one and two orders of magnitude faster than the single-CPU version. A test run using four GPUs in parallel shows a speed-up factor of about 3 as compared to the single-GPU version. The conception and design of this first release is aimed at users with access to traditional parallel CPU clusters or computational nodes with one or a few GPU cards.

ICON-MIC: Implementing a CPU/MIC Collaboration Parallel Framework for ICON on Tianhe-2 Supercomputer.

PubMed

Wang, Zihao; Chen, Yu; Zhang, Jingrong; Li, Lun; Wan, Xiaohua; Liu, Zhiyong; Sun, Fei; Zhang, Fa

2018-03-01

Electron tomography (ET) is an important technique for studying the three-dimensional structures of the biological ultrastructure. Recently, ET has reached sub-nanometer resolution for investigating the native and conformational dynamics of macromolecular complexes by combining with the sub-tomogram averaging approach. Due to the limited sampling angles, ET reconstruction typically suffers from the "missing wedge" problem. Using a validation procedure, iterative compressed-sensing optimized nonuniform fast Fourier transform (NUFFT) reconstruction (ICON) demonstrates its power in restoring validated missing information for a low-signal-to-noise ratio biological ET dataset. However, the huge computational demand has become a bottleneck for the application of ICON. In this work, we implemented a parallel acceleration technology ICON-many integrated core (MIC) on Xeon Phi cards to address the huge computational demand of ICON. During this step, we parallelize the element-wise matrix operations and use the efficient summation of a matrix to reduce the cost of matrix computation. We also developed parallel versions of NUFFT on MIC to achieve a high acceleration of ICON by using more efficient fast Fourier transform (FFT) calculation. We then proposed a hybrid task allocation strategy (two-level load balancing) to improve the overall performance of ICON-MIC by making full use of the idle resources on Tianhe-2 supercomputer. Experimental results using two different datasets show that ICON-MIC has high accuracy in biological specimens under different noise levels and a significant acceleration, up to 13.3 × , compared with the CPU version. Further, ICON-MIC has good scalability efficiency and overall performance on Tianhe-2 supercomputer.

Scientific Services on the Cloud

NASA Astrophysics Data System (ADS)

Chapman, David; Joshi, Karuna P.; Yesha, Yelena; Halem, Milt; Yesha, Yaacov; Nguyen, Phuong

Scientific Computing was one of the first every applications for parallel and distributed computation. To this date, scientific applications remain some of the most compute intensive, and have inspired creation of petaflop compute infrastructure such as the Oak Ridge Jaguar and Los Alamos RoadRunner. Large dedicated hardware infrastructure has become both a blessing and a curse to the scientific community. Scientists are interested in cloud computing for much the same reason as businesses and other professionals. The hardware is provided, maintained, and administrated by a third party. Software abstraction and virtualization provide reliability, and fault tolerance. Graduated fees allow for multi-scale prototyping and execution. Cloud computing resources are only a few clicks away, and by far the easiest high performance distributed platform to gain access to. There may still be dedicated infrastructure for ultra-scale science, but the cloud can easily play a major part of the scientific computing initiative.

Node fingerprinting: an efficient heuristic for aligning biological networks.

PubMed

Radu, Alex; Charleston, Michael

2014-10-01

With the continuing increase in availability of biological data and improvements to biological models, biological network analysis has become a promising area of research. An emerging technique for the analysis of biological networks is through network alignment. Network alignment has been used to calculate genetic distance, similarities between regulatory structures, and the effect of external forces on gene expression, and to depict conditional activity of expression modules in cancer. Network alignment is algorithmically complex, and therefore we must rely on heuristics, ideally as efficient and accurate as possible. The majority of current techniques for network alignment rely on precomputed information, such as with protein sequence alignment, or on tunable network alignment parameters, which may introduce an increased computational overhead. Our presented algorithm, which we call Node Fingerprinting (NF), is appropriate for performing global pairwise network alignment without precomputation or tuning, can be fully parallelized, and is able to quickly compute an accurate alignment between two biological networks. It has performed as well as or better than existing algorithms on biological and simulated data, and with fewer computational resources. The algorithmic validation performed demonstrates the low computational resource requirements of NF.

Quantum computation based on photonic systems with two degrees of freedom assisted by the weak cross-Kerr nonlinearity

PubMed Central

Luo, Ming-Xing; Li, Hui-Ran; Lai, Hong

2016-01-01

Most of previous quantum computations only take use of one degree of freedom (DoF) of photons. An experimental system may possess various DoFs simultaneously. In this paper, with the weak cross-Kerr nonlinearity, we investigate the parallel quantum computation dependent on photonic systems with two DoFs. We construct nearly deterministic controlled-not (CNOT) gates operating on the polarization spatial DoFs of the two-photon or one-photon system. These CNOT gates show that two photonic DoFs can be encoded as independent qubits without auxiliary DoF in theory. Only the coherent states are required. Thus one half of quantum simulation resources may be saved in quantum applications if more complicated circuits are involved. Hence, one may trade off the implementation complexity and simulation resources by using different photonic systems. These CNOT gates are also used to complete various applications including the quantum teleportation and quantum superdense coding. PMID:27424767

Quantum computation based on photonic systems with two degrees of freedom assisted by the weak cross-Kerr nonlinearity.

PubMed

Luo, Ming-Xing; Li, Hui-Ran; Lai, Hong

2016-07-18

Most of previous quantum computations only take use of one degree of freedom (DoF) of photons. An experimental system may possess various DoFs simultaneously. In this paper, with the weak cross-Kerr nonlinearity, we investigate the parallel quantum computation dependent on photonic systems with two DoFs. We construct nearly deterministic controlled-not (CNOT) gates operating on the polarization spatial DoFs of the two-photon or one-photon system. These CNOT gates show that two photonic DoFs can be encoded as independent qubits without auxiliary DoF in theory. Only the coherent states are required. Thus one half of quantum simulation resources may be saved in quantum applications if more complicated circuits are involved. Hence, one may trade off the implementation complexity and simulation resources by using different photonic systems. These CNOT gates are also used to complete various applications including the quantum teleportation and quantum superdense coding.

Accessing and Visualizing scientific spatiotemporal data

NASA Technical Reports Server (NTRS)

Katz, Daniel S.; Bergou, Attila; Berriman, Bruce G.; Block, Gary L.; Collier, Jim; Curkendall, David W.; Good, John; Husman, Laura; Jacob, Joseph C.; Laity, Anastasia;

Multifaceted free-space image distributor for optical interconnects in massively parrallel processing

NASA Astrophysics Data System (ADS)

Zhao, Feng; Frietman, Edward E. E.; Han, Zhong; Chen, Ray T.

1999-04-01

A characteristic feature of a conventional von Neumann computer is that computing power is delivered by a single processing unit. Although increasing the clock frequency improves the performance of the computer, the switching speed of the semiconductor devices and the finite speed at which electrical signals propagate along the bus set the boundaries. Architectures containing large numbers of nodes can solve this performance dilemma, with the comment that main obstacles in designing such systems are caused by difficulties to come up with solutions that guarantee efficient communications among the nodes. Exchanging data becomes really a bottleneck should al nodes be connected by a shared resource. Only optics, due to its inherent parallelism, could solve that bottleneck. Here, we explore a multi-faceted free space image distributor to be used in optical interconnects in massively parallel processing. In this paper, physical and optical models of the image distributor are focused on from diffraction theory of light wave to optical simulations. the general features and the performance of the image distributor are also described. The new structure of an image distributor and the simulations for it are discussed. From the digital simulation and experiment, it is found that the multi-faceted free space image distributing technique is quite suitable for free space optical interconnection in massively parallel processing and new structure of the multifaceted free space image distributor would perform better.

Leveraging human oversight and intervention in large-scale parallel processing of open-source data

NASA Astrophysics Data System (ADS)

Casini, Enrico; Suri, Niranjan; Bradshaw, Jeffrey M.

2015-05-01

The popularity of cloud computing along with the increased availability of cheap storage have led to the necessity of elaboration and transformation of large volumes of open-source data, all in parallel. One way to handle such extensive volumes of information properly is to take advantage of distributed computing frameworks like Map-Reduce. Unfortunately, an entirely automated approach that excludes human intervention is often unpredictable and error prone. Highly accurate data processing and decision-making can be achieved by supporting an automatic process through human collaboration, in a variety of environments such as warfare, cyber security and threat monitoring. Although this mutual participation seems easily exploitable, human-machine collaboration in the field of data analysis presents several challenges. First, due to the asynchronous nature of human intervention, it is necessary to verify that once a correction is made, all the necessary reprocessing is done in chain. Second, it is often needed to minimize the amount of reprocessing in order to optimize the usage of resources due to limited availability. In order to improve on these strict requirements, this paper introduces improvements to an innovative approach for human-machine collaboration in the processing of large amounts of open-source data in parallel.

Geospatial Applications on Different Parallel and Distributed Systems in enviroGRIDS Project

NASA Astrophysics Data System (ADS)

Rodila, D.; Bacu, V.; Gorgan, D.

2012-04-01

The execution of Earth Science applications and services on parallel and distributed systems has become a necessity especially due to the large amounts of Geospatial data these applications require and the large geographical areas they cover. The parallelization of these applications comes to solve important performance issues and can spread from task parallelism to data parallelism as well. Parallel and distributed architectures such as Grid, Cloud, Multicore, etc. seem to offer the necessary functionalities to solve important problems in the Earth Science domain: storing, distribution, management, processing and security of Geospatial data, execution of complex processing through task and data parallelism, etc. A main goal of the FP7-funded project enviroGRIDS (Black Sea Catchment Observation and Assessment System supporting Sustainable Development) [1] is the development of a Spatial Data Infrastructure targeting this catchment region but also the development of standardized and specialized tools for storing, analyzing, processing and visualizing the Geospatial data concerning this area. For achieving these objectives, the enviroGRIDS deals with the execution of different Earth Science applications, such as hydrological models, Geospatial Web services standardized by the Open Geospatial Consortium (OGC) and others, on parallel and distributed architecture to maximize the obtained performance. This presentation analysis the integration and execution of Geospatial applications on different parallel and distributed architectures and the possibility of choosing among these architectures based on application characteristics and user requirements through a specialized component. Versions of the proposed platform have been used in enviroGRIDS project on different use cases such as: the execution of Geospatial Web services both on Web and Grid infrastructures [2] and the execution of SWAT hydrological models both on Grid and Multicore architectures [3]. The current focus is to integrate in the proposed platform the Cloud infrastructure, which is still a paradigm with critical problems to be solved despite the great efforts and investments. Cloud computing comes as a new way of delivering resources while using a large set of old as well as new technologies and tools for providing the necessary functionalities. The main challenges in the Cloud computing, most of them identified also in the Open Cloud Manifesto 2009, address resource management and monitoring, data and application interoperability and portability, security, scalability, software licensing, etc. We propose a platform able to execute different Geospatial applications on different parallel and distributed architectures such as Grid, Cloud, Multicore, etc. with the possibility of choosing among these architectures based on application characteristics and complexity, user requirements, necessary performances, cost support, etc. The execution redirection on a selected architecture is realized through a specialized component and has the purpose of offering a flexible way in achieving the best performances considering the existing restrictions.

Cloudbursting - Solving the 3-body problem

NASA Astrophysics Data System (ADS)

Chang, G.; Heistand, S.; Vakhnin, A.; Huang, T.; Zimdars, P.; Hua, H.; Hood, R.; Koenig, J.; Mehrotra, P.; Little, M. M.; Law, E.

2014-12-01

Many science projects in the future will be accomplished through collaboration among 2 or more NASA centers along with, potentially, external scientists. Science teams will be composed of more geographically dispersed individuals and groups. However, the current computing environment does not make this easy and seamless. By being able to share computing resources among members of a multi-center team working on a science/ engineering project, limited pre-competition funds could be more efficiently applied and technical work could be conducted more effectively with less time spent moving data or waiting for computing resources to free up. Based on the work from an NASA CIO IT Labs task, this presentation will highlight our prototype work in identifying the feasibility and identify the obstacles, both technical and management, to perform "Cloudbursting" among private clouds located at three different centers. We will demonstrate the use of private cloud computing infrastructure at the Jet Propulsion Laboratory, Langley Research Center, and Ames Research Center to provide elastic computation to each other to perform parallel Earth Science data imaging. We leverage elastic load balancing and auto-scaling features at each data center so that each location can independently define how many resources to allocate to a particular job that was "bursted" from another data center and demonstrate that compute capacity scales up and down with the job. We will also discuss future work in the area, which could include the use of cloud infrastructure from different cloud framework providers as well as other cloud service providers.

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.

Proposed hardware architectures of particle filter for object tracking

NASA Astrophysics Data System (ADS)

Abd El-Halym, Howida A.; Mahmoud, Imbaby Ismail; Habib, SED

2012-12-01

In this article, efficient hardware architectures for particle filter (PF) are presented. We propose three different architectures for Sequential Importance Resampling Filter (SIRF) implementation. The first architecture is a two-step sequential PF machine, where particle sampling, weight, and output calculations are carried out in parallel during the first step followed by sequential resampling in the second step. For the weight computation step, a piecewise linear function is used instead of the classical exponential function. This decreases the complexity of the architecture without degrading the results. The second architecture speeds up the resampling step via a parallel, rather than a serial, architecture. This second architecture targets a balance between hardware resources and the speed of operation. The third architecture implements the SIRF as a distributed PF composed of several processing elements and central unit. All the proposed architectures are captured using VHDL synthesized using Xilinx environment, and verified using the ModelSim simulator. Synthesis results confirmed the resource reduction and speed up advantages of our architectures.

NGScloud: RNA-seq analysis of non-model species using cloud computing.

PubMed

Mora-Márquez, Fernando; Vázquez-Poletti, José Luis; López de Heredia, Unai

2018-05-03

RNA-seq analysis usually requires large computing infrastructures. NGScloud is a bioinformatic system developed to analyze RNA-seq data using the cloud computing services of Amazon that permit the access to ad hoc computing infrastructure scaled according to the complexity of the experiment, so its costs and times can be optimized. The application provides a user-friendly front-end to operate Amazon's hardware resources, and to control a workflow of RNA-seq analysis oriented to non-model species, incorporating the cluster concept, which allows parallel runs of common RNA-seq analysis programs in several virtual machines for faster analysis. NGScloud is freely available at https://github.com/GGFHF/NGScloud/. A manual detailing installation and how-to-use instructions is available with the distribution. unai.lopezdeheredia@upm.es.

The 2nd Symposium on the Frontiers of Massively Parallel Computations

NASA Technical Reports Server (NTRS)

Mills, Ronnie (Editor)

1988-01-01

Programming languages, computer graphics, neural networks, massively parallel computers, SIMD architecture, algorithms, digital terrain models, sort computation, simulation of charged particle transport on the massively parallel processor and image processing are among the topics discussed.

Parallel Computation of the Jacobian Matrix for Nonlinear Equation Solvers Using MATLAB

NASA Technical Reports Server (NTRS)

Rose, Geoffrey K.; Nguyen, Duc T.; Newman, Brett A.

2017-01-01

Demonstrating speedup for parallel code on a multicore shared memory PC can be challenging in MATLAB due to underlying parallel operations that are often opaque to the user. This can limit potential for improvement of serial code even for the so-called embarrassingly parallel applications. One such application is the computation of the Jacobian matrix inherent to most nonlinear equation solvers. Computation of this matrix represents the primary bottleneck in nonlinear solver speed such that commercial finite element (FE) and multi-body-dynamic (MBD) codes attempt to minimize computations. A timing study using MATLAB's Parallel Computing Toolbox was performed for numerical computation of the Jacobian. Several approaches for implementing parallel code were investigated while only the single program multiple data (spmd) method using composite objects provided positive results. Parallel code speedup is demonstrated but the goal of linear speedup through the addition of processors was not achieved due to PC architecture.

Performance Evaluation in Network-Based Parallel Computing

NASA Technical Reports Server (NTRS)

Dezhgosha, Kamyar

1996-01-01

Network-based parallel computing is emerging as a cost-effective alternative for solving many problems which require use of supercomputers or massively parallel computers. The primary objective of this project has been to conduct experimental research on performance evaluation for clustered parallel computing. First, a testbed was established by augmenting our existing SUNSPARCs' network with PVM (Parallel Virtual Machine) which is a software system for linking clusters of machines. Second, a set of three basic applications were selected. The applications consist of a parallel search, a parallel sort, a parallel matrix multiplication. These application programs were implemented in C programming language under PVM. Third, we conducted performance evaluation under various configurations and problem sizes. Alternative parallel computing models and workload allocations for application programs were explored. The performance metric was limited to elapsed time or response time which in the context of parallel computing can be expressed in terms of speedup. The results reveal that the overhead of communication latency between processes in many cases is the restricting factor to performance. That is, coarse-grain parallelism which requires less frequent communication between processes will result in higher performance in network-based computing. Finally, we are in the final stages of installing an Asynchronous Transfer Mode (ATM) switch and four ATM interfaces (each 155 Mbps) which will allow us to extend our study to newer applications, performance metrics, and configurations.

ARACHNE: A neural-neuroglial network builder with remotely controlled parallel computing

PubMed Central

Rusakov, Dmitri A.; Savtchenko, Leonid P.

2017-01-01

Creating and running realistic models of neural networks has hitherto been a task for computing professionals rather than experimental neuroscientists. This is mainly because such networks usually engage substantial computational resources, the handling of which requires specific programing skills. Here we put forward a newly developed simulation environment ARACHNE: it enables an investigator to build and explore cellular networks of arbitrary biophysical and architectural complexity using the logic of NEURON and a simple interface on a local computer or a mobile device. The interface can control, through the internet, an optimized computational kernel installed on a remote computer cluster. ARACHNE can combine neuronal (wired) and astroglial (extracellular volume-transmission driven) network types and adopt realistic cell models from the NEURON library. The program and documentation (current version) are available at GitHub repository https://github.com/LeonidSavtchenko/Arachne under the MIT License (MIT). PMID:28362877

Optimized R functions for analysis of ecological community data using the R virtual laboratory (RvLab)

PubMed Central

Varsos, Constantinos; Patkos, Theodore; Pavloudi, Christina; Gougousis, Alexandros; Ijaz, Umer Zeeshan; Filiopoulou, Irene; Pattakos, Nikolaos; Vanden Berghe, Edward; Fernández-Guerra, Antonio; Faulwetter, Sarah; Chatzinikolaou, Eva; Pafilis, Evangelos; Bekiari, Chryssoula; Doerr, Martin; Arvanitidis, Christos

2016-01-01

Abstract Background Parallel data manipulation using R has previously been addressed by members of the R community, however most of these studies produce ad hoc solutions that are not readily available to the average R user. Our targeted users, ranging from the expert ecologist/microbiologists to computational biologists, often experience difficulties in finding optimal ways to exploit the full capacity of their computational resources. In addition, improving performance of commonly used R scripts becomes increasingly difficult especially with large datasets. Furthermore, the implementations described here can be of significant interest to expert bioinformaticians or R developers. Therefore, our goals can be summarized as: (i) description of a complete methodology for the analysis of large datasets by combining capabilities of diverse R packages, (ii) presentation of their application through a virtual R laboratory (RvLab) that makes execution of complex functions and visualization of results easy and readily available to the end-user. New information In this paper, the novelty stems from implementations of parallel methodologies which rely on the processing of data on different levels of abstraction and the availability of these processes through an integrated portal. Parallel implementation R packages, such as the pbdMPI (Programming with Big Data – Interface to MPI) package, are used to implement Single Program Multiple Data (SPMD) parallelization on primitive mathematical operations, allowing for interplay with functions of the vegan package. The dplyr and RPostgreSQL R packages are further integrated offering connections to dataframe like objects (databases) as secondary storage solutions whenever memory demands exceed available RAM resources. The RvLab is running on a PC cluster, using version 3.1.2 (2014-10-31) on a x86_64-pc-linux-gnu (64-bit) platform, and offers an intuitive virtual environmet interface enabling users to perform analysis of ecological and microbial communities based on optimized vegan functions. A beta version of the RvLab is available after registration at: https://portal.lifewatchgreece.eu/ PMID:27932907

Optimized R functions for analysis of ecological community data using the R virtual laboratory (RvLab).

PubMed

Varsos, Constantinos; Patkos, Theodore; Oulas, Anastasis; Pavloudi, Christina; Gougousis, Alexandros; Ijaz, Umer Zeeshan; Filiopoulou, Irene; Pattakos, Nikolaos; Vanden Berghe, Edward; Fernández-Guerra, Antonio; Faulwetter, Sarah; Chatzinikolaou, Eva; Pafilis, Evangelos; Bekiari, Chryssoula; Doerr, Martin; Arvanitidis, Christos

2016-01-01

Parallel data manipulation using R has previously been addressed by members of the R community, however most of these studies produce ad hoc solutions that are not readily available to the average R user. Our targeted users, ranging from the expert ecologist/microbiologists to computational biologists, often experience difficulties in finding optimal ways to exploit the full capacity of their computational resources. In addition, improving performance of commonly used R scripts becomes increasingly difficult especially with large datasets. Furthermore, the implementations described here can be of significant interest to expert bioinformaticians or R developers. Therefore, our goals can be summarized as: (i) description of a complete methodology for the analysis of large datasets by combining capabilities of diverse R packages, (ii) presentation of their application through a virtual R laboratory (RvLab) that makes execution of complex functions and visualization of results easy and readily available to the end-user. In this paper, the novelty stems from implementations of parallel methodologies which rely on the processing of data on different levels of abstraction and the availability of these processes through an integrated portal. Parallel implementation R packages, such as the pbdMPI (Programming with Big Data - Interface to MPI) package, are used to implement Single Program Multiple Data (SPMD) parallelization on primitive mathematical operations, allowing for interplay with functions of the vegan package. The dplyr and RPostgreSQL R packages are further integrated offering connections to dataframe like objects (databases) as secondary storage solutions whenever memory demands exceed available RAM resources. The RvLab is running on a PC cluster, using version 3.1.2 (2014-10-31) on a x86_64-pc-linux-gnu (64-bit) platform, and offers an intuitive virtual environmet interface enabling users to perform analysis of ecological and microbial communities based on optimized vegan functions. A beta version of the RvLab is available after registration at: https://portal.lifewatchgreece.eu/.

Parallel Computing Using Web Servers and "Servlets".

ERIC Educational Resources Information Center

Lo, Alfred; Bloor, Chris; Choi, Y. K.

2000-01-01

Describes parallel computing and presents inexpensive ways to implement a virtual parallel computer with multiple Web servers. Highlights include performance measurement of parallel systems; models for using Java and intranet technology including single server, multiple clients and multiple servers, single client; and a comparison of CGI (common…

A class of parallel algorithms for computation of the manipulator inertia matrix

NASA Technical Reports Server (NTRS)

Fijany, Amir; Bejczy, Antal K.

1989-01-01

Parallel and parallel/pipeline algorithms for computation of the manipulator inertia matrix are presented. An algorithm based on composite rigid-body spatial inertia method, which provides better features for parallelization, is used for the computation of the inertia matrix. Two parallel algorithms are developed which achieve the time lower bound in computation. Also described is the mapping of these algorithms with topological variation on a two-dimensional processor array, with nearest-neighbor connection, and with cardinality variation on a linear processor array. An efficient parallel/pipeline algorithm for the linear array was also developed, but at significantly higher efficiency.

A microeconomic scheduler for parallel computers

NASA Technical Reports Server (NTRS)

Stoica, Ion; Abdel-Wahab, Hussein; Pothen, Alex

1995-01-01

We describe a scheduler based on the microeconomic paradigm for scheduling on-line a set of parallel jobs in a multiprocessor system. In addition to the classical objectives of increasing the system throughput and reducing the response time, we consider fairness in allocating system resources among the users, and providing the user with control over the relative performances of his jobs. We associate with every user a savings account in which he receives money at a constant rate. When a user wants to run a job, he creates an expense account for that job to which he transfers money from his savings account. The job uses the funds in its expense account to obtain the system resources it needs for execution. The share of the system resources allocated to the user is directly related to the rate at which the user receives money; the rate at which the user transfers money into a job expense account controls the job's performance. We prove that starvation is not possible in our model. Simulation results show that our scheduler improves both system and user performances in comparison with two different variable partitioning policies. It is also shown to be effective in guaranteeing fairness and providing control over the performance of jobs.

Interfacing HTCondor-CE with OpenStack

NASA Astrophysics Data System (ADS)

Bockelman, B.; Caballero Bejar, J.; Hover, J.

2017-10-01

Over the past few years, Grid Computing technologies have reached a high level of maturity. One key aspect of this success has been the development and adoption of newer Compute Elements to interface the external Grid users with local batch systems. These new Compute Elements allow for better handling of jobs requirements and a more precise management of diverse local resources. However, despite this level of maturity, the Grid Computing world is lacking diversity in local execution platforms. As Grid Computing technologies have historically been driven by the needs of the High Energy Physics community, most resource providers run the platform (operating system version and architecture) that best suits the needs of their particular users. In parallel, the development of virtualization and cloud technologies has accelerated recently, making available a variety of solutions, both commercial and academic, proprietary and open source. Virtualization facilitates performing computational tasks on platforms not available at most computing sites. This work attempts to join the technologies, allowing users to interact with computing sites through one of the standard Computing Elements, HTCondor-CE, but running their jobs within VMs on a local cloud platform, OpenStack, when needed. The system will re-route, in a transparent way, end user jobs into dynamically-launched VM worker nodes when they have requirements that cannot be satisfied by the static local batch system nodes. Also, once the automated mechanisms are in place, it becomes straightforward to allow an end user to invoke a custom Virtual Machine at the site. This will allow cloud resources to be used without requiring the user to establish a separate account. Both scenarios are described in this work.

Electronic Neural Networks

NASA Technical Reports Server (NTRS)

Thakoor, Anil

1990-01-01

Viewgraphs on electronic neural networks for space station are presented. Topics covered include: electronic neural networks; electronic implementations; VLSI/thin film hybrid hardware for neurocomputing; computations with analog parallel processing; features of neuroprocessors; applications of neuroprocessors; neural network hardware for terrain trafficability determination; a dedicated processor for path planning; neural network system interface; neural network for robotic control; error backpropagation algorithm for learning; resource allocation matrix; global optimization neuroprocessor; and electrically programmable read only thin-film synaptic array.

Parallel Computing Strategies for Irregular Algorithms

NASA Technical Reports Server (NTRS)

Biswas, Rupak; Oliker, Leonid; Shan, Hongzhang; Biegel, Bryan (Technical Monitor)

2002-01-01

Parallel computing promises several orders of magnitude increase in our ability to solve realistic computationally-intensive problems, but relies on their efficient mapping and execution on large-scale multiprocessor architectures. Unfortunately, many important applications are irregular and dynamic in nature, making their effective parallel implementation a daunting task. Moreover, with the proliferation of parallel architectures and programming paradigms, the typical scientist is faced with a plethora of questions that must be answered in order to obtain an acceptable parallel implementation of the solution algorithm. In this paper, we consider three representative irregular applications: unstructured remeshing, sparse matrix computations, and N-body problems, and parallelize them using various popular programming paradigms on a wide spectrum of computer platforms ranging from state-of-the-art supercomputers to PC clusters. We present the underlying problems, the solution algorithms, and the parallel implementation strategies. Smart load-balancing, partitioning, and ordering techniques are used to enhance parallel performance. Overall results demonstrate the complexity of efficiently parallelizing irregular algorithms.

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 solution of sparse one-dimensional dynamic programming problems

NASA Technical Reports Server (NTRS)

Nicol, David M.

1989-01-01

Parallel computation offers the potential for quickly solving large computational problems. However, it is often a non-trivial task to effectively use parallel computers. Solution methods must sometimes be reformulated to exploit parallelism; the reformulations are often more complex than their slower serial counterparts. We illustrate these points by studying the parallelization of sparse one-dimensional dynamic programming problems, those which do not obviously admit substantial parallelization. We propose a new method for parallelizing such problems, develop analytic models which help us to identify problems which parallelize well, and compare the performance of our algorithm with existing algorithms on a multiprocessor.

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.

Distributed project scheduling at NASA: Requirements for manual protocols and computer-based support

NASA Technical Reports Server (NTRS)

Richards, Stephen F.

1992-01-01

The increasing complexity of space operations and the inclusion of interorganizational and international groups in the planning and control of space missions lead to requirements for greater communication, coordination, and cooperation among mission schedulers. These schedulers must jointly allocate scarce shared resources among the various operational and mission oriented activities while adhering to all constraints. This scheduling environment is complicated by such factors as the presence of varying perspectives and conflicting objectives among the schedulers, the need for different schedulers to work in parallel, and limited communication among schedulers. Smooth interaction among schedulers requires the use of protocols that govern such issues as resource sharing, authority to update the schedule, and communication of updates. This paper addresses the development and characteristics of such protocols and their use in a distributed scheduling environment that incorporates computer-aided scheduling tools. An example problem is drawn from the domain of Space Shuttle mission planning.

Development of Fuel Shuffling Module for PHISICS

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

Allan Mabe; Andrea Alfonsi; Cristian Rabiti

2013-06-01

PHISICS (Parallel and Highly Innovative Simulation for the INL Code System) [4] code toolkit has been in development at the Idaho National Laboratory. This package is intended to provide a modern analysis tool for reactor physics investigation. It is designed with the mindset to maximize accuracy for a given availability of computational resources and to give state of the art tools to the modern nuclear engineer. This is obtained by implementing several different algorithms and meshing approaches among which the user will be able to choose, in order to optimize his computational resources and accuracy needs. The software is completelymore » modular in order to simplify the independent development of modules by different teams and future maintenance. The package is coupled with the thermo-hydraulic code RELAP5-3D [3]. In the following the structure of the different PHISICS modules is briefly recalled, focusing on the new shuffling module (SHUFFLE), object of this paper.« less

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

Newman, G.A.; Commer, M.

Three-dimensional (3D) geophysical imaging is now receiving considerable attention for electrical conductivity mapping of potential offshore oil and gas reservoirs. The imaging technology employs controlled source electromagnetic (CSEM) and magnetotelluric (MT) fields and treats geological media exhibiting transverse anisotropy. Moreover when combined with established seismic methods, direct imaging of reservoir fluids is possible. Because of the size of the 3D conductivity imaging problem, strategies are required exploiting computational parallelism and optimal meshing. The algorithm thus developed has been shown to scale to tens of thousands of processors. In one imaging experiment, 32,768 tasks/processors on the IBM Watson Research Blue Gene/Lmore » supercomputer were successfully utilized. Over a 24 hour period we were able to image a large scale field data set that previously required over four months of processing time on distributed clusters based on Intel or AMD processors utilizing 1024 tasks on an InfiniBand fabric. Electrical conductivity imaging using massively parallel computational resources produces results that cannot be obtained otherwise and are consistent with timeframes required for practical exploration problems.« less

On the Use of CAD and Cartesian Methods for Aerodynamic Optimization

NASA Technical Reports Server (NTRS)

Nemec, M.; Aftosmis, M. J.; Pulliam, T. H.

2004-01-01

The objective for this paper is to present the development of an optimization capability for Curt3D, a Cartesian inviscid-flow analysis package. We present the construction of a new optimization framework and we focus on the following issues: 1) Component-based geometry parameterization approach using parametric-CAD models and CAPRI. A novel geometry server is introduced that addresses the issue of parallel efficiency while only sparingly consuming CAD resources; 2) The use of genetic and gradient-based algorithms for three-dimensional aerodynamic design problems. The influence of noise on the optimization methods is studied. Our goal is to create a responsive and automated framework that efficiently identifies design modifications that result in substantial performance improvements. In addition, we examine the architectural issues associated with the deployment of a CAD-based approach in a heterogeneous parallel computing environment that contains both CAD workstations and dedicated compute engines. We demonstrate the effectiveness of the framework for a design problem that features topology changes and complex geometry.

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

NASA Astrophysics Data System (ADS)

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

2016-10-01

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

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.

Production Level CFD Code Acceleration for Hybrid Many-Core Architectures

NASA Technical Reports Server (NTRS)

Duffy, Austen C.; Hammond, Dana P.; Nielsen, Eric J.

2012-01-01

In this work, a novel graphics processing unit (GPU) distributed sharing model for hybrid many-core architectures is introduced and employed in the acceleration of a production-level computational fluid dynamics (CFD) code. The latest generation graphics hardware allows multiple processor cores to simultaneously share a single GPU through concurrent kernel execution. This feature has allowed the NASA FUN3D code to be accelerated in parallel with up to four processor cores sharing a single GPU. For codes to scale and fully use resources on these and the next generation machines, codes will need to employ some type of GPU sharing model, as presented in this work. Findings include the effects of GPU sharing on overall performance. A discussion of the inherent challenges that parallel unstructured CFD codes face in accelerator-based computing environments is included, with considerations for future generation architectures. This work was completed by the author in August 2010, and reflects the analysis and results of the time.

RPython high-level synthesis

NASA Astrophysics Data System (ADS)

Cieszewski, Radoslaw; Linczuk, Maciej

2016-09-01

The development of FPGA technology and the increasing complexity of applications in recent decades have forced compilers to move to higher abstraction levels. Compilers interprets an algorithmic description of a desired behavior written in High-Level Languages (HLLs) and translate it to Hardware Description Languages (HDLs). This paper presents a RPython based High-Level synthesis (HLS) compiler. The compiler get the configuration parameters and map RPython program to VHDL. Then, VHDL code can be used to program FPGA chips. In comparison of other technologies usage, FPGAs have the potential to achieve far greater performance than software as a result of omitting the fetch-decode-execute operations of General Purpose Processors (GPUs), and introduce more parallel computation. This can be exploited by utilizing many resources at the same time. Creating parallel algorithms computed with FPGAs in pure HDL is difficult and time consuming. Implementation time can be greatly reduced with High-Level Synthesis compiler. This article describes design methodologies and tools, implementation and first results of created VHDL backend for RPython compiler.

Method and apparatus for offloading compute resources to a flash co-processing appliance

DOEpatents

Tzelnic, Percy; Faibish, Sorin; Gupta, Uday K.; Bent, John; Grider, Gary Alan; Chen, Hsing -bung

2015-10-13

Solid-State Drive (SSD) burst buffer nodes are interposed into a parallel supercomputing cluster to enable fast burst checkpoint of cluster memory to or from nearby interconnected solid-state storage with asynchronous migration between the burst buffer nodes and slower more distant disk storage. The SSD nodes also perform tasks offloaded from the compute nodes or associated with the checkpoint data. For example, the data for the next job is preloaded in the SSD node and very fast uploaded to the respective compute node just before the next job starts. During a job, the SSD nodes perform fast visualization and statistical analysis upon the checkpoint data. The SSD nodes can also perform data reduction and encryption of the checkpoint data.

Computation of the Fluid and Optical Fields About the Stratospheric Observatory for Infrared Astronomy (SOFIA) and the Coupling of Fluids, Dynamics, and Control Laws on Parallel Computers

NASA Technical Reports Server (NTRS)

Atwood, Christopher A.

1993-01-01

The June 1992 to May 1993 grant NCC-2-677 provided for the continued demonstration of Computational Fluid Dynamics (CFD) as applied to the Stratospheric Observatory for Infrared Astronomy (SOFIA). While earlier grant years allowed validation of CFD through comparison against experiments, this year a new design proposal was evaluated. The new configuration would place the cavity aft of the wing, as opposed to the earlier baseline which was located immediately aft of the cockpit. This aft cavity placement allows for simplified structural and aircraft modification requirements, thus lowering the program cost of this national astronomy resource. Three appendices concerning this subject are presented.

Fast parallel algorithm for slicing STL based on pipeline

NASA Astrophysics Data System (ADS)

Ma, Xulong; Lin, Feng; Yao, Bo

2016-05-01

In Additive Manufacturing field, the current researches of data processing mainly focus on a slicing process of large STL files or complicated CAD models. To improve the efficiency and reduce the slicing time, a parallel algorithm has great advantages. However, traditional algorithms can't make full use of multi-core CPU hardware resources. In the paper, a fast parallel algorithm is presented to speed up data processing. A pipeline mode is adopted to design the parallel algorithm. And the complexity of the pipeline algorithm is analyzed theoretically. To evaluate the performance of the new algorithm, effects of threads number and layers number are investigated by a serial of experiments. The experimental results show that the threads number and layers number are two remarkable factors to the speedup ratio. The tendency of speedup versus threads number reveals a positive relationship which greatly agrees with the Amdahl's law, and the tendency of speedup versus layers number also keeps a positive relationship agreeing with Gustafson's law. The new algorithm uses topological information to compute contours with a parallel method of speedup. Another parallel algorithm based on data parallel is used in experiments to show that pipeline parallel mode is more efficient. A case study at last shows a suspending performance of the new parallel algorithm. Compared with the serial slicing algorithm, the new pipeline parallel algorithm can make full use of the multi-core CPU hardware, accelerate the slicing process, and compared with the data parallel slicing algorithm, the new slicing algorithm in this paper adopts a pipeline parallel model, and a much higher speedup ratio and efficiency is achieved.

Integration of Titan supercomputer at OLCF with ATLAS Production System

NASA Astrophysics Data System (ADS)

Barreiro Megino, F.; De, K.; Jha, S.; Klimentov, A.; Maeno, T.; Nilsson, P.; Oleynik, D.; Padolski, S.; Panitkin, S.; Wells, J.; Wenaus, T.; ATLAS Collaboration

2017-10-01

The PanDA (Production and Distributed Analysis) workload management system was developed to meet the scale and complexity of distributed computing for the ATLAS experiment. PanDA managed resources are distributed worldwide, on hundreds of computing sites, with thousands of physicists accessing hundreds of Petabytes of data and the rate of data processing already exceeds Exabyte per year. While PanDA currently uses more than 200,000 cores at well over 100 Grid sites, future LHC data taking runs will require more resources than Grid computing can possibly provide. Additional computing and storage resources are required. Therefore ATLAS is engaged in an ambitious program to expand the current computing model to include additional resources such as the opportunistic use of supercomputers. In this paper we will describe a project aimed at integration of ATLAS Production System with Titan supercomputer at Oak Ridge Leadership Computing Facility (OLCF). Current approach utilizes modified PanDA Pilot framework for job submission to Titan’s batch queues and local data management, with lightweight MPI wrappers to run single node workloads in parallel on Titan’s multi-core worker nodes. It provides for running of standard ATLAS production jobs on unused resources (backfill) on Titan. The system already allowed ATLAS to collect on Titan millions of core-hours per month, execute hundreds of thousands jobs, while simultaneously improving Titans utilization efficiency. We will discuss the details of the implementation, current experience with running the system, as well as future plans aimed at improvements in scalability and efficiency. Notice: This manuscript has been authored, by employees of Brookhaven Science Associates, LLC under Contract No. DE-AC02-98CH10886 with the U.S. Department of Energy. The publisher by accepting the manuscript for publication acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes.

A CFD Heterogeneous Parallel Solver Based on Collaborating CPU and GPU

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

The Automated Instrumentation and Monitoring System (AIMS) reference manual

NASA Technical Reports Server (NTRS)

Yan, Jerry; Hontalas, Philip; Listgarten, Sherry

1993-01-01

Whether a researcher is designing the 'next parallel programming paradigm,' another 'scalable multiprocessor' or investigating resource allocation algorithms for multiprocessors, a facility that enables parallel program execution to be captured and displayed is invaluable. Careful analysis of execution traces can help computer designers and software architects to uncover system behavior and to take advantage of specific application characteristics and hardware features. A software tool kit that facilitates performance evaluation of parallel applications on multiprocessors is described. The Automated Instrumentation and Monitoring System (AIMS) has four major software components: a source code instrumentor which automatically inserts active event recorders into the program's source code before compilation; a run time performance-monitoring library, which collects performance data; a trace file animation and analysis tool kit which reconstructs program execution from the trace file; and a trace post-processor which compensate for data collection overhead. Besides being used as prototype for developing new techniques for instrumenting, monitoring, and visualizing parallel program execution, AIMS is also being incorporated into the run-time environments of various hardware test beds to evaluate their impact on user productivity. Currently, AIMS instrumentors accept FORTRAN and C parallel programs written for Intel's NX operating system on the iPSC family of multi computers. A run-time performance-monitoring library for the iPSC/860 is included in this release. We plan to release monitors for other platforms (such as PVM and TMC's CM-5) in the near future. Performance data collected can be graphically displayed on workstations (e.g. Sun Sparc and SGI) supporting X-Windows (in particular, Xl IR5, Motif 1.1.3).

Implementing Scientific Simulation Codes Highly Tailored for Vector Architectures Using Custom Configurable Computing Machines

NASA Technical Reports Server (NTRS)

Rutishauser, David

2006-01-01

The motivation for this work comes from an observation that amidst the push for Massively Parallel (MP) solutions to high-end computing problems such as numerical physical simulations, large amounts of legacy code exist that are highly optimized for vector supercomputers. Because re-hosting legacy code often requires a complete re-write of the original code, which can be a very long and expensive effort, this work examines the potential to exploit reconfigurable computing machines in place of a vector supercomputer to implement an essentially unmodified legacy source code. Custom and reconfigurable computing resources could be used to emulate an original application's target platform to the extent required to achieve high performance. To arrive at an architecture that delivers the desired performance subject to limited resources involves solving a multi-variable optimization problem with constraints. Prior research in the area of reconfigurable computing has demonstrated that designing an optimum hardware implementation of a given application under hardware resource constraints is an NP-complete problem. The premise of the approach is that the general issue of applying reconfigurable computing resources to the implementation of an application, maximizing the performance of the computation subject to physical resource constraints, can be made a tractable problem by assuming a computational paradigm, such as vector processing. This research contributes a formulation of the problem and a methodology to design a reconfigurable vector processing implementation of a given application that satisfies a performance metric. A generic, parametric, architectural framework for vector processing implemented in reconfigurable logic is developed as a target for a scheduling/mapping algorithm that maps an input computation to a given instance of the architecture. This algorithm is integrated with an optimization framework to arrive at a specification of the architecture parameters that attempts to minimize execution time, while staying within resource constraints. The flexibility of using a custom reconfigurable implementation is exploited in a unique manner to leverage the lessons learned in vector supercomputer development. The vector processing framework is tailored to the application, with variable parameters that are fixed in traditional vector processing. Benchmark data that demonstrates the functionality and utility of the approach is presented. The benchmark data includes an identified bottleneck in a real case study example vector code, the NASA Langley Terminal Area Simulation System (TASS) application.

Processing Solutions for Big Data in Astronomy

NASA Astrophysics Data System (ADS)

Fillatre, L.; Lepiller, D.

2016-09-01

This paper gives a simple introduction to processing solutions applied to massive amounts of data. It proposes a general presentation of the Big Data paradigm. The Hadoop framework, which is considered as the pioneering processing solution for Big Data, is described together with YARN, the integrated Hadoop tool for resource allocation. This paper also presents the main tools for the management of both the storage (NoSQL solutions) and computing capacities (MapReduce parallel processing schema) of a cluster of machines. Finally, more recent processing solutions like Spark are discussed. Big Data frameworks are now able to run complex applications while keeping the programming simple and greatly improving the computing speed.

Google Earth Engine: a new cloud-computing platform for global-scale earth observation data and analysis

NASA Astrophysics Data System (ADS)

Moore, R. T.; Hansen, M. C.

2011-12-01

Google Earth Engine is a new technology platform that enables monitoring and measurement of changes in the earth's environment, at planetary scale, on a large catalog of earth observation data. The platform offers intrinsically-parallel computational access to thousands of computers in Google's data centers. Initial efforts have focused primarily on global forest monitoring and measurement, in support of REDD+ activities in the developing world. The intent is to put this platform into the hands of scientists and developing world nations, in order to advance the broader operational deployment of existing scientific methods, and strengthen the ability for public institutions and civil society to better understand, manage and report on the state of their natural resources. Earth Engine currently hosts online nearly the complete historical Landsat archive of L5 and L7 data collected over more than twenty-five years. Newly-collected Landsat imagery is downloaded from USGS EROS Center into Earth Engine on a daily basis. Earth Engine also includes a set of historical and current MODIS data products. The platform supports generation, on-demand, of spatial and temporal mosaics, "best-pixel" composites (for example to remove clouds and gaps in satellite imagery), as well as a variety of spectral indices. Supervised learning methods are available over the Landsat data catalog. The platform also includes a new application programming framework, or "API", that allows scientists access to these computational and data resources, to scale their current algorithms or develop new ones. Under the covers of the Google Earth Engine API is an intrinsically-parallel image-processing system. Several forest monitoring applications powered by this API are currently in development and expected to be operational in 2011. Combining science with massive data and technology resources in a cloud-computing framework can offer advantages of computational speed, ease-of-use and collaboration, as well as transparency in data and methods. Methods developed for global processing of MODIS data to map land cover are being adopted for use with Landsat data. Specifically, the MODIS Vegetation Continuous Field product methodology has been applied for mapping forest extent and change at national scales using Landsat time-series data sets. Scaling this method to continental and global scales is enabled by Google Earth Engine computing capabilities. By combining the supervised learning VCF approach with the Landsat archive and cloud computing, unprecedented monitoring of land cover dynamics is enabled.

A lightweight, flow-based toolkit for parallel and distributed bioinformatics pipelines

PubMed Central

2011-01-01

Background Bioinformatic analyses typically proceed as chains of data-processing tasks. A pipeline, or 'workflow', is a well-defined protocol, with a specific structure defined by the topology of data-flow interdependencies, and a particular functionality arising from the data transformations applied at each step. In computer science, the dataflow programming (DFP) paradigm defines software systems constructed in this manner, as networks of message-passing components. Thus, bioinformatic workflows can be naturally mapped onto DFP concepts. Results To enable the flexible creation and execution of bioinformatics dataflows, we have written a modular framework for parallel pipelines in Python ('PaPy'). A PaPy workflow is created from re-usable components connected by data-pipes into a directed acyclic graph, which together define nested higher-order map functions. The successive functional transformations of input data are evaluated on flexibly pooled compute resources, either local or remote. Input items are processed in batches of adjustable size, all flowing one to tune the trade-off between parallelism and lazy-evaluation (memory consumption). An add-on module ('NuBio') facilitates the creation of bioinformatics workflows by providing domain specific data-containers (e.g., for biomolecular sequences, alignments, structures) and functionality (e.g., to parse/write standard file formats). Conclusions PaPy offers a modular framework for the creation and deployment of parallel and distributed data-processing workflows. Pipelines derive their functionality from user-written, data-coupled components, so PaPy also can be viewed as a lightweight toolkit for extensible, flow-based bioinformatics data-processing. The simplicity and flexibility of distributed PaPy pipelines may help users bridge the gap between traditional desktop/workstation and grid computing. PaPy is freely distributed as open-source Python code at http://muralab.org/PaPy, and includes extensive documentation and annotated usage examples. PMID:21352538

A lightweight, flow-based toolkit for parallel and distributed bioinformatics pipelines.

PubMed

Cieślik, Marcin; Mura, Cameron

2011-02-25

Bioinformatic analyses typically proceed as chains of data-processing tasks. A pipeline, or 'workflow', is a well-defined protocol, with a specific structure defined by the topology of data-flow interdependencies, and a particular functionality arising from the data transformations applied at each step. In computer science, the dataflow programming (DFP) paradigm defines software systems constructed in this manner, as networks of message-passing components. Thus, bioinformatic workflows can be naturally mapped onto DFP concepts. To enable the flexible creation and execution of bioinformatics dataflows, we have written a modular framework for parallel pipelines in Python ('PaPy'). A PaPy workflow is created from re-usable components connected by data-pipes into a directed acyclic graph, which together define nested higher-order map functions. The successive functional transformations of input data are evaluated on flexibly pooled compute resources, either local or remote. Input items are processed in batches of adjustable size, all flowing one to tune the trade-off between parallelism and lazy-evaluation (memory consumption). An add-on module ('NuBio') facilitates the creation of bioinformatics workflows by providing domain specific data-containers (e.g., for biomolecular sequences, alignments, structures) and functionality (e.g., to parse/write standard file formats). PaPy offers a modular framework for the creation and deployment of parallel and distributed data-processing workflows. Pipelines derive their functionality from user-written, data-coupled components, so PaPy also can be viewed as a lightweight toolkit for extensible, flow-based bioinformatics data-processing. The simplicity and flexibility of distributed PaPy pipelines may help users bridge the gap between traditional desktop/workstation and grid computing. PaPy is freely distributed as open-source Python code at http://muralab.org/PaPy, and includes extensive documentation and annotated usage examples.

CMS Readiness for Multi-Core Workload Scheduling

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

Perez-Calero Yzquierdo, A.; Balcas, J.; Hernandez, J.

In the present run of the LHC, CMS data reconstruction and simulation algorithms benefit greatly from being executed as multiple threads running on several processor cores. The complexity of the Run 2 events requires parallelization of the code to reduce the memory-per- core footprint constraining serial execution programs, thus optimizing the exploitation of present multi-core processor architectures. The allocation of computing resources for multi-core tasks, however, becomes a complex problem in itself. The CMS workload submission infrastructure employs multi-slot partitionable pilots, built on HTCondor and GlideinWMS native features, to enable scheduling of single and multi-core jobs simultaneously. This provides amore » solution for the scheduling problem in a uniform way across grid sites running a diversity of gateways to compute resources and batch system technologies. This paper presents this strategy and the tools on which it has been implemented. The experience of managing multi-core resources at the Tier-0 and Tier-1 sites during 2015, along with the deployment phase to Tier-2 sites during early 2016 is reported. The process of performance monitoring and optimization to achieve efficient and flexible use of the resources is also described.« less

CMS readiness for multi-core workload scheduling

NASA Astrophysics Data System (ADS)

Perez-Calero Yzquierdo, A.; Balcas, J.; Hernandez, J.; Aftab Khan, F.; Letts, J.; Mason, D.; Verguilov, V.

2017-10-01

In the present run of the LHC, CMS data reconstruction and simulation algorithms benefit greatly from being executed as multiple threads running on several processor cores. The complexity of the Run 2 events requires parallelization of the code to reduce the memory-per- core footprint constraining serial execution programs, thus optimizing the exploitation of present multi-core processor architectures. The allocation of computing resources for multi-core tasks, however, becomes a complex problem in itself. The CMS workload submission infrastructure employs multi-slot partitionable pilots, built on HTCondor and GlideinWMS native features, to enable scheduling of single and multi-core jobs simultaneously. This provides a solution for the scheduling problem in a uniform way across grid sites running a diversity of gateways to compute resources and batch system technologies. This paper presents this strategy and the tools on which it has been implemented. The experience of managing multi-core resources at the Tier-0 and Tier-1 sites during 2015, along with the deployment phase to Tier-2 sites during early 2016 is reported. The process of performance monitoring and optimization to achieve efficient and flexible use of the resources is also described.

Grid Computing Environment using a Beowulf Cluster

NASA Astrophysics Data System (ADS)

Alanis, Fransisco; Mahmood, Akhtar

2003-10-01

Custom-made Beowulf clusters using PCs are currently replacing expensive supercomputers to carry out complex scientific computations. At the University of Texas - Pan American, we built a 8 Gflops Beowulf Cluster for doing HEP research using RedHat Linux 7.3 and the LAM-MPI middleware. We will describe how we built and configured our Cluster, which we have named the Sphinx Beowulf Cluster. We will describe the results of our cluster benchmark studies and the run-time plots of several parallel application codes that were compiled in C on the cluster using the LAM-XMPI graphics user environment. We will demonstrate a "simple" prototype grid environment, where we will submit and run parallel jobs remotely across multiple cluster nodes over the internet from the presentation room at Texas Tech. University. The Sphinx Beowulf Cluster will be used for monte-carlo grid test-bed studies for the LHC-ATLAS high energy physics experiment. Grid is a new IT concept for the next generation of the "Super Internet" for high-performance computing. The Grid will allow scientist worldwide to view and analyze huge amounts of data flowing from the large-scale experiments in High Energy Physics. The Grid is expected to bring together geographically and organizationally dispersed computational resources, such as CPUs, storage systems, communication systems, and data sources.

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

Gooding, Thomas M.

Distributing an executable job load file to compute nodes in a parallel computer, the parallel computer comprising a plurality of compute nodes, including: determining, by a compute node in the parallel computer, whether the compute node is participating in a job; determining, by the compute node in the parallel computer, whether a descendant compute node is participating in the job; responsive to determining that the compute node is participating in the job or that the descendant compute node is participating in the job, communicating, by the compute node to a parent compute node, an identification of a data communications linkmore » over which the compute node receives data from the parent compute node; constructing a class route for the job, wherein the class route identifies all compute nodes participating in the job; and broadcasting the executable load file for the job along the class route for the job.« less

SIAM Conference on Parallel Processing for Scientific Computing, 4th, Chicago, IL, Dec. 11-13, 1989, Proceedings

NASA Technical Reports Server (NTRS)

Dongarra, Jack (Editor); Messina, Paul (Editor); Sorensen, Danny C. (Editor); Voigt, Robert G. (Editor)

1990-01-01

Attention is given to such topics as an evaluation of block algorithm variants in LAPACK and presents a large-grain parallel sparse system solver, a multiprocessor method for the solution of the generalized Eigenvalue problem on an interval, and a parallel QR algorithm for iterative subspace methods on the CM2. A discussion of numerical methods includes the topics of asynchronous numerical solutions of PDEs on parallel computers, parallel homotopy curve tracking on a hypercube, and solving Navier-Stokes equations on the Cedar Multi-Cluster system. A section on differential equations includes a discussion of a six-color procedure for the parallel solution of elliptic systems using the finite quadtree structure, data parallel algorithms for the finite element method, and domain decomposition methods in aerodynamics. Topics dealing with massively parallel computing include hypercube vs. 2-dimensional meshes and massively parallel computation of conservation laws. Performance and tools are also discussed.

MOLNs: A CLOUD PLATFORM FOR INTERACTIVE, REPRODUCIBLE, AND SCALABLE SPATIAL STOCHASTIC COMPUTATIONAL EXPERIMENTS IN SYSTEMS BIOLOGY USING PyURDME

PubMed Central

Drawert, Brian; Trogdon, Michael; Toor, Salman; Petzold, Linda; Hellander, Andreas

2017-01-01

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. PMID:28190948

Towards a Cloud Computing Environment: Near Real-time Cloud Product Processing and Distribution for Next Generation Satellites

NASA Astrophysics Data System (ADS)

Nguyen, L.; Chee, T.; Minnis, P.; Palikonda, R.; Smith, W. L., Jr.; Spangenberg, D.

2016-12-01

The NASA LaRC Satellite ClOud and Radiative Property retrieval System (SatCORPS) processes and derives near real-time (NRT) global cloud products from operational geostationary satellite imager datasets. These products are being used in NRT to improve forecast model, aircraft icing warnings, and support aircraft field campaigns. Next generation satellites, such as the Japanese Himawari-8 and the upcoming NOAA GOES-R, present challenges for NRT data processing and product dissemination due to the increase in temporal and spatial resolution. The volume of data is expected to increase to approximately 10 folds. This increase in data volume will require additional IT resources to keep up with the processing demands to satisfy NRT requirements. In addition, these resources are not readily available due to cost and other technical limitations. To anticipate and meet these computing resource requirements, we have employed a hybrid cloud computing environment to augment the generation of SatCORPS products. This paper will describe the workflow to ingest, process, and distribute SatCORPS products and the technologies used. Lessons learn from working on both AWS Clouds and GovCloud will be discussed: benefits, similarities, and differences that could impact decision to use cloud computing and storage. A detail cost analysis will be presented. In addition, future cloud utilization, parallelization, and architecture layout will be discussed for GOES-R.

Multicore job scheduling in the Worldwide LHC Computing Grid

NASA Astrophysics Data System (ADS)

Forti, A.; Pérez-Calero Yzquierdo, A.; Hartmann, T.; Alef, M.; Lahiff, A.; Templon, J.; Dal Pra, S.; Gila, M.; Skipsey, S.; Acosta-Silva, C.; Filipcic, A.; Walker, R.; Walker, C. J.; Traynor, D.; Gadrat, S.

2015-12-01

After the successful first run of the LHC, data taking is scheduled to restart in Summer 2015 with experimental conditions leading to increased data volumes and event complexity. In order to process the data generated in such scenario and exploit the multicore architectures of current CPUs, the LHC experiments have developed parallelized software for data reconstruction and simulation. However, a good fraction of their computing effort is still expected to be executed as single-core tasks. Therefore, jobs with diverse resources requirements will be distributed across the Worldwide LHC Computing Grid (WLCG), making workload scheduling a complex problem in itself. In response to this challenge, the WLCG Multicore Deployment Task Force has been created in order to coordinate the joint effort from experiments and WLCG sites. The main objective is to ensure the convergence of approaches from the different LHC Virtual Organizations (VOs) to make the best use of the shared resources in order to satisfy their new computing needs, minimizing any inefficiency originated from the scheduling mechanisms, and without imposing unnecessary complexities in the way sites manage their resources. This paper describes the activities and progress of the Task Force related to the aforementioned topics, including experiences from key sites on how to best use different batch system technologies, the evolution of workload submission tools by the experiments and the knowledge gained from scale tests of the different proposed job submission strategies.

Towards anatomic scale agent-based modeling with a massively parallel spatially explicit general-purpose model of enteric tissue (SEGMEnT_HPC).

PubMed

Cockrell, Robert Chase; Christley, Scott; Chang, Eugene; An, Gary

2015-01-01

Perhaps the greatest challenge currently facing the biomedical research community is the ability to integrate highly detailed cellular and molecular mechanisms to represent clinical disease states as a pathway to engineer effective therapeutics. This is particularly evident in the representation of organ-level pathophysiology in terms of abnormal tissue structure, which, through histology, remains a mainstay in disease diagnosis and staging. As such, being able to generate anatomic scale simulations is a highly desirable goal. While computational limitations have previously constrained the size and scope of multi-scale computational models, advances in the capacity and availability of high-performance computing (HPC) resources have greatly expanded the ability of computational models of biological systems to achieve anatomic, clinically relevant scale. Diseases of the intestinal tract are exemplary examples of pathophysiological processes that manifest at multiple scales of spatial resolution, with structural abnormalities present at the microscopic, macroscopic and organ-levels. In this paper, we describe a novel, massively parallel computational model of the gut, the Spatially Explicitly General-purpose Model of Enteric Tissue_HPC (SEGMEnT_HPC), which extends an existing model of the gut epithelium, SEGMEnT, in order to create cell-for-cell anatomic scale simulations. We present an example implementation of SEGMEnT_HPC that simulates the pathogenesis of ileal pouchitis, and important clinical entity that affects patients following remedial surgery for ulcerative colitis.

High speed civil transport: Sonic boom softening and aerodynamic optimization

NASA Technical Reports Server (NTRS)

Cheung, Samson

1994-01-01

An improvement in sonic boom extrapolation techniques has been the desire of aerospace designers for years. This is because the linear acoustic theory developed in the 60's is incapable of predicting the nonlinear phenomenon of shock wave propagation. On the other hand, CFD techniques are too computationally expensive to employ on sonic boom problems. Therefore, this research focused on the development of a fast and accurate sonic boom extrapolation method that solves the Euler equations for axisymmetric flow. This new technique has brought the sonic boom extrapolation techniques up to the standards of the 90's. Parallel computing is a fast growing subject in the field of computer science because of its promising speed. A new optimizer (IIOWA) for the parallel computing environment has been developed and tested for aerodynamic drag minimization. This is a promising method for CFD optimization making use of the computational resources of workstations, which unlike supercomputers can spend most of their time idle. Finally, the OAW concept is attractive because of its overall theoretical performance. In order to fully understand the concept, a wind-tunnel model was built and is currently being tested at NASA Ames Research Center. The CFD calculations performed under this cooperative agreement helped to identify the problem of the flow separation, and also aided the design by optimizing the wing deflection for roll trim.

TU-AB-BRC-12: Optimized Parallel MonteCarlo Dose Calculations for Secondary MU Checks

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

French, S; Nazareth, D; Bellor, M

Purpose: Secondary MU checks are an important tool used during a physics review of a treatment plan. Commercial software packages offer varying degrees of theoretical dose calculation accuracy, depending on the modality involved. Dose calculations of VMAT plans are especially prone to error due to the large approximations involved. Monte Carlo (MC) methods are not commonly used due to their long run times. We investigated two methods to increase the computational efficiency of MC dose simulations with the BEAMnrc code. Distributed computing resources, along with optimized code compilation, will allow for accurate and efficient VMAT dose calculations. Methods: The BEAMnrcmore » package was installed on a high performance computing cluster accessible to our clinic. MATLAB and PYTHON scripts were developed to convert a clinical VMAT DICOM plan into BEAMnrc input files. The BEAMnrc installation was optimized by running the VMAT simulations through profiling tools which indicated the behavior of the constituent routines in the code, e.g. the bremsstrahlung splitting routine, and the specified random number generator. This information aided in determining the most efficient compiling parallel configuration for the specific CPU’s available on our cluster, resulting in the fastest VMAT simulation times. Our method was evaluated with calculations involving 10{sup 8} – 10{sup 9} particle histories which are sufficient to verify patient dose using VMAT. Results: Parallelization allowed the calculation of patient dose on the order of 10 – 15 hours with 100 parallel jobs. Due to the compiler optimization process, further speed increases of 23% were achieved when compared with the open-source compiler BEAMnrc packages. Conclusion: Analysis of the BEAMnrc code allowed us to optimize the compiler configuration for VMAT dose calculations. In future work, the optimized MC code, in conjunction with the parallel processing capabilities of BEAMnrc, will be applied to provide accurate and efficient secondary MU checks.« less

Handling Big Data in Medical Imaging: Iterative Reconstruction with Large-Scale Automated Parallel Computation

PubMed Central

Lee, Jae H.; Yao, Yushu; Shrestha, Uttam; Gullberg, Grant T.; Seo, Youngho

2014-01-01

The primary goal of this project is to implement the iterative statistical image reconstruction algorithm, in this case maximum likelihood expectation maximum (MLEM) used for dynamic cardiac single photon emission computed tomography, on Spark/GraphX. This involves porting the algorithm to run on large-scale parallel computing systems. Spark is an easy-to- program software platform that can handle large amounts of data in parallel. GraphX is a graph analytic system running on top of Spark to handle graph and sparse linear algebra operations in parallel. The main advantage of implementing MLEM algorithm in Spark/GraphX is that it allows users to parallelize such computation without any expertise in parallel computing or prior knowledge in computer science. In this paper we demonstrate a successful implementation of MLEM in Spark/GraphX and present the performance gains with the goal to eventually make it useable in clinical setting. PMID:27081299

Handling Big Data in Medical Imaging: Iterative Reconstruction with Large-Scale Automated Parallel Computation.

PubMed

Lee, Jae H; Yao, Yushu; Shrestha, Uttam; Gullberg, Grant T; Seo, Youngho

2014-11-01

The primary goal of this project is to implement the iterative statistical image reconstruction algorithm, in this case maximum likelihood expectation maximum (MLEM) used for dynamic cardiac single photon emission computed tomography, on Spark/GraphX. This involves porting the algorithm to run on large-scale parallel computing systems. Spark is an easy-to- program software platform that can handle large amounts of data in parallel. GraphX is a graph analytic system running on top of Spark to handle graph and sparse linear algebra operations in parallel. The main advantage of implementing MLEM algorithm in Spark/GraphX is that it allows users to parallelize such computation without any expertise in parallel computing or prior knowledge in computer science. In this paper we demonstrate a successful implementation of MLEM in Spark/GraphX and present the performance gains with the goal to eventually make it useable in clinical setting.

ANL statement of site strategy for computing workstations

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

Fenske, K.R.; Boxberger, L.M.; Amiot, L.W.

1991-11-01

This Statement of Site Strategy describes the procedure at Argonne National Laboratory for defining, acquiring, using, and evaluating scientific and office workstations and related equipment and software in accord with DOE Order 1360.1A (5-30-85), and Laboratory policy. It is Laboratory policy to promote the installation and use of computing workstations to improve productivity and communications for both programmatic and support personnel, to ensure that computing workstations acquisitions meet the expressed need in a cost-effective manner, and to ensure that acquisitions of computing workstations are in accord with Laboratory and DOE policies. The overall computing site strategy at ANL is tomore » develop a hierarchy of integrated computing system resources to address the current and future computing needs of the laboratory. The major system components of this hierarchical strategy are: Supercomputers, Parallel computers, Centralized general purpose computers, Distributed multipurpose minicomputers, and Computing workstations and office automation support systems. Computing workstations include personal computers, scientific and engineering workstations, computer terminals, microcomputers, word processing and office automation electronic workstations, and associated software and peripheral devices costing less than $25,000 per item.« less

Use of Multiple GPUs to Speedup the Execution of a Three-Dimensional Computational Model of the Innate Immune System

NASA Astrophysics Data System (ADS)

Xavier, M. P.; do Nascimento, T. M.; dos Santos, R. W.; Lobosco, M.

2014-03-01

The development of computational systems that mimics the physiological response of organs or even the entire body is a complex task. One of the issues that makes this task extremely complex is the huge computational resources needed to execute the simulations. For this reason, the use of parallel computing is mandatory. In this work, we focus on the simulation of temporal and spatial behaviour of some human innate immune system cells and molecules in a small three-dimensional section of a tissue. To perform this simulation, we use multiple Graphics Processing Units (GPUs) in a shared-memory environment. Despite of high initialization and communication costs imposed by the use of GPUs, the techniques used to implement the HIS simulator have shown to be very effective to achieve this purpose.

Life on the line: the therapeutic potentials of computer-mediated conversation.

PubMed

Miller, J K; Gergen, K J

1998-04-01

In what ways are computer networking practices comparable to face-to-face therapy? With the exponential increase in computer-mediated communication and the increasing numbers of people joining topically based computer networks, the potential for grass-roots therapeutic (or antitherapeutic) interchange is greatly augmented. Here we report the results of research into exchanges on an electronic bulletin board devoted to the topic of suicide. Over an 11-month period participants offered each other valuable resources in terms of validation of experience, sympathy, acceptance, and encouragement. They also asked provocative questions and furnished broad-ranging advice. Hostile entries were rare. However, there were few communiques that parallel the change-inducing practices more frequent within many therapeutic settings. In effect, on-line dialogues seemed more sustaining than transforming. Further limits and potentials of on-line communication are explored.

Parallel Markov chain Monte Carlo - bridging the gap to high-performance Bayesian computation in animal breeding and genetics.

PubMed

Wu, Xiao-Lin; Sun, Chuanyu; Beissinger, Timothy M; Rosa, Guilherme Jm; Weigel, Kent A; Gatti, Natalia de Leon; Gianola, Daniel

2012-09-25

Most Bayesian models for the analysis of complex traits are not analytically tractable and inferences are based on computationally intensive techniques. This is true of Bayesian models for genome-enabled selection, which uses whole-genome molecular data to predict the genetic merit of candidate animals for breeding purposes. In this regard, parallel computing can overcome the bottlenecks that can arise from series computing. Hence, a major goal of the present study is to bridge the gap to high-performance Bayesian computation in the context of animal breeding and genetics. Parallel Monte Carlo Markov chain algorithms and strategies are described in the context of animal breeding and genetics. Parallel Monte Carlo algorithms are introduced as a starting point including their applications to computing single-parameter and certain multiple-parameter models. Then, two basic approaches for parallel Markov chain Monte Carlo are described: one aims at parallelization within a single chain; the other is based on running multiple chains, yet some variants are discussed as well. Features and strategies of the parallel Markov chain Monte Carlo are illustrated using real data, including a large beef cattle dataset with 50K SNP genotypes. Parallel Markov chain Monte Carlo algorithms are useful for computing complex Bayesian models, which does not only lead to a dramatic speedup in computing but can also be used to optimize model parameters in complex Bayesian models. Hence, we anticipate that use of parallel Markov chain Monte Carlo will have a profound impact on revolutionizing the computational tools for genomic selection programs.

Parallel Markov chain Monte Carlo - bridging the gap to high-performance Bayesian computation in animal breeding and genetics

PubMed Central

2012-01-01

Background Most Bayesian models for the analysis of complex traits are not analytically tractable and inferences are based on computationally intensive techniques. This is true of Bayesian models for genome-enabled selection, which uses whole-genome molecular data to predict the genetic merit of candidate animals for breeding purposes. In this regard, parallel computing can overcome the bottlenecks that can arise from series computing. Hence, a major goal of the present study is to bridge the gap to high-performance Bayesian computation in the context of animal breeding and genetics. Results Parallel Monte Carlo Markov chain algorithms and strategies are described in the context of animal breeding and genetics. Parallel Monte Carlo algorithms are introduced as a starting point including their applications to computing single-parameter and certain multiple-parameter models. Then, two basic approaches for parallel Markov chain Monte Carlo are described: one aims at parallelization within a single chain; the other is based on running multiple chains, yet some variants are discussed as well. Features and strategies of the parallel Markov chain Monte Carlo are illustrated using real data, including a large beef cattle dataset with 50K SNP genotypes. Conclusions Parallel Markov chain Monte Carlo algorithms are useful for computing complex Bayesian models, which does not only lead to a dramatic speedup in computing but can also be used to optimize model parameters in complex Bayesian models. Hence, we anticipate that use of parallel Markov chain Monte Carlo will have a profound impact on revolutionizing the computational tools for genomic selection programs. PMID:23009363

Scalable and fast heterogeneous molecular simulation with predictive parallelization schemes

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

Guzman, Horacio V.; Junghans, Christoph; Kremer, Kurt

Multiscale and inhomogeneous molecular systems are challenging topics in the field of molecular simulation. In particular, modeling biological systems in the context of multiscale simulations and exploring material properties are driving a permanent development of new simulation methods and optimization algorithms. In computational terms, those methods require parallelization schemes that make a productive use of computational resources for each simulation and from its genesis. Here, we introduce the heterogeneous domain decomposition approach, which is a combination of an heterogeneity-sensitive spatial domain decomposition with an a priori rearrangement of subdomain walls. Within this approach and paper, the theoretical modeling and scalingmore » laws for the force computation time are proposed and studied as a function of the number of particles and the spatial resolution ratio. We also show the new approach capabilities, by comparing it to both static domain decomposition algorithms and dynamic load-balancing schemes. Specifically, two representative molecular systems have been simulated and compared to the heterogeneous domain decomposition proposed in this work. Finally, these two systems comprise an adaptive resolution simulation of a biomolecule solvated in water and a phase-separated binary Lennard-Jones fluid.« less

Scalable and fast heterogeneous molecular simulation with predictive parallelization schemes

DOE PAGES

Guzman, Horacio V.; Junghans, Christoph; Kremer, Kurt; ...

2017-11-27

Multiscale and inhomogeneous molecular systems are challenging topics in the field of molecular simulation. In particular, modeling biological systems in the context of multiscale simulations and exploring material properties are driving a permanent development of new simulation methods and optimization algorithms. In computational terms, those methods require parallelization schemes that make a productive use of computational resources for each simulation and from its genesis. Here, we introduce the heterogeneous domain decomposition approach, which is a combination of an heterogeneity-sensitive spatial domain decomposition with an a priori rearrangement of subdomain walls. Within this approach and paper, the theoretical modeling and scalingmore » laws for the force computation time are proposed and studied as a function of the number of particles and the spatial resolution ratio. We also show the new approach capabilities, by comparing it to both static domain decomposition algorithms and dynamic load-balancing schemes. Specifically, two representative molecular systems have been simulated and compared to the heterogeneous domain decomposition proposed in this work. Finally, these two systems comprise an adaptive resolution simulation of a biomolecule solvated in water and a phase-separated binary Lennard-Jones fluid.« less

Simulation of 2D Kinetic Effects in Plasmas using the Grid Based Continuum Code LOKI

NASA Astrophysics Data System (ADS)

Banks, Jeffrey; Berger, Richard; Chapman, Tom; Brunner, Stephan

2016-10-01

Kinetic simulation of multi-dimensional plasma waves through direct discretization of the Vlasov equation is a useful tool to study many physical interactions and is particularly attractive for situations where minimal fluctuation levels are desired, for instance, when measuring growth rates of plasma wave instabilities. However, direct discretization of phase space can be computationally expensive, and as a result there are few examples of published results using Vlasov codes in more than a single configuration space dimension. In an effort to fill this gap we have developed the Eulerian-based kinetic code LOKI that evolves the Vlasov-Poisson system in 2+2-dimensional phase space. The code is designed to reduce the cost of phase-space computation by using fully 4th order accurate conservative finite differencing, while retaining excellent parallel scalability that efficiently uses large scale computing resources. In this poster I will discuss the algorithms used in the code as well as some aspects of their parallel implementation using MPI. I will also overview simulation results of basic plasma wave instabilities relevant to laser plasma interaction, which have been obtained using the code.

Parallel approach in RDF query processing

NASA Astrophysics Data System (ADS)

Vajgl, Marek; Parenica, Jan

2017-07-01

Parallel approach is nowadays a very cheap solution to increase computational power due to possibility of usage of multithreaded computational units. This hardware became typical part of nowadays personal computers or notebooks and is widely spread. This contribution deals with experiments how evaluation of computational complex algorithm of the inference over RDF data can be parallelized over graphical cards to decrease computational time.

An Empirical Study of Combining Communicating Processes in a Parallel Discrete Event Simulation

DTIC Science & Technology

1990-12-01

dynamics of the cost/performance criteria which typically made up computer resource acquisition decisions . offering a broad range of tradeoffs in the way... prcesses has a significant impact on simulation performance. It is the hypothesis of this 3-4 SYSTEM DECOMPOSITION PHYSICAL SYSTEM 1: N PHYSICAL PROCESS 1...EMPTY)) next-event = pop(next-event-queue); lp-clock = next-event - time; Simulate next event departure- consume event-enqueue new event end while; If no

A comparative study of serial and parallel aeroelastic computations of wings

NASA Technical Reports Server (NTRS)

Byun, Chansup; Guruswamy, Guru P.

1994-01-01

A procedure for computing the aeroelasticity of wings on parallel multiple-instruction, multiple-data (MIMD) computers is presented. In this procedure, fluids are modeled using Euler equations, and structures are modeled using modal or finite element equations. The procedure is designed in such a way that each discipline can be developed and maintained independently by using a domain decomposition approach. In the present parallel procedure, each computational domain is scalable. A parallel integration scheme is used to compute aeroelastic responses by solving fluid and structural equations concurrently. The computational efficiency issues of parallel integration of both fluid and structural equations are investigated in detail. This approach, which reduces the total computational time by a factor of almost 2, is demonstrated for a typical aeroelastic wing by using various numbers of processors on the Intel iPSC/860.

Research in parallel computing

NASA Technical Reports Server (NTRS)

Ortega, James M.; Henderson, Charles

1994-01-01

This report summarizes work on parallel computations for NASA Grant NAG-1-1529 for the period 1 Jan. - 30 June 1994. Short summaries on highly parallel preconditioners, target-specific parallel reductions, and simulation of delta-cache protocols are provided.

Parallel computations and control of adaptive structures

NASA Technical Reports Server (NTRS)

Park, K. C.; Alvin, Kenneth F.; Belvin, W. Keith; Chong, K. P. (Editor); Liu, S. C. (Editor); Li, J. C. (Editor)

1991-01-01

The equations of motion for structures with adaptive elements for vibration control are presented for parallel computations to be used as a software package for real-time control of flexible space structures. A brief introduction of the state-of-the-art parallel computational capability is also presented. Time marching strategies are developed for an effective use of massive parallel mapping, partitioning, and the necessary arithmetic operations. An example is offered for the simulation of control-structure interaction on a parallel computer and the impact of the approach presented for applications in other disciplines than aerospace industry is assessed.

Design of a massively parallel computer using bit serial processing elements

NASA Technical Reports Server (NTRS)

Aburdene, Maurice F.; Khouri, Kamal S.; Piatt, Jason E.; Zheng, Jianqing

1995-01-01

A 1-bit serial processor designed for a parallel computer architecture is described. This processor is used to develop a massively parallel computational engine, with a single instruction-multiple data (SIMD) architecture. The computer is simulated and tested to verify its operation and to measure its performance for further development.

Optimizing CyberShake Seismic Hazard Workflows for Large HPC Resources

NASA Astrophysics Data System (ADS)

Callaghan, S.; Maechling, P. J.; Juve, G.; Vahi, K.; Deelman, E.; Jordan, T. H.

2014-12-01

The CyberShake computational platform is a well-integrated collection of scientific software and middleware that calculates 3D simulation-based probabilistic seismic hazard curves and hazard maps for the Los Angeles region. Currently each CyberShake model comprises about 235 million synthetic seismograms from about 415,000 rupture variations computed at 286 sites. CyberShake integrates large-scale parallel and high-throughput serial seismological research codes into a processing framework in which early stages produce files used as inputs by later stages. Scientific workflow tools are used to manage the jobs, data, and metadata. The Southern California Earthquake Center (SCEC) developed the CyberShake platform using USC High Performance Computing and Communications systems and open-science NSF resources.CyberShake calculations were migrated to the NSF Track 1 system NCSA Blue Waters when it became operational in 2013, via an interdisciplinary team approach including domain scientists, computer scientists, and middleware developers. Due to the excellent performance of Blue Waters and CyberShake software optimizations, we reduced the makespan (a measure of wallclock time-to-solution) of a CyberShake study from 1467 to 342 hours. We will describe the technical enhancements behind this improvement, including judicious introduction of new GPU software, improved scientific software components, increased workflow-based automation, and Blue Waters-specific workflow optimizations.Our CyberShake performance improvements highlight the benefits of scientific workflow tools. The CyberShake workflow software stack includes the Pegasus Workflow Management System (Pegasus-WMS, which includes Condor DAGMan), HTCondor, and Globus GRAM, with Pegasus-mpi-cluster managing the high-throughput tasks on the HPC resources. The workflow tools handle data management, automatically transferring about 13 TB back to SCEC storage.We will present performance metrics from the most recent CyberShake study, executed on Blue Waters. We will compare the performance of CPU and GPU versions of our large-scale parallel wave propagation code, AWP-ODC-SGT. Finally, we will discuss how these enhancements have enabled SCEC to move forward with plans to increase the CyberShake simulation frequency to 1.0 Hz.

Performance Evaluation of Parallel Branch and Bound Search with the Intel iPSC (Intel Personal SuperComputer) Hypercube Computer.

DTIC Science & Technology

1986-12-01

17 III. Analysis of Parallel Design ................................................ 18 Parallel Abstract Data ...Types ........................................... 18 Abstract Data Type .................................................. 19 Parallel ADT...22 Data -Structure Design ........................................... 23 Object-Oriented Design

INFN-Pisa scientific computation environment (GRID, HPC and Interactive Analysis)

NASA Astrophysics Data System (ADS)

Arezzini, S.; Carboni, A.; Caruso, G.; Ciampa, A.; Coscetti, S.; Mazzoni, E.; Piras, S.

2014-06-01

The INFN-Pisa Tier2 infrastructure is described, optimized not only for GRID CPU and Storage access, but also for a more interactive use of the resources in order to provide good solutions for the final data analysis step. The Data Center, equipped with about 6700 production cores, permits the use of modern analysis techniques realized via advanced statistical tools (like RooFit and RooStat) implemented in multicore systems. In particular a POSIX file storage access integrated with standard SRM access is provided. Therefore the unified storage infrastructure is described, based on GPFS and Xrootd, used both for SRM data repository and interactive POSIX access. Such a common infrastructure allows a transparent access to the Tier2 data to the users for their interactive analysis. The organization of a specialized many cores CPU facility devoted to interactive analysis is also described along with the login mechanism integrated with the INFN-AAI (National INFN Infrastructure) to extend the site access and use to a geographical distributed community. Such infrastructure is used also for a national computing facility in use to the INFN theoretical community, it enables a synergic use of computing and storage resources. Our Center initially developed for the HEP community is now growing and includes also HPC resources fully integrated. In recent years has been installed and managed a cluster facility (1000 cores, parallel use via InfiniBand connection) and we are now updating this facility that will provide resources for all the intermediate level HPC computing needs of the INFN theoretical national community.

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

The GeantV project: Preparing the future of simulation

DOE PAGES

Amadio, G.; J. Apostolakis; Bandieramonte, M.; ...

2015-12-23

Detector simulation is consuming at least half of the HEP computing cycles, and even so, experiments have to take hard decisions on what to simulate, as their needs greatly surpass the availability of computing resources. New experiments still in the design phase such as FCC, CLIC and ILC as well as upgraded versions of the existing LHC detectors will push further the simulation requirements. Since the increase in computing resources is not likely to keep pace with our needs, it is therefore necessary to explore innovative ways of speeding up simulation in order to sustain the progress of High Energymore » Physics. The GeantV project aims at developing a high performance detector simulation system integrating fast and full simulation that can be ported on different computing architectures, including CPU accelerators. After more than two years of R&D the project has produced a prototype capable of transporting particles in complex geometries exploiting micro-parallelism, SIMD and multithreading. Portability is obtained via C++ template techniques that allow the development of machine- independent computational kernels. Furthermore, a set of tables derived from Geant4 for cross sections and final states provides a realistic shower development and, having been ported into a Geant4 physics list, can be used as a basis for a direct performance comparison.« less

Hypercluster Parallel Processor

NASA Technical Reports Server (NTRS)

Blech, Richard A.; Cole, Gary L.; Milner, Edward J.; Quealy, Angela

1992-01-01

Hypercluster computer system includes multiple digital processors, operation of which coordinated through specialized software. Configurable according to various parallel-computing architectures of shared-memory or distributed-memory class, including scalar computer, vector computer, reduced-instruction-set computer, and complex-instruction-set computer. Designed as flexible, relatively inexpensive system that provides single programming and operating environment within which one can investigate effects of various parallel-computing architectures and combinations on performance in solution of complicated problems like those of three-dimensional flows in turbomachines. Hypercluster software and architectural concepts are in public domain.

Parallel processing architecture for computing inverse differential kinematic equations of the PUMA arm

NASA Technical Reports Server (NTRS)

Hsia, T. C.; Lu, G. Z.; Han, W. H.

1987-01-01

In advanced robot control problems, on-line computation of inverse Jacobian solution is frequently required. Parallel processing architecture is an effective way to reduce computation time. A parallel processing architecture is developed for the inverse Jacobian (inverse differential kinematic equation) of the PUMA arm. The proposed pipeline/parallel algorithm can be inplemented on an IC chip using systolic linear arrays. This implementation requires 27 processing cells and 25 time units. Computation time is thus significantly reduced.

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.

Automatic Generation of OpenMP Directives and Its Application to Computational Fluid Dynamics Codes

NASA Technical Reports Server (NTRS)

Yan, Jerry; Jin, Haoqiang; Frumkin, Michael; Yan, Jerry (Technical Monitor)

2000-01-01

The shared-memory programming model is a very effective way to achieve parallelism on shared memory parallel computers. As great progress was made in hardware and software technologies, performance of parallel programs with compiler directives has demonstrated large improvement. The introduction of OpenMP directives, the industrial standard for shared-memory programming, has minimized the issue of portability. In this study, we have extended CAPTools, a computer-aided parallelization toolkit, to automatically generate OpenMP-based parallel programs with nominal user assistance. We outline techniques used in the implementation of the tool and discuss the application of this tool on the NAS Parallel Benchmarks and several computational fluid dynamics codes. This work demonstrates the great potential of using the tool to quickly port parallel programs and also achieve good performance that exceeds some of the commercial tools.

Hypergraph partitioning implementation for parallelizing matrix-vector multiplication using CUDA GPU-based parallel computing

NASA Astrophysics Data System (ADS)

Murni, Bustamam, A.; Ernastuti, Handhika, T.; Kerami, D.

2017-07-01

Calculation of the matrix-vector multiplication in the real-world problems often involves large matrix with arbitrary size. Therefore, parallelization is needed to speed up the calculation process that usually takes a long time. Graph partitioning techniques that have been discussed in the previous studies cannot be used to complete the parallelized calculation of matrix-vector multiplication with arbitrary size. This is due to the assumption of graph partitioning techniques that can only solve the square and symmetric matrix. Hypergraph partitioning techniques will overcome the shortcomings of the graph partitioning technique. This paper addresses the efficient parallelization of matrix-vector multiplication through hypergraph partitioning techniques using CUDA GPU-based parallel computing. CUDA (compute unified device architecture) is a parallel computing platform and programming model that was created by NVIDIA and implemented by the GPU (graphics processing unit).

[Series: Medical Applications of the PHITS Code (2): Acceleration by Parallel Computing].

PubMed

Furuta, Takuya; Sato, Tatsuhiko

2015-01-01

Time-consuming Monte Carlo dose calculation becomes feasible owing to the development of computer technology. However, the recent development is due to emergence of the multi-core high performance computers. Therefore, parallel computing becomes a key to achieve good performance of software programs. A Monte Carlo simulation code PHITS contains two parallel computing functions, the distributed-memory parallelization using protocols of message passing interface (MPI) and the shared-memory parallelization using open multi-processing (OpenMP) directives. Users can choose the two functions according to their needs. This paper gives the explanation of the two functions with their advantages and disadvantages. Some test applications are also provided to show their performance using a typical multi-core high performance workstation.

Distributed GPU Computing in GIScience

NASA Astrophysics Data System (ADS)

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

2013-12-01

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

Evolving binary classifiers through parallel computation of multiple fitness cases.

PubMed

Cagnoni, Stefano; Bergenti, Federico; Mordonini, Monica; Adorni, Giovanni

2005-06-01

This paper describes two versions of a novel approach to developing binary classifiers, based on two evolutionary computation paradigms: cellular programming and genetic programming. Such an approach achieves high computation efficiency both during evolution and at runtime. Evolution speed is optimized by allowing multiple solutions to be computed in parallel. Runtime performance is optimized explicitly using parallel computation in the case of cellular programming or implicitly taking advantage of the intrinsic parallelism of bitwise operators on standard sequential architectures in the case of genetic programming. The approach was tested on a digit recognition problem and compared with a reference classifier.

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.

Dynamic Resource Management for Parallel Tasks in an Oversubscribed Energy-Constrained Heterogeneous Environment

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

Imam, Neena; Koenig, Gregory A; Machovec, Dylan

2016-01-01

Abstract: The worth of completing parallel tasks is modeled using utility functions, which monotonically-decrease with time and represent the importance and urgency of a task. These functions define the utility earned by a task at the time of its completion. The performance of such a system is measured as the total utility earned by all completed tasks over some interval of time (e.g., 24 hours). To maximize system performance when scheduling dynamically arriving parallel tasks onto a high performance computing (HPC) system that is oversubscribed and energy-constrained, we have designed, analyzed, and compared different heuristic techniques. Four utility-aware heuristics (i.e.,more » Max Utility, Max Utility-per-Time, Max Utility-per-Resource, and Max Utility-per-Energy), three FCFS-based heuristics (Conservative Backfilling, EASY Backfilling, and FCFS with Multiple Queues), and a Random heuristic were examined in this study. A technique that is often used with the FCFS-based heuristics is the concept of a permanent reservation. We compare the performance of permanent reservations with temporary place-holders to demonstrate the advantages that place-holders can provide. We also present a novel energy filtering technique that constrains the maximum energy-per-resource used by each task. We conducted a simulation study to evaluate the performance of these heuristics and techniques in an energy-constrained oversubscribed HPC environment. With place-holders, energy filtering, and dropping tasks with low potential utility, our utility-aware heuristics are able to significantly outperform the existing FCFS-based techniques.« less

Review of An Introduction to Parallel and Vector Scientific Computing

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

Bailey, David H.; Lefton, Lew

2006-06-30

On one hand, the field of high-performance scientific computing is thriving beyond measure. Performance of leading-edge systems on scientific calculations, as measured say by the Top500 list, has increased by an astounding factor of 8000 during the 15-year period from 1993 to 2008, which is slightly faster even than Moore's Law. Even more importantly, remarkable advances in numerical algorithms, numerical libraries and parallel programming environments have led to improvements in the scope of what can be computed that are entirely on a par with the advances in computing hardware. And these successes have spread far beyond the confines of largemore » government-operated laboratories, many universities, modest-sized research institutes and private firms now operate clusters that differ only in scale from the behemoth systems at the large-scale facilities. In the wake of these recent successes, researchers from fields that heretofore have not been part of the scientific computing world have been drawn into the arena. For example, at the recent SC07 conference, the exhibit hall, which long has hosted displays from leading computer systems vendors and government laboratories, featured some 70 exhibitors who had not previously participated. In spite of all these exciting developments, and in spite of the clear need to present these concepts to a much broader technical audience, there is a perplexing dearth of training material and textbooks in the field, particularly at the introductory level. Only a handful of universities offer coursework in the specific area of highly parallel scientific computing, and instructors of such courses typically rely on custom-assembled material. For example, the present reviewer and Robert F. Lucas relied on materials assembled in a somewhat ad-hoc fashion from colleagues and personal resources when presenting a course on parallel scientific computing at the University of California, Berkeley, a few years ago. Thus it is indeed refreshing to see the publication of the book An Introduction to Parallel and Vector Scientic Computing, written by Ronald W. Shonkwiler and Lew Lefton, both of the Georgia Institute of Technology. They have taken the bull by the horns and produced a book that appears to be entirely satisfactory as an introductory textbook for use in such a course. It is also of interest to the much broader community of researchers who are already in the field, laboring day by day to improve the power and performance of their numerical simulations. The book is organized into 11 chapters, plus an appendix. The first three chapters describe the basics of system architecture including vector, parallel and distributed memory systems, the details of task dependence and synchronization, and the various programming models currently in use - threads, MPI and OpenMP. Chapters four through nine provide a competent introduction to floating-point arithmetic, numerical error and numerical linear algebra. Some of the topics presented include Gaussian elimination, LU decomposition, tridiagonal systems, Givens rotations, QR decompositions, Gauss-Seidel iterations and Householder transformations. Chapters 10 and 11 introduce Monte Carlo methods and schemes for discrete optimization such as genetic algorithms.« less

A Debugger for Computational Grid Applications

NASA Technical Reports Server (NTRS)

Hood, Robert; Jost, Gabriele; Biegel, Bryan (Technical Monitor)

2001-01-01

This viewgraph presentation gives an overview of a debugger for computational grid applications. Details are given on NAS parallel tools groups (including parallelization support tools, evaluation of various parallelization strategies, and distributed and aggregated computing), debugger dependencies, scalability, initial implementation, the process grid, and information on Globus.

Application of a Scalable, Parallel, Unstructured-Grid-Based Navier-Stokes Solver

NASA Technical Reports Server (NTRS)

Parikh, Paresh

2001-01-01

A parallel version of an unstructured-grid based Navier-Stokes solver, USM3Dns, previously developed for efficient operation on a variety of parallel computers, has been enhanced to incorporate upgrades made to the serial version. The resultant parallel code has been extensively tested on a variety of problems of aerospace interest and on two sets of parallel computers to understand and document its characteristics. An innovative grid renumbering construct and use of non-blocking communication are shown to produce superlinear computing performance. Preliminary results from parallelization of a recently introduced "porous surface" boundary condition are also presented.

How to Build an AppleSeed: A Parallel Macintosh Cluster for Numerically Intensive Computing

NASA Astrophysics Data System (ADS)

Decyk, V. K.; Dauger, D. E.

We have constructed a parallel cluster consisting of a mixture of Apple Macintosh G3 and G4 computers running the Mac OS, and have achieved very good performance on numerically intensive, parallel plasma particle-incell simulations. A subset of the MPI message-passing library was implemented in Fortran77 and C. This library enabled us to port code, without modification, from other parallel processors to the Macintosh cluster. Unlike Unix-based clusters, no special expertise in operating systems is required to build and run the cluster. This enables us to move parallel computing from the realm of experts to the main stream of computing.

Parallel simulation of tsunami inundation on a large-scale supercomputer

NASA Astrophysics Data System (ADS)

Oishi, Y.; Imamura, F.; Sugawara, D.

2013-12-01

An accurate prediction of tsunami inundation is important for disaster mitigation purposes. One approach is to approximate the tsunami wave source through an instant inversion analysis using real-time observation data (e.g., Tsushima et al., 2009) and then use the resulting wave source data in an instant tsunami inundation simulation. However, a bottleneck of this approach is the large computational cost of the non-linear inundation simulation and the computational power of recent massively parallel supercomputers is helpful to enable faster than real-time execution of a tsunami inundation simulation. Parallel computers have become approximately 1000 times faster in 10 years (www.top500.org), and so it is expected that very fast parallel computers will be more and more prevalent in the near future. Therefore, it is important to investigate how to efficiently conduct a tsunami simulation on parallel computers. In this study, we are targeting very fast tsunami inundation simulations on the K computer, currently the fastest Japanese supercomputer, which has a theoretical peak performance of 11.2 PFLOPS. One computing node of the K computer consists of 1 CPU with 8 cores that share memory, and the nodes are connected through a high-performance torus-mesh network. The K computer is designed for distributed-memory parallel computation, so we have developed a parallel tsunami model. Our model is based on TUNAMI-N2 model of Tohoku University, which is based on a leap-frog finite difference method. A grid nesting scheme is employed to apply high-resolution grids only at the coastal regions. To balance the computation load of each CPU in the parallelization, CPUs are first allocated to each nested layer in proportion to the number of grid points of the nested layer. Using CPUs allocated to each layer, 1-D domain decomposition is performed on each layer. In the parallel computation, three types of communication are necessary: (1) communication to adjacent neighbours for the finite difference calculation, (2) communication between adjacent layers for the calculations to connect each layer, and (3) global communication to obtain the time step which satisfies the CFL condition in the whole domain. A preliminary test on the K computer showed the parallel efficiency on 1024 cores was 57% relative to 64 cores. We estimate that the parallel efficiency will be considerably improved by applying a 2-D domain decomposition instead of the present 1-D domain decomposition in future work. The present parallel tsunami model was applied to the 2011 Great Tohoku tsunami. The coarsest resolution layer covers a 758 km × 1155 km region with a 405 m grid spacing. A nesting of five layers was used with the resolution ratio of 1/3 between nested layers. The finest resolution region has 5 m resolution and covers most of the coastal region of Sendai city. To complete 2 hours of simulation time, the serial (non-parallel) computation took approximately 4 days on a workstation. To complete the same simulation on 1024 cores of the K computer, it took 45 minutes which is more than two times faster than real-time. This presentation discusses the updated parallel computational performance and the efficient use of the K computer when considering the characteristics of the tsunami inundation simulation model in relation to the characteristics and capabilities of the K computer.

Assessing the Need for Supercomputing Resources Within the Pacific Area of Responsibility

DTIC Science & Technology

2015-05-26

portion of today’s research and development dollars are going toward developing machines that will be better suited for addressing big data applications...2009; Radu Sion, “To Cloud or Not to? Musings on Clouds, Security and Big Data ,” in Secure Data Management, Vol. 8425, May 2014, pp. 3–5; Yao Chen...Applied Parallel and Scientific Computing, Vol. 7134, 2010. Sion, Radu, “To Cloud or Not to? Musings on Clouds, Security and Big Data ,” in Secure Data

Designing a parallel evolutionary algorithm for inferring gene networks on the cloud computing environment.

PubMed

Lee, Wei-Po; Hsiao, Yu-Ting; Hwang, Wei-Che

2014-01-16

To improve the tedious task of reconstructing gene networks through testing experimentally the possible interactions between genes, it becomes a trend to adopt the automated reverse engineering procedure instead. Some evolutionary algorithms have been suggested for deriving network parameters. However, to infer large networks by the evolutionary algorithm, it is necessary to address two important issues: premature convergence and high computational cost. To tackle the former problem and to enhance the performance of traditional evolutionary algorithms, it is advisable to use parallel model evolutionary algorithms. To overcome the latter and to speed up the computation, it is advocated to adopt the mechanism of cloud computing as a promising solution: most popular is the method of MapReduce programming model, a fault-tolerant framework to implement parallel algorithms for inferring large gene networks. This work presents a practical framework to infer large gene networks, by developing and parallelizing a hybrid GA-PSO optimization method. Our parallel method is extended to work with the Hadoop MapReduce programming model and is executed in different cloud computing environments. To evaluate the proposed approach, we use a well-known open-source software GeneNetWeaver to create several yeast S. cerevisiae sub-networks and use them to produce gene profiles. Experiments have been conducted and the results have been analyzed. They show that our parallel approach can be successfully used to infer networks with desired behaviors and the computation time can be largely reduced. Parallel population-based algorithms can effectively determine network parameters and they perform better than the widely-used sequential algorithms in gene network inference. These parallel algorithms can be distributed to the cloud computing environment to speed up the computation. By coupling the parallel model population-based optimization method and the parallel computational framework, high quality solutions can be obtained within relatively short time. This integrated approach is a promising way for inferring large networks.

Designing a parallel evolutionary algorithm for inferring gene networks on the cloud computing environment

PubMed Central

2014-01-01

Background To improve the tedious task of reconstructing gene networks through testing experimentally the possible interactions between genes, it becomes a trend to adopt the automated reverse engineering procedure instead. Some evolutionary algorithms have been suggested for deriving network parameters. However, to infer large networks by the evolutionary algorithm, it is necessary to address two important issues: premature convergence and high computational cost. To tackle the former problem and to enhance the performance of traditional evolutionary algorithms, it is advisable to use parallel model evolutionary algorithms. To overcome the latter and to speed up the computation, it is advocated to adopt the mechanism of cloud computing as a promising solution: most popular is the method of MapReduce programming model, a fault-tolerant framework to implement parallel algorithms for inferring large gene networks. Results This work presents a practical framework to infer large gene networks, by developing and parallelizing a hybrid GA-PSO optimization method. Our parallel method is extended to work with the Hadoop MapReduce programming model and is executed in different cloud computing environments. To evaluate the proposed approach, we use a well-known open-source software GeneNetWeaver to create several yeast S. cerevisiae sub-networks and use them to produce gene profiles. Experiments have been conducted and the results have been analyzed. They show that our parallel approach can be successfully used to infer networks with desired behaviors and the computation time can be largely reduced. Conclusions Parallel population-based algorithms can effectively determine network parameters and they perform better than the widely-used sequential algorithms in gene network inference. These parallel algorithms can be distributed to the cloud computing environment to speed up the computation. By coupling the parallel model population-based optimization method and the parallel computational framework, high quality solutions can be obtained within relatively short time. This integrated approach is a promising way for inferring large networks. PMID:24428926

Parallel CE/SE Computations via Domain Decomposition

NASA Technical Reports Server (NTRS)

Himansu, Ananda; Jorgenson, Philip C. E.; Wang, Xiao-Yen; Chang, Sin-Chung

2000-01-01

This paper describes the parallelization strategy and achieved parallel efficiency of an explicit time-marching algorithm for solving conservation laws. The Space-Time Conservation Element and Solution Element (CE/SE) algorithm for solving the 2D and 3D Euler equations is parallelized with the aid of domain decomposition. The parallel efficiency of the resultant algorithm on a Silicon Graphics Origin 2000 parallel computer is checked.

Swarm Verification

NASA Technical Reports Server (NTRS)

Holzmann, Gerard J.; Joshi, Rajeev; Groce, Alex

2008-01-01

Reportedly, supercomputer designer Seymour Cray once said that he would sooner use two strong oxen to plow a field than a thousand chickens. Although this is undoubtedly wise when it comes to plowing a field, it is not so clear for other types of tasks. Model checking problems are of the proverbial "search the needle in a haystack" type. Such problems can often be parallelized easily. Alas, none of the usual divide and conquer methods can be used to parallelize the working of a model checker. Given that it has become easier than ever to gain access to large numbers of computers to perform even routine tasks it is becoming more and more attractive to find alternate ways to use these resources to speed up model checking tasks. This paper describes one such method, called swarm verification.

Parallel Algorithms for Least Squares and Related Computations.

DTIC Science & Technology

1991-03-22

for dense computations in linear algebra . The work has recently been published in a general reference book on parallel algorithms by SIAM. AFO SR...written his Ph.D. dissertation with the principal investigator. (See publication 6.) • Parallel Algorithms for Dense Linear Algebra Computations. Our...and describe and to put into perspective a selection of the more important parallel algorithms for numerical linear algebra . We give a major new

Load Balancing Strategies for Multiphase Flows on Structured Grids

NASA Astrophysics Data System (ADS)

Olshefski, Kristopher; Owkes, Mark

2017-11-01

The computation time required to perform large simulations of complex systems is currently one of the leading bottlenecks of computational research. Parallelization allows multiple processing cores to perform calculations simultaneously and reduces computational times. However, load imbalances between processors waste computing resources as processors wait for others to complete imbalanced tasks. In multiphase flows, these imbalances arise due to the additional computational effort required at the gas-liquid interface. However, many current load balancing schemes are only designed for unstructured grid applications. The purpose of this research is to develop a load balancing strategy while maintaining the simplicity of a structured grid. Several approaches are investigated including brute force oversubscription, node oversubscription through Message Passing Interface (MPI) commands, and shared memory load balancing using OpenMP. Each of these strategies are tested with a simple one-dimensional model prior to implementation into the three-dimensional NGA code. Current results show load balancing will reduce computational time by at least 30%.

Reliability models for dataflow computer systems

NASA Technical Reports Server (NTRS)

Kavi, K. M.; Buckles, B. P.

1985-01-01

The demands for concurrent operation within a computer system and the representation of parallelism in programming languages have yielded a new form of program representation known as data flow (DENN 74, DENN 75, TREL 82a). A new model based on data flow principles for parallel computations and parallel computer systems is presented. Necessary conditions for liveness and deadlock freeness in data flow graphs are derived. The data flow graph is used as a model to represent asynchronous concurrent computer architectures including data flow computers.

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.

NAS Technical Summaries, March 1993 - February 1994

NASA Technical Reports Server (NTRS)

1995-01-01

NASA created the Numerical Aerodynamic Simulation (NAS) Program in 1987 to focus resources on solving critical problems in aeroscience and related disciplines by utilizing the power of the most advanced supercomputers available. The NAS Program provides scientists with the necessary computing power to solve today's most demanding computational fluid dynamics problems and serves as a pathfinder in integrating leading-edge supercomputing technologies, thus benefitting other supercomputer centers in government and industry. The 1993-94 operational year concluded with 448 high-speed processor projects and 95 parallel projects representing NASA, the Department of Defense, other government agencies, private industry, and universities. This document provides a glimpse at some of the significant scientific results for the year.

NAS technical summaries. Numerical aerodynamic simulation program, March 1992 - February 1993

NASA Technical Reports Server (NTRS)

1994-01-01

NASA created the Numerical Aerodynamic Simulation (NAS) Program in 1987 to focus resources on solving critical problems in aeroscience and related disciplines by utilizing the power of the most advanced supercomputers available. The NAS Program provides scientists with the necessary computing power to solve today's most demanding computational fluid dynamics problems and serves as a pathfinder in integrating leading-edge supercomputing technologies, thus benefitting other supercomputer centers in government and industry. The 1992-93 operational year concluded with 399 high-speed processor projects and 91 parallel projects representing NASA, the Department of Defense, other government agencies, private industry, and universities. This document provides a glimpse at some of the significant scientific results for the year.

Endpoint-based parallel data processing in a parallel active messaging interface of a parallel computer

DOEpatents

Archer, Charles J; Blocksome, Michael E; Ratterman, Joseph D; Smith, Brian E

2014-02-11

Endpoint-based parallel data processing in a parallel active messaging interface ('PAMI') of a parallel computer, the PAMI composed of data communications endpoints, each endpoint including a specification of data communications parameters for a thread of execution on a compute node, including specifications of a client, a context, and a task, the compute nodes coupled for data communications through the PAMI, including establishing a data communications geometry, the geometry specifying, for tasks representing processes of execution of the parallel application, a set of endpoints that are used in collective operations of the PAMI including a plurality of endpoints for one of the tasks; receiving in endpoints of the geometry an instruction for a collective operation; and executing the instruction for a collective opeartion through the endpoints in dependence upon the geometry, including dividing data communications operations among the plurality of endpoints for one of the tasks.

Endpoint-based parallel data processing in a parallel active messaging interface of a parallel computer

DOEpatents

Archer, Charles J.; Blocksome, Michael A.; Ratterman, Joseph D.; Smith, Brian E.

2014-08-12

Endpoint-based parallel data processing in a parallel active messaging interface (`PAMI`) of a parallel computer, the PAMI composed of data communications endpoints, each endpoint including a specification of data communications parameters for a thread of execution on a compute node, including specifications of a client, a context, and a task, the compute nodes coupled for data communications through the PAMI, including establishing a data communications geometry, the geometry specifying, for tasks representing processes of execution of the parallel application, a set of endpoints that are used in collective operations of the PAMI including a plurality of endpoints for one of the tasks; receiving in endpoints of the geometry an instruction for a collective operation; and executing the instruction for a collective operation through the endpoints in dependence upon the geometry, including dividing data communications operations among the plurality of endpoints for one of the tasks.

Why not make a PC cluster of your own? 5. AppleSeed: A Parallel Macintosh Cluster for Scientific Computing

NASA Astrophysics Data System (ADS)

Decyk, Viktor K.; Dauger, Dean E.

We have constructed a parallel cluster consisting of Apple Macintosh G4 computers running both Classic Mac OS as well as the Unix-based Mac OS X, and have achieved very good performance on numerically intensive, parallel plasma particle-in-cell simulations. Unlike other Unix-based clusters, no special expertise in operating systems is required to build and run the cluster. This enables us to move parallel computing from the realm of experts to the mainstream of computing.

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

Not Available

The Computing and Communications (C) Division is responsible for the Laboratory's Integrated Computing Network (ICN) as well as Laboratory-wide communications. Our computing network, used by 8,000 people distributed throughout the nation, constitutes one of the most powerful scientific computing facilities in the world. In addition to the stable production environment of the ICN, we have taken a leadership role in high-performance computing and have established the Advanced Computing Laboratory (ACL), the site of research on experimental, massively parallel computers; high-speed communication networks; distributed computing; and a broad variety of advanced applications. The computational resources available in the ACL are ofmore » the type needed to solve problems critical to national needs, the so-called Grand Challenge'' problems. The purpose of this publication is to inform our clients of our strategic and operating plans in these important areas. We review major accomplishments since late 1990 and describe our strategic planning goals and specific projects that will guide our operations over the next few years. Our mission statement, planning considerations, and management policies and practices are also included.« less

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

Not Available

The Computing and Communications (C) Division is responsible for the Laboratory`s Integrated Computing Network (ICN) as well as Laboratory-wide communications. Our computing network, used by 8,000 people distributed throughout the nation, constitutes one of the most powerful scientific computing facilities in the world. In addition to the stable production environment of the ICN, we have taken a leadership role in high-performance computing and have established the Advanced Computing Laboratory (ACL), the site of research on experimental, massively parallel computers; high-speed communication networks; distributed computing; and a broad variety of advanced applications. The computational resources available in the ACL are ofmore » the type needed to solve problems critical to national needs, the so-called ``Grand Challenge`` problems. The purpose of this publication is to inform our clients of our strategic and operating plans in these important areas. We review major accomplishments since late 1990 and describe our strategic planning goals and specific projects that will guide our operations over the next few years. Our mission statement, planning considerations, and management policies and practices are also included.« less

A Gateway for Phylogenetic Analysis Powered by Grid Computing Featuring GARLI 2.0

PubMed Central

Bazinet, Adam L.; Zwickl, Derrick J.; Cummings, Michael P.

2014-01-01

We introduce molecularevolution.org, a publicly available gateway for high-throughput, maximum-likelihood phylogenetic analysis powered by grid computing. The gateway features a garli 2.0 web service that enables a user to quickly and easily submit thousands of maximum likelihood tree searches or bootstrap searches that are executed in parallel on distributed computing resources. The garli web service allows one to easily specify partitioned substitution models using a graphical interface, and it performs sophisticated post-processing of phylogenetic results. Although the garli web service has been used by the research community for over three years, here we formally announce the availability of the service, describe its capabilities, highlight new features and recent improvements, and provide details about how the grid system efficiently delivers high-quality phylogenetic results. [garli, gateway, grid computing, maximum likelihood, molecular evolution portal, phylogenetics, web service.] PMID:24789072

Faster than Real-Time Dynamic Simulation for Large-Size Power System with Detailed Dynamic Models using High-Performance Computing Platform

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

Huang, Renke; Jin, Shuangshuang; Chen, Yousu

This paper presents a faster-than-real-time dynamic simulation software package that is designed for large-size power system dynamic simulation. It was developed on the GridPACKTM high-performance computing (HPC) framework. The key features of the developed software package include (1) faster-than-real-time dynamic simulation for a WECC system (17,000 buses) with different types of detailed generator, controller, and relay dynamic models, (2) a decoupled parallel dynamic simulation algorithm with optimized computation architecture to better leverage HPC resources and technologies, (3) options for HPC-based linear and iterative solvers, (4) hidden HPC details, such as data communication and distribution, to enable development centered on mathematicalmore » models and algorithms rather than on computational details for power system researchers, and (5) easy integration of new dynamic models and related algorithms into the software package.« less

Template Interfaces for Agile Parallel Data-Intensive Science

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

Ramakrishnan, Lavanya; Gunter, Daniel; Pastorello, Gilerto Z.

Tigres provides a programming library to compose and execute large-scale data-intensive scientific workflows from desktops to supercomputers. DOE User Facilities and large science collaborations are increasingly generating large enough data sets that it is no longer practical to download them to a desktop to operate on them. They are instead stored at centralized compute and storage resources such as high performance computing (HPC) centers. Analysis of this data requires an ability to run on these facilities, but with current technologies, scaling an analysis to an HPC center and to a large data set is difficult even for experts. Tigres ismore » addressing the challenge of enabling collaborative analysis of DOE Science data through a new concept of reusable "templates" that enable scientists to easily compose, run and manage collaborative computational tasks. These templates define common computation patterns used in analyzing a data set.« less

THC-MP: High performance numerical simulation of reactive transport and multiphase flow in porous media

NASA Astrophysics Data System (ADS)

Wei, Xiaohui; Li, Weishan; Tian, Hailong; Li, Hongliang; Xu, Haixiao; Xu, Tianfu

2015-07-01

The numerical simulation of multiphase flow and reactive transport in the porous media on complex subsurface problem is a computationally intensive application. To meet the increasingly computational requirements, this paper presents a parallel computing method and architecture. Derived from TOUGHREACT that is a well-established code for simulating subsurface multi-phase flow and reactive transport problems, we developed a high performance computing THC-MP based on massive parallel computer, which extends greatly on the computational capability for the original code. The domain decomposition method was applied to the coupled numerical computing procedure in the THC-MP. We designed the distributed data structure, implemented the data initialization and exchange between the computing nodes and the core solving module using the hybrid parallel iterative and direct solver. Numerical accuracy of the THC-MP was verified through a CO2 injection-induced reactive transport problem by comparing the results obtained from the parallel computing and sequential computing (original code). Execution efficiency and code scalability were examined through field scale carbon sequestration applications on the multicore cluster. The results demonstrate successfully the enhanced performance using the THC-MP on parallel computing facilities.

A parallel Jacobson-Oksman optimization algorithm. [parallel processing (computers)

NASA Technical Reports Server (NTRS)

Straeter, T. A.; Markos, A. T.

1975-01-01

A gradient-dependent optimization technique which exploits the vector-streaming or parallel-computing capabilities of some modern computers is presented. The algorithm, derived by assuming that the function to be minimized is homogeneous, is a modification of the Jacobson-Oksman serial minimization method. In addition to describing the algorithm, conditions insuring the convergence of the iterates of the algorithm and the results of numerical experiments on a group of sample test functions are presented. The results of these experiments indicate that this algorithm will solve optimization problems in less computing time than conventional serial methods on machines having vector-streaming or parallel-computing capabilities.

Methods of parallel computation applied on granular simulations

NASA Astrophysics Data System (ADS)

Martins, Gustavo H. B.; Atman, Allbens P. F.

2017-06-01

Every year, parallel computing has becoming cheaper and more accessible. As consequence, applications were spreading over all research areas. Granular materials is a promising area for parallel computing. To prove this statement we study the impact of parallel computing in simulations of the BNE (Brazil Nut Effect). This property is due the remarkable arising of an intruder confined to a granular media when vertically shaken against gravity. By means of DEM (Discrete Element Methods) simulations, we study the code performance testing different methods to improve clock time. A comparison between serial and parallel algorithms, using OpenMP® is also shown. The best improvement was obtained by optimizing the function that find contacts using Verlet's cells.

Parallel computation using boundary elements in solid mechanics

NASA Technical Reports Server (NTRS)

Chien, L. S.; Sun, C. T.

1990-01-01

The inherent parallelism of the boundary element method is shown. The boundary element is formulated by assuming the linear variation of displacements and tractions within a line element. Moreover, MACSYMA symbolic program is employed to obtain the analytical results for influence coefficients. Three computational components are parallelized in this method to show the speedup and efficiency in computation. The global coefficient matrix is first formed concurrently. Then, the parallel Gaussian elimination solution scheme is applied to solve the resulting system of equations. Finally, and more importantly, the domain solutions of a given boundary value problem are calculated simultaneously. The linear speedups and high efficiencies are shown for solving a demonstrated problem on Sequent Symmetry S81 parallel computing system.

Parallel Algorithms for the Exascale Era

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

Robey, Robert W.

New parallel algorithms are needed to reach the Exascale level of parallelism with millions of cores. We look at some of the research developed by students in projects at LANL. The research blends ideas from the early days of computing while weaving in the fresh approach brought by students new to the field of high performance computing. We look at reproducibility of global sums and why it is important to parallel computing. Next we look at how the concept of hashing has led to the development of more scalable algorithms suitable for next-generation parallel computers. Nearly all of this workmore » has been done by undergraduates and published in leading scientific journals.« less

Reactive transport modeling in the subsurface environment with OGS-IPhreeqc

NASA Astrophysics Data System (ADS)

He, Wenkui; Beyer, Christof; Fleckenstein, Jan; Jang, Eunseon; Kalbacher, Thomas; Naumov, Dimitri; Shao, Haibing; Wang, Wenqing; Kolditz, Olaf

2015-04-01

Worldwide, sustainable water resource management becomes an increasingly challenging task due to the growth of population and extensive applications of fertilizer in agriculture. Moreover, climate change causes further stresses to both water quantity and quality. Reactive transport modeling in the coupled soil-aquifer system is a viable approach to assess the impacts of different land use and groundwater exploitation scenarios on the water resources. However, the application of this approach is usually limited in spatial scale and to simplified geochemical systems due to the huge computational expense involved. Such computational expense is not only caused by solving the high non-linearity of the initial boundary value problems of water flow in the unsaturated zone numerically with rather fine spatial and temporal discretization for the correct mass balance and numerical stability, but also by the intensive computational task of quantifying geochemical reactions. In the present study, a flexible and efficient tool for large scale reactive transport modeling in variably saturated porous media and its applications are presented. The open source scientific software OpenGeoSys (OGS) is coupled with the IPhreeqc module of the geochemical solver PHREEQC. The new coupling approach makes full use of advantages from both codes: OGS provides a flexible choice of different numerical approaches for simulation of water flow in the vadose zone such as the pressure-based or mixed forms of Richards equation; whereas the IPhreeqc module leads to a simplification of data storage and its communication with OGS, which greatly facilitates the coupling and code updating. Moreover, a parallelization scheme with MPI (Message Passing Interface) is applied, in which the computational task of water flow and mass transport is partitioned through domain decomposition, whereas the efficient parallelization of geochemical reactions is achieved by smart allocation of computational workload over multiple compute nodes. The plausibility of the new coupling is verified by several benchmark tests. In addition, the efficiency of the new coupling approach is demonstrated by its application in a large scale scenario, in which the environmental fate of pesticides in a complex soil-aquifer system is studied.

Reactive transport modeling in variably saturated porous media with OGS-IPhreeqc

NASA Astrophysics Data System (ADS)

He, W.; Beyer, C.; Fleckenstein, J. H.; Jang, E.; Kalbacher, T.; Shao, H.; Wang, W.; Kolditz, O.

2014-12-01

Worldwide, sustainable water resource management becomes an increasingly challenging task due to the growth of population and extensive applications of fertilizer in agriculture. Moreover, climate change causes further stresses to both water quantity and quality. Reactive transport modeling in the coupled soil-aquifer system is a viable approach to assess the impacts of different land use and groundwater exploitation scenarios on the water resources. However, the application of this approach is usually limited in spatial scale and to simplified geochemical systems due to the huge computational expense involved. Such computational expense is not only caused by solving the high non-linearity of the initial boundary value problems of water flow in the unsaturated zone numerically with rather fine spatial and temporal discretization for the correct mass balance and numerical stability, but also by the intensive computational task of quantifying geochemical reactions. In the present study, a flexible and efficient tool for large scale reactive transport modeling in variably saturated porous media and its applications are presented. The open source scientific software OpenGeoSys (OGS) is coupled with the IPhreeqc module of the geochemical solver PHREEQC. The new coupling approach makes full use of advantages from both codes: OGS provides a flexible choice of different numerical approaches for simulation of water flow in the vadose zone such as the pressure-based or mixed forms of Richards equation; whereas the IPhreeqc module leads to a simplification of data storage and its communication with OGS, which greatly facilitates the coupling and code updating. Moreover, a parallelization scheme with MPI (Message Passing Interface) is applied, in which the computational task of water flow and mass transport is partitioned through domain decomposition, whereas the efficient parallelization of geochemical reactions is achieved by smart allocation of computational workload over multiple compute nodes. The plausibility of the new coupling is verified by several benchmark tests. In addition, the efficiency of the new coupling approach is demonstrated by its application in a large scale scenario, in which the environmental fate of pesticides in a complex soil-aquifer system is studied.

Research in computer science

NASA Technical Reports Server (NTRS)

Ortega, J. M.

1986-01-01

Various graduate research activities in the field of computer science are reported. Among the topics discussed are: (1) failure probabilities in multi-version software; (2) Gaussian Elimination on parallel computers; (3) three dimensional Poisson solvers on parallel/vector computers; (4) automated task decomposition for multiple robot arms; (5) multi-color incomplete cholesky conjugate gradient methods on the Cyber 205; and (6) parallel implementation of iterative methods for solving linear equations.

A high-speed linear algebra library with automatic parallelism

NASA Technical Reports Server (NTRS)

Boucher, Michael L.

1994-01-01

Parallel or distributed processing is key to getting highest performance workstations. However, designing and implementing efficient parallel algorithms is difficult and error-prone. It is even more difficult to write code that is both portable to and efficient on many different computers. Finally, it is harder still to satisfy the above requirements and include the reliability and ease of use required of commercial software intended for use in a production environment. As a result, the application of parallel processing technology to commercial software has been extremely small even though there are numerous computationally demanding programs that would significantly benefit from application of parallel processing. This paper describes DSSLIB, which is a library of subroutines that perform many of the time-consuming computations in engineering and scientific software. DSSLIB combines the high efficiency and speed of parallel computation with a serial programming model that eliminates many undesirable side-effects of typical parallel code. The result is a simple way to incorporate the power of parallel processing into commercial software without compromising maintainability, reliability, or ease of use. This gives significant advantages over less powerful non-parallel entries in the market.

RRAM-based parallel computing architecture using k-nearest neighbor classification for pattern recognition

NASA Astrophysics Data System (ADS)

Jiang, Yuning; Kang, Jinfeng; Wang, Xinan

2017-03-01

Resistive switching memory (RRAM) is considered as one of the most promising devices for parallel computing solutions that may overcome the von Neumann bottleneck of today’s electronic systems. However, the existing RRAM-based parallel computing architectures suffer from practical problems such as device variations and extra computing circuits. In this work, we propose a novel parallel computing architecture for pattern recognition by implementing k-nearest neighbor classification on metal-oxide RRAM crossbar arrays. Metal-oxide RRAM with gradual RESET behaviors is chosen as both the storage and computing components. The proposed architecture is tested by the MNIST database. High speed (~100 ns per example) and high recognition accuracy (97.05%) are obtained. The influence of several non-ideal device properties is also discussed, and it turns out that the proposed architecture shows great tolerance to device variations. This work paves a new way to achieve RRAM-based parallel computing hardware systems with high performance.

Symplectic molecular dynamics simulations on specially designed parallel computers.

PubMed

Borstnik, Urban; Janezic, Dusanka

2005-01-01

We have developed a computer program for molecular dynamics (MD) simulation that implements the Split Integration Symplectic Method (SISM) and is designed to run on specialized parallel computers. The MD integration is performed by the SISM, which analytically treats high-frequency vibrational motion and thus enables the use of longer simulation time steps. The low-frequency motion is treated numerically on specially designed parallel computers, which decreases the computational time of each simulation time step. The combination of these approaches means that less time is required and fewer steps are needed and so enables fast MD simulations. We study the computational performance of MD simulation of molecular systems on specialized computers and provide a comparison to standard personal computers. The combination of the SISM with two specialized parallel computers is an effective way to increase the speed of MD simulations up to 16-fold over a single PC processor.

Towards Anatomic Scale Agent-Based Modeling with a Massively Parallel Spatially Explicit General-Purpose Model of Enteric Tissue (SEGMEnT_HPC)

PubMed Central

Cockrell, Robert Chase; Christley, Scott; Chang, Eugene; An, Gary

2015-01-01

Perhaps the greatest challenge currently facing the biomedical research community is the ability to integrate highly detailed cellular and molecular mechanisms to represent clinical disease states as a pathway to engineer effective therapeutics. This is particularly evident in the representation of organ-level pathophysiology in terms of abnormal tissue structure, which, through histology, remains a mainstay in disease diagnosis and staging. As such, being able to generate anatomic scale simulations is a highly desirable goal. While computational limitations have previously constrained the size and scope of multi-scale computational models, advances in the capacity and availability of high-performance computing (HPC) resources have greatly expanded the ability of computational models of biological systems to achieve anatomic, clinically relevant scale. Diseases of the intestinal tract are exemplary examples of pathophysiological processes that manifest at multiple scales of spatial resolution, with structural abnormalities present at the microscopic, macroscopic and organ-levels. In this paper, we describe a novel, massively parallel computational model of the gut, the Spatially Explicitly General-purpose Model of Enteric Tissue_HPC (SEGMEnT_HPC), which extends an existing model of the gut epithelium, SEGMEnT, in order to create cell-for-cell anatomic scale simulations. We present an example implementation of SEGMEnT_HPC that simulates the pathogenesis of ileal pouchitis, and important clinical entity that affects patients following remedial surgery for ulcerative colitis. PMID:25806784

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

NASA Astrophysics Data System (ADS)

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

2012-12-01

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

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.

Parallelization of fine-scale computation in Agile Multiscale Modelling Methodology

NASA Astrophysics Data System (ADS)

Macioł, Piotr; Michalik, Kazimierz

2016-10-01

Nowadays, multiscale modelling of material behavior is an extensively developed area. An important obstacle against its wide application is high computational demands. Among others, the parallelization of multiscale computations is a promising solution. Heterogeneous multiscale models are good candidates for parallelization, since communication between sub-models is limited. In this paper, the possibility of parallelization of multiscale models based on Agile Multiscale Methodology framework is discussed. A sequential, FEM based macroscopic model has been combined with concurrently computed fine-scale models, employing a MatCalc thermodynamic simulator. The main issues, being investigated in this work are: (i) the speed-up of multiscale models with special focus on fine-scale computations and (ii) on decreasing the quality of computations enforced by parallel execution. Speed-up has been evaluated on the basis of Amdahl's law equations. The problem of `delay error', rising from the parallel execution of fine scale sub-models, controlled by the sequential macroscopic sub-model is discussed. Some technical aspects of combining third-party commercial modelling software with an in-house multiscale framework and a MPI library are also discussed.

Parallel algorithms for mapping pipelined and parallel computations

NASA Technical Reports Server (NTRS)

Nicol, David M.

1988-01-01

Many computational problems in image processing, signal processing, and scientific computing are naturally structured for either pipelined or parallel computation. When mapping such problems onto a parallel architecture it is often necessary to aggregate an obvious problem decomposition. Even in this context the general mapping problem is known to be computationally intractable, but recent advances have been made in identifying classes of problems and architectures for which optimal solutions can be found in polynomial time. Among these, the mapping of pipelined or parallel computations onto linear array, shared memory, and host-satellite systems figures prominently. This paper extends that work first by showing how to improve existing serial mapping algorithms. These improvements have significantly lower time and space complexities: in one case a published O(nm sup 3) time algorithm for mapping m modules onto n processors is reduced to an O(nm log m) time complexity, and its space requirements reduced from O(nm sup 2) to O(m). Run time complexity is further reduced with parallel mapping algorithms based on these improvements, which run on the architecture for which they create the mappings.

Synthesizing parallel imaging applications using the CAP (computer-aided parallelization) tool

NASA Astrophysics Data System (ADS)

Gennart, Benoit A.; Mazzariol, Marc; Messerli, Vincent; Hersch, Roger D.

1997-12-01

Imaging applications such as filtering, image transforms and compression/decompression require vast amounts of computing power when applied to large data sets. These applications would potentially benefit from the use of parallel processing. However, dedicated parallel computers are expensive and their processing power per node lags behind that of the most recent commodity components. Furthermore, developing parallel applications remains a difficult task: writing and debugging the application is difficult (deadlocks), programs may not be portable from one parallel architecture to the other, and performance often comes short of expectations. In order to facilitate the development of parallel applications, we propose the CAP computer-aided parallelization tool which enables application programmers to specify at a high-level of abstraction the flow of data between pipelined-parallel operations. In addition, the CAP tool supports the programmer in developing parallel imaging and storage operations. CAP enables combining efficiently parallel storage access routines and image processing sequential operations. This paper shows how processing and I/O intensive imaging applications must be implemented to take advantage of parallelism and pipelining between data access and processing. This paper's contribution is (1) to show how such implementations can be compactly specified in CAP, and (2) to demonstrate that CAP specified applications achieve the performance of custom parallel code. The paper analyzes theoretically the performance of CAP specified applications and demonstrates the accuracy of the theoretical analysis through experimental measurements.

A multipurpose computing center with distributed resources

NASA Astrophysics Data System (ADS)

Chudoba, J.; Adam, M.; Adamová, D.; Kouba, T.; Mikula, A.; Říkal, V.; Švec, J.; Uhlířová, J.; Vokáč, P.; Svatoš, M.

2017-10-01

The Computing Center of the Institute of Physics (CC IoP) of the Czech Academy of Sciences serves a broad spectrum of users with various computing needs. It runs WLCG Tier-2 center for the ALICE and the ATLAS experiments; the same group of services is used by astroparticle physics projects the Pierre Auger Observatory (PAO) and the Cherenkov Telescope Array (CTA). OSG stack is installed for the NOvA experiment. Other groups of users use directly local batch system. Storage capacity is distributed to several locations. DPM servers used by the ATLAS and the PAO are all in the same server room, but several xrootd servers for the ALICE experiment are operated in the Nuclear Physics Institute in Řež, about 10 km away. The storage capacity for the ATLAS and the PAO is extended by resources of the CESNET - the Czech National Grid Initiative representative. Those resources are in Plzen and Jihlava, more than 100 km away from the CC IoP. Both distant sites use a hierarchical storage solution based on disks and tapes. They installed one common dCache instance, which is published in the CC IoP BDII. ATLAS users can use these resources using the standard ATLAS tools in the same way as the local storage without noticing this geographical distribution. Computing clusters LUNA and EXMAG dedicated to users mostly from the Solid State Physics departments offer resources for parallel computing. They are part of the Czech NGI infrastructure MetaCentrum with distributed batch system based on torque with a custom scheduler. Clusters are installed remotely by the MetaCentrum team and a local contact helps only when needed. Users from IoP have exclusive access only to a part of these two clusters and take advantage of higher priorities on the rest (1500 cores in total), which can also be used by any user of the MetaCentrum. IoP researchers can also use distant resources located in several towns of the Czech Republic with a capacity of more than 12000 cores in total.

CSM parallel structural methods research

NASA Technical Reports Server (NTRS)

Storaasli, Olaf O.

1989-01-01

Parallel structural methods, research team activities, advanced architecture computers for parallel computational structural mechanics (CSM) research, the FLEX/32 multicomputer, a parallel structural analyses testbed, blade-stiffened aluminum panel with a circular cutout and the dynamic characteristics of a 60 meter, 54-bay, 3-longeron deployable truss beam are among the topics discussed.

The curse of planning: dissecting multiple reinforcement-learning systems by taxing the central executive.

PubMed

Otto, A Ross; Gershman, Samuel J; Markman, Arthur B; Daw, Nathaniel D

2013-05-01

A number of accounts of human and animal behavior posit the operation of parallel and competing valuation systems in the control of choice behavior. In these accounts, a flexible but computationally expensive model-based reinforcement-learning system has been contrasted with a less flexible but more efficient model-free reinforcement-learning system. The factors governing which system controls behavior-and under what circumstances-are still unclear. Following the hypothesis that model-based reinforcement learning requires cognitive resources, we demonstrated that having human decision makers perform a demanding secondary task engenders increased reliance on a model-free reinforcement-learning strategy. Further, we showed that, across trials, people negotiate the trade-off between the two systems dynamically as a function of concurrent executive-function demands, and people's choice latencies reflect the computational expenses of the strategy they employ. These results demonstrate that competition between multiple learning systems can be controlled on a trial-by-trial basis by modulating the availability of cognitive resources.

The Curse of Planning: Dissecting multiple reinforcement learning systems by taxing the central executive

PubMed Central

Otto, A. Ross; Gershman, Samuel J.; Markman, Arthur B.; Daw, Nathaniel D.

2013-01-01

A number of accounts of human and animal behavior posit the operation of parallel and competing valuation systems in the control of choice behavior. Along these lines, a flexible but computationally expensive model-based reinforcement learning system has been contrasted with a less flexible but more efficient model-free reinforcement learning system. The factors governing which system controls behavior—and under what circumstances—are still unclear. Based on the hypothesis that model-based reinforcement learning requires cognitive resources, we demonstrate that having human decision-makers perform a demanding secondary task engenders increased reliance on a model-free reinforcement learning strategy. Further, we show that across trials, people negotiate this tradeoff dynamically as a function of concurrent executive function demands and their choice latencies reflect the computational expenses of the strategy employed. These results demonstrate that competition between multiple learning systems can be controlled on a trial-by-trial basis by modulating the availability of cognitive resources. PMID:23558545

An Innovative Infrastructure with a Universal Geo-Spatiotemporal Data Representation Supporting Cost-Effective Integration of Diverse Earth Science Data

NASA Technical Reports Server (NTRS)

Rilee, Michael Lee; Kuo, Kwo-Sen

2017-01-01

The SpatioTemporal Adaptive Resolution Encoding (STARE) is a unifying scheme encoding geospatial and temporal information for organizing data on scalable computing/storage resources, minimizing expensive data transfers. STARE provides a compact representation that turns set-logic functions into integer operations, e.g. conditional sub-setting, taking into account representative spatiotemporal resolutions of the data in the datasets. STARE geo-spatiotemporally aligns data placements of diverse data on massive parallel resources to maximize performance. Automating important scientific functions (e.g. regridding) and computational functions (e.g. data placement) allows scientists to focus on domain-specific questions instead of expending their efforts and expertise on data processing. With STARE-enabled automation, SciDB (Scientific Database) plus STARE provides a database interface, reducing costly data preparation, increasing the volume and variety of interoperable data, and easing result sharing. Using SciDB plus STARE as part of an integrated analysis infrastructure dramatically eases combining diametrically different datasets.

Parallelized direct execution simulation of message-passing parallel programs

NASA Technical Reports Server (NTRS)

Dickens, Phillip M.; Heidelberger, Philip; Nicol, David M.

1994-01-01

As massively parallel computers proliferate, there is growing interest in findings ways by which performance of massively parallel codes can be efficiently predicted. This problem arises in diverse contexts such as parallelizing computers, parallel performance monitoring, and parallel algorithm development. In this paper we describe one solution where one directly executes the application code, but uses a discrete-event simulator to model details of the presumed parallel machine such as operating system and communication network behavior. Because this approach is computationally expensive, we are interested in its own parallelization specifically the parallelization of the discrete-event simulator. We describe methods suitable for parallelized direct execution simulation of message-passing parallel programs, and report on the performance of such a system, Large Application Parallel Simulation Environment (LAPSE), we have built on the Intel Paragon. On all codes measured to date, LAPSE predicts performance well typically within 10 percent relative error. Depending on the nature of the application code, we have observed low slowdowns (relative to natively executing code) and high relative speedups using up to 64 processors.

Automatic Generation of Directive-Based Parallel Programs for Shared Memory Parallel Systems

NASA Technical Reports Server (NTRS)

Jin, Hao-Qiang; Yan, Jerry; Frumkin, Michael

2000-01-01

The shared-memory programming model is a very effective way to achieve parallelism on shared memory parallel computers. As great progress was made in hardware and software technologies, performance of parallel programs with compiler directives has demonstrated large improvement. The introduction of OpenMP directives, the industrial standard for shared-memory programming, has minimized the issue of portability. Due to its ease of programming and its good performance, the technique has become very popular. In this study, we have extended CAPTools, a computer-aided parallelization toolkit, to automatically generate directive-based, OpenMP, parallel programs. We outline techniques used in the implementation of the tool and present test results on the NAS parallel benchmarks and ARC3D, a CFD application. This work demonstrates the great potential of using computer-aided tools to quickly port parallel programs and also achieve good performance.

Distributed multitasking ITS with PVM

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

Fan, W.C.; Halbleib, J.A. Sr.

1995-12-31

Advances in computer hardware and communication software have made it possible to perform parallel-processing computing on a collection of desktop workstations. For many applications, multitasking on a cluster of high-performance workstations has achieved performance comparable to or better than that on a traditional supercomputer. From the point of view of cost-effectiveness, it also allows users to exploit available but unused computational resources and thus achieve a higher performance-to-cost ratio. Monte Carlo calculations are inherently parallelizable because the individual particle trajectories can be generated independently with minimum need for interprocessor communication. Furthermore, the number of particle histories that can be generatedmore » in a given amount of wall-clock time is nearly proportional to the number of processors in the cluster. This is an important fact because the inherent statistical uncertainty in any Monte Carlo result decreases as the number of histories increases. For these reasons, researchers have expended considerable effort to take advantage of different parallel architectures for a variety of Monte Carlo radiation transport codes, often with excellent results. The initial interest in this work was sparked by the multitasking capability of the MCNP code on a cluster of workstations using the Parallel Virtual Machine (PVM) software. On a 16-machine IBM RS/6000 cluster, it has been demonstrated that MCNP runs ten times as fast as on a single-processor CRAY YMP. In this paper, we summarize the implementation of a similar multitasking capability for the coupled electronphoton transport code system, the Integrated TIGER Series (ITS), and the evaluation of two load-balancing schemes for homogeneous and heterogeneous networks.« less

Distributed multitasking ITS with PVM

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

Fan, W.C.; Halbleib, J.A. Sr.

1995-02-01

Advances of computer hardware and communication software have made it possible to perform parallel-processing computing on a collection of desktop workstations. For many applications, multitasking on a cluster of high-performance workstations has achieved performance comparable or better than that on a traditional supercomputer. From the point of view of cost-effectiveness, it also allows users to exploit available but unused computational resources, and thus achieve a higher performance-to-cost ratio. Monte Carlo calculations are inherently parallelizable because the individual particle trajectories can be generated independently with minimum need for interprocessor communication. Furthermore, the number of particle histories that can be generated inmore » a given amount of wall-clock time is nearly proportional to the number of processors in the cluster. This is an important fact because the inherent statistical uncertainty in any Monte Carlo result decreases as the number of histories increases. For these reasons, researchers have expended considerable effort to take advantage of different parallel architectures for a variety of Monte Carlo radiation transport codes, often with excellent results. The initial interest in this work was sparked by the multitasking capability of MCNP on a cluster of workstations using the Parallel Virtual Machine (PVM) software. On a 16-machine IBM RS/6000 cluster, it has been demonstrated that MCNP runs ten times as fast as on a single-processor CRAY YMP. In this paper, we summarize the implementation of a similar multitasking capability for the coupled electron/photon transport code system, the Integrated TIGER Series (ITS), and the evaluation of two load balancing schemes for homogeneous and heterogeneous networks.« less

The science of computing - Parallel computation

NASA Technical Reports Server (NTRS)

Denning, P. J.

1985-01-01

Although parallel computation architectures have been known for computers since the 1920s, it was only in the 1970s that microelectronic components technologies advanced to the point where it became feasible to incorporate multiple processors in one machine. Concommitantly, the development of algorithms for parallel processing also lagged due to hardware limitations. The speed of computing with solid-state chips is limited by gate switching delays. The physical limit implies that a 1 Gflop operational speed is the maximum for sequential processors. A computer recently introduced features a 'hypercube' architecture with 128 processors connected in networks at 5, 6 or 7 points per grid, depending on the design choice. Its computing speed rivals that of supercomputers, but at a fraction of the cost. The added speed with less hardware is due to parallel processing, which utilizes algorithms representing different parts of an equation that can be broken into simpler statements and processed simultaneously. Present, highly developed computer languages like FORTRAN, PASCAL, COBOL, etc., rely on sequential instructions. Thus, increased emphasis will now be directed at parallel processing algorithms to exploit the new architectures.

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).

System-wide power management control via clock distribution network

DOEpatents

Coteus, Paul W.; Gara, Alan; Gooding, Thomas M.; Haring, Rudolf A.; Kopcsay, Gerard V.; Liebsch, Thomas A.; Reed, Don D.

2015-05-19

An apparatus, method and computer program product for automatically controlling power dissipation of a parallel computing system that includes a plurality of processors. A computing device issues a command to the parallel computing system. A clock pulse-width modulator encodes the command in a system clock signal to be distributed to the plurality of processors. The plurality of processors in the parallel computing system receive the system clock signal including the encoded command, and adjusts power dissipation according to the encoded command.

Parallel Computing:. Some Activities in High Energy Physics

NASA Astrophysics Data System (ADS)

Willers, Ian

This paper examines some activities in High Energy Physics that utilise parallel computing. The topic includes all computing from the proposed SIMD front end detectors, the farming applications, high-powered RISC processors and the large machines in the computer centers. We start by looking at the motivation behind using parallelism for general purpose computing. The developments around farming are then described from its simplest form to the more complex system in Fermilab. Finally, there is a list of some developments that are happening close to the experiments.

Large Spatial Scale Ground Displacement Mapping through the P-SBAS Processing of Sentinel-1 Data on a Cloud Computing Environment

NASA Astrophysics Data System (ADS)

Casu, F.; Bonano, M.; de Luca, C.; Lanari, R.; Manunta, M.; Manzo, M.; Zinno, I.

2017-12-01

Since its launch in 2014, the Sentinel-1 (S1) constellation has played a key role on SAR data availability and dissemination all over the World. Indeed, the free and open access data policy adopted by the European Copernicus program together with the global coverage acquisition strategy, make the Sentinel constellation as a game changer in the Earth Observation scenario. Being the SAR data become ubiquitous, the technological and scientific challenge is focused on maximizing the exploitation of such huge data flow. In this direction, the use of innovative processing algorithms and distributed computing infrastructures, such as the Cloud Computing platforms, can play a crucial role. In this work we present a Cloud Computing solution for the advanced interferometric (DInSAR) processing chain based on the Parallel SBAS (P-SBAS) approach, aimed at processing S1 Interferometric Wide Swath (IWS) data for the generation of large spatial scale deformation time series in efficient, automatic and systematic way. Such a DInSAR chain ingests Sentinel 1 SLC images and carries out several processing steps, to finally compute deformation time series and mean deformation velocity maps. Different parallel strategies have been designed ad hoc for each processing step of the P-SBAS S1 chain, encompassing both multi-core and multi-node programming techniques, in order to maximize the computational efficiency achieved within a Cloud Computing environment and cut down the relevant processing times. The presented P-SBAS S1 processing chain has been implemented on the Amazon Web Services platform and a thorough analysis of the attained parallel performances has been performed to identify and overcome the major bottlenecks to the scalability. The presented approach is used to perform national-scale DInSAR analyses over Italy, involving the processing of more than 3000 S1 IWS images acquired from both ascending and descending orbits. Such an experiment confirms the big advantage of exploiting large computational and storage resources of Cloud Computing platforms for large scale DInSAR analysis. The presented Cloud Computing P-SBAS processing chain can be a precious tool in the perspective of developing operational services disposable for the EO scientific community related to hazard monitoring and risk prevention and mitigation.

Special purpose parallel computer architecture for real-time control and simulation in robotic applications

NASA Technical Reports Server (NTRS)

Fijany, Amir (Inventor); Bejczy, Antal K. (Inventor)

1993-01-01

This is a real-time robotic controller and simulator which is a MIMD-SIMD parallel architecture for interfacing with an external host computer and providing a high degree of parallelism in computations for robotic control and simulation. It includes a host processor for receiving instructions from the external host computer and for transmitting answers to the external host computer. There are a plurality of SIMD microprocessors, each SIMD processor being a SIMD parallel processor capable of exploiting fine grain parallelism and further being able to operate asynchronously to form a MIMD architecture. Each SIMD processor comprises a SIMD architecture capable of performing two matrix-vector operations in parallel while fully exploiting parallelism in each operation. There is a system bus connecting the host processor to the plurality of SIMD microprocessors and a common clock providing a continuous sequence of clock pulses. There is also a ring structure interconnecting the plurality of SIMD microprocessors and connected to the clock for providing the clock pulses to the SIMD microprocessors and for providing a path for the flow of data and instructions between the SIMD microprocessors. The host processor includes logic for controlling the RRCS by interpreting instructions sent by the external host computer, decomposing the instructions into a series of computations to be performed by the SIMD microprocessors, using the system bus to distribute associated data among the SIMD microprocessors, and initiating activity of the SIMD microprocessors to perform the computations on the data by procedure call.

Use of parallel computing in mass processing of laser data

NASA Astrophysics Data System (ADS)

Będkowski, J.; Bratuś, R.; Prochaska, M.; Rzonca, A.

2015-12-01

The first part of the paper includes a description of the rules used to generate the algorithm needed for the purpose of parallel computing and also discusses the origins of the idea of research on the use of graphics processors in large scale processing of laser scanning data. The next part of the paper includes the results of an efficiency assessment performed for an array of different processing options, all of which were substantially accelerated with parallel computing. The processing options were divided into the generation of orthophotos using point clouds, coloring of point clouds, transformations, and the generation of a regular grid, as well as advanced processes such as the detection of planes and edges, point cloud classification, and the analysis of data for the purpose of quality control. Most algorithms had to be formulated from scratch in the context of the requirements of parallel computing. A few of the algorithms were based on existing technology developed by the Dephos Software Company and then adapted to parallel computing in the course of this research study. Processing time was determined for each process employed for a typical quantity of data processed, which helped confirm the high efficiency of the solutions proposed and the applicability of parallel computing to the processing of laser scanning data. The high efficiency of parallel computing yields new opportunities in the creation and organization of processing methods for laser scanning data.

Novel Scalable 3-D MT Inverse Solver

NASA Astrophysics Data System (ADS)

Kuvshinov, A. V.; Kruglyakov, M.; Geraskin, A.

2016-12-01

We present a new, robust and fast, three-dimensional (3-D) magnetotelluric (MT) inverse solver. As a forward modelling engine a highly-scalable solver extrEMe [1] is used. The (regularized) inversion is based on an iterative gradient-type optimization (quasi-Newton method) and exploits adjoint sources approach for fast calculation of the gradient of the misfit. The inverse solver is able to deal with highly detailed and contrasting models, allows for working (separately or jointly) with any type of MT (single-site and/or inter-site) responses, and supports massive parallelization. Different parallelization strategies implemented in the code allow for optimal usage of available computational resources for a given problem set up. To parameterize an inverse domain a mask approach is implemented, which means that one can merge any subset of forward modelling cells in order to account for (usually) irregular distribution of observation sites. We report results of 3-D numerical experiments aimed at analysing the robustness, performance and scalability of the code. In particular, our computational experiments carried out at different platforms ranging from modern laptops to high-performance clusters demonstrate practically linear scalability of the code up to thousands of nodes. 1. Kruglyakov, M., A. Geraskin, A. Kuvshinov, 2016. Novel accurate and scalable 3-D MT forward solver based on a contracting integral equation method, Computers and Geosciences, in press.

Simulation of particle motion in a closed conduit validated against experimental data

NASA Astrophysics Data System (ADS)

Dolanský, Jindřich

2015-05-01

Motion of a number of spherical particles in a closed conduit is examined by means of both simulation and experiment. The bed of the conduit is covered by stationary spherical particles of the size of the moving particles. The flow is driven by experimentally measured velocity profiles which are inputs of the simulation. Altering input velocity profiles generates various trajectory patterns. The lattice Boltzmann method (LBM) based simulation is developed to study mutual interactions of the flow and the particles. The simulation enables to model both the particle motion and the fluid flow. The entropic LBM is employed to deal with the flow characterized by the high Reynolds number. The entropic modification of the LBM along with the enhanced refinement of the lattice grid yield an increase in demands on computational resources. Due to the inherently parallel nature of the LBM it can be handled by employing the Parallel Computing Toolbox (MATLAB) and other transformations enabling usage of the CUDA GPU computing technology. The trajectories of the particles determined within the LBM simulation are validated against data gained from the experiments. The compatibility of the simulation results with the outputs of experimental measurements is evaluated. The accuracy of the applied approach is assessed and stability and efficiency of the simulation is also considered.

Towards scalable Byzantine fault-tolerant replication

NASA Astrophysics Data System (ADS)

Zbierski, Maciej

2017-08-01

Byzantine fault-tolerant (BFT) replication is a powerful technique, enabling distributed systems to remain available and correct even in the presence of arbitrary faults. Unfortunately, existing BFT replication protocols are mostly load-unscalable, i.e. they fail to respond with adequate performance increase whenever new computational resources are introduced into the system. This article proposes a universal architecture facilitating the creation of load-scalable distributed services based on BFT replication. The suggested approach exploits parallel request processing to fully utilize the available resources, and uses a load balancer module to dynamically adapt to the properties of the observed client workload. The article additionally provides a discussion on selected deployment scenarios, and explains how the proposed architecture could be used to increase the dependability of contemporary large-scale distributed systems.

Parallelized computation for computer simulation of electrocardiograms using personal computers with multi-core CPU and general-purpose GPU.

PubMed

Shen, Wenfeng; Wei, Daming; Xu, Weimin; Zhu, Xin; Yuan, Shizhong

2010-10-01

Biological computations like electrocardiological modelling and simulation usually require high-performance computing environments. This paper introduces an implementation of parallel computation for computer simulation of electrocardiograms (ECGs) in a personal computer environment with an Intel CPU of Core (TM) 2 Quad Q6600 and a GPU of Geforce 8800GT, with software support by OpenMP and CUDA. It was tested in three parallelization device setups: (a) a four-core CPU without a general-purpose GPU, (b) a general-purpose GPU plus 1 core of CPU, and (c) a four-core CPU plus a general-purpose GPU. To effectively take advantage of a multi-core CPU and a general-purpose GPU, an algorithm based on load-prediction dynamic scheduling was developed and applied to setting (c). In the simulation with 1600 time steps, the speedup of the parallel computation as compared to the serial computation was 3.9 in setting (a), 16.8 in setting (b), and 20.0 in setting (c). This study demonstrates that a current PC with a multi-core CPU and a general-purpose GPU provides a good environment for parallel computations in biological modelling and simulation studies. Copyright 2010 Elsevier Ireland Ltd. All rights reserved.

Parallelized multi–graphics processing unit framework for high-speed Gabor-domain optical coherence microscopy

PubMed Central

Tankam, Patrice; Santhanam, Anand P.; Lee, Kye-Sung; Won, Jungeun; Canavesi, Cristina; Rolland, Jannick P.

2014-01-01

Abstract. Gabor-domain optical coherence microscopy (GD-OCM) is a volumetric high-resolution technique capable of acquiring three-dimensional (3-D) skin images with histological resolution. Real-time image processing is needed to enable GD-OCM imaging in a clinical setting. We present a parallelized and scalable multi-graphics processing unit (GPU) computing framework for real-time GD-OCM image processing. A parallelized control mechanism was developed to individually assign computation tasks to each of the GPUs. For each GPU, the optimal number of amplitude-scans (A-scans) to be processed in parallel was selected to maximize GPU memory usage and core throughput. We investigated five computing architectures for computational speed-up in processing 1000×1000 A-scans. The proposed parallelized multi-GPU computing framework enables processing at a computational speed faster than the GD-OCM image acquisition, thereby facilitating high-speed GD-OCM imaging in a clinical setting. Using two parallelized GPUs, the image processing of a 1×1×0.6  mm3 skin sample was performed in about 13 s, and the performance was benchmarked at 6.5 s with four GPUs. This work thus demonstrates that 3-D GD-OCM data may be displayed in real-time to the examiner using parallelized GPU processing. PMID:24695868

Parallelized multi-graphics processing unit framework for high-speed Gabor-domain optical coherence microscopy.

PubMed

Tankam, Patrice; Santhanam, Anand P; Lee, Kye-Sung; Won, Jungeun; Canavesi, Cristina; Rolland, Jannick P

2014-07-01

Gabor-domain optical coherence microscopy (GD-OCM) is a volumetric high-resolution technique capable of acquiring three-dimensional (3-D) skin images with histological resolution. Real-time image processing is needed to enable GD-OCM imaging in a clinical setting. We present a parallelized and scalable multi-graphics processing unit (GPU) computing framework for real-time GD-OCM image processing. A parallelized control mechanism was developed to individually assign computation tasks to each of the GPUs. For each GPU, the optimal number of amplitude-scans (A-scans) to be processed in parallel was selected to maximize GPU memory usage and core throughput. We investigated five computing architectures for computational speed-up in processing 1000×1000 A-scans. The proposed parallelized multi-GPU computing framework enables processing at a computational speed faster than the GD-OCM image acquisition, thereby facilitating high-speed GD-OCM imaging in a clinical setting. Using two parallelized GPUs, the image processing of a 1×1×0.6  mm3 skin sample was performed in about 13 s, and the performance was benchmarked at 6.5 s with four GPUs. This work thus demonstrates that 3-D GD-OCM data may be displayed in real-time to the examiner using parallelized GPU processing.

clubber: removing the bioinformatics bottleneck in big data analyses.

PubMed

Miller, Maximilian; Zhu, Chengsheng; Bromberg, Yana

2017-06-13

With the advent of modern day high-throughput technologies, the bottleneck in biological discovery has shifted from the cost of doing experiments to that of analyzing results. clubber is our automated cluster-load balancing system developed for optimizing these "big data" analyses. Its plug-and-play framework encourages re-use of existing solutions for bioinformatics problems. clubber's goals are to reduce computation times and to facilitate use of cluster computing. The first goal is achieved by automating the balance of parallel submissions across available high performance computing (HPC) resources. Notably, the latter can be added on demand, including cloud-based resources, and/or featuring heterogeneous environments. The second goal of making HPCs user-friendly is facilitated by an interactive web interface and a RESTful API, allowing for job monitoring and result retrieval. We used clubber to speed up our pipeline for annotating molecular functionality of metagenomes. Here, we analyzed the Deepwater Horizon oil-spill study data to quantitatively show that the beach sands have not yet entirely recovered. Further, our analysis of the CAMI-challenge data revealed that microbiome taxonomic shifts do not necessarily correlate with functional shifts. These examples (21 metagenomes processed in 172 min) clearly illustrate the importance of clubber in the everyday computational biology environment.

Design and Analysis of Self-Adapted Task Scheduling Strategies in Wireless Sensor Networks

PubMed Central

Guo, Wenzhong; Xiong, Naixue; Chao, Han-Chieh; Hussain, Sajid; Chen, Guolong

2011-01-01

In a wireless sensor network (WSN), the usage of resources is usually highly related to the execution of tasks which consume a certain amount of computing and communication bandwidth. Parallel processing among sensors is a promising solution to provide the demanded computation capacity in WSNs. Task allocation and scheduling is a typical problem in the area of high performance computing. Although task allocation and scheduling in wired processor networks has been well studied in the past, their counterparts for WSNs remain largely unexplored. Existing traditional high performance computing solutions cannot be directly implemented in WSNs due to the limitations of WSNs such as limited resource availability and the shared communication medium. In this paper, a self-adapted task scheduling strategy for WSNs is presented. First, a multi-agent-based architecture for WSNs is proposed and a mathematical model of dynamic alliance is constructed for the task allocation problem. Then an effective discrete particle swarm optimization (PSO) algorithm for the dynamic alliance (DPSO-DA) with a well-designed particle position code and fitness function is proposed. A mutation operator which can effectively improve the algorithm’s ability of global search and population diversity is also introduced in this algorithm. Finally, the simulation results show that the proposed solution can achieve significant better performance than other algorithms. PMID:22163971

clubber: removing the bioinformatics bottleneck in big data analyses

PubMed Central

Miller, Maximilian; Zhu, Chengsheng; Bromberg, Yana

2018-01-01

With the advent of modern day high-throughput technologies, the bottleneck in biological discovery has shifted from the cost of doing experiments to that of analyzing results. clubber is our automated cluster-load balancing system developed for optimizing these “big data” analyses. Its plug-and-play framework encourages re-use of existing solutions for bioinformatics problems. clubber’s goals are to reduce computation times and to facilitate use of cluster computing. The first goal is achieved by automating the balance of parallel submissions across available high performance computing (HPC) resources. Notably, the latter can be added on demand, including cloud-based resources, and/or featuring heterogeneous environments. The second goal of making HPCs user-friendly is facilitated by an interactive web interface and a RESTful API, allowing for job monitoring and result retrieval. We used clubber to speed up our pipeline for annotating molecular functionality of metagenomes. Here, we analyzed the Deepwater Horizon oil-spill study data to quantitatively show that the beach sands have not yet entirely recovered. Further, our analysis of the CAMI-challenge data revealed that microbiome taxonomic shifts do not necessarily correlate with functional shifts. These examples (21 metagenomes processed in 172 min) clearly illustrate the importance of clubber in the everyday computational biology environment. PMID:28609295

What Physicists Should Know About High Performance Computing - Circa 2002

NASA Astrophysics Data System (ADS)

Frederick, Donald

2002-08-01

High Performance Computing (HPC) is a dynamic, cross-disciplinary field that traditionally has involved applied mathematicians, computer scientists, and others primarily from the various disciplines that have been major users of HPC resources - physics, chemistry, engineering, with increasing use by those in the life sciences. There is a technological dynamic that is powered by economic as well as by technical innovations and developments. This talk will discuss practical ideas to be considered when developing numerical applications for research purposes. Even with the rapid pace of development in the field, the author believes that these concepts will not become obsolete for a while, and will be of use to scientists who either are considering, or who have already started down the HPC path. These principles will be applied in particular to current parallel HPC systems, but there will also be references of value to desktop users. The talk will cover such topics as: computing hardware basics, single-cpu optimization, compilers, timing, numerical libraries, debugging and profiling tools and the emergence of Computational Grids.

Six Years of Parallel Computing at NAS (1987 - 1993): What Have we Learned?

NASA Technical Reports Server (NTRS)

Simon, Horst D.; Cooper, D. M. (Technical Monitor)

1994-01-01

In the fall of 1987 the age of parallelism at NAS began with the installation of a 32K processor CM-2 from Thinking Machines. In 1987 this was described as an "experiment" in parallel processing. In the six years since, NAS acquired a series of parallel machines, and conducted an active research and development effort focused on the use of highly parallel machines for applications in the computational aerosciences. In this time period parallel processing for scientific applications evolved from a fringe research topic into the one of main activities at NAS. In this presentation I will review the history of parallel computing at NAS in the context of the major progress, which has been made in the field in general. I will attempt to summarize the lessons we have learned so far, and the contributions NAS has made to the state of the art. Based on these insights I will comment on the current state of parallel computing (including the HPCC effort) and try to predict some trends for the next six years.

Endpoint-based parallel data processing with non-blocking collective instructions in a parallel active messaging interface of a parallel computer

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

Archer, Charles J; Blocksome, Michael A; Cernohous, Bob R

Methods, apparatuses, and computer program products for endpoint-based parallel data processing with non-blocking collective instructions in a parallel active messaging interface (`PAMI`) of a parallel computer are provided. Embodiments include establishing by a parallel application a data communications geometry, the geometry specifying a set of endpoints that are used in collective operations of the PAMI, including associating with the geometry a list of collective algorithms valid for use with the endpoints of the geometry. Embodiments also include registering in each endpoint in the geometry a dispatch callback function for a collective operation and executing without blocking, through a single onemore » of the endpoints in the geometry, an instruction for the collective operation.« less

Oak Ridge Institutional Cluster Autotune Test Drive Report

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

Jibonananda, Sanyal; New, Joshua Ryan

2014-02-01

The Oak Ridge Institutional Cluster (OIC) provides general purpose computational resources for the ORNL staff to run computation heavy jobs that are larger than desktop applications but do not quite require the scale and power of the Oak Ridge Leadership Computing Facility (OLCF). This report details the efforts made and conclusions derived in performing a short test drive of the cluster resources on Phase 5 of the OIC. EnergyPlus was used in the analysis as a candidate user program and the overall software environment was evaluated against anticipated challenges experienced with resources such as the shared memory-Nautilus (JICS) and Titanmore » (OLCF). The OIC performed within reason and was found to be acceptable in the context of running EnergyPlus simulations. The number of cores per node and the availability of scratch space per node allow non-traditional desktop focused applications to leverage parallel ensemble execution. Although only individual runs of EnergyPlus were executed, the software environment on the OIC appeared suitable to run ensemble simulations with some modifications to the Autotune workflow. From a standpoint of general usability, the system supports common Linux libraries, compilers, standard job scheduling software (Torque/Moab), and the OpenMPI library (the only MPI library) for MPI communications. The file system is a Panasas file system which literature indicates to be an efficient file system.« less

A parallel variable metric optimization algorithm

NASA Technical Reports Server (NTRS)

Straeter, T. A.

1973-01-01

An algorithm, designed to exploit the parallel computing or vector streaming (pipeline) capabilities of computers is presented. When p is the degree of parallelism, then one cycle of the parallel variable metric algorithm is defined as follows: first, the function and its gradient are computed in parallel at p different values of the independent variable; then the metric is modified by p rank-one corrections; and finally, a single univariant minimization is carried out in the Newton-like direction. Several properties of this algorithm are established. The convergence of the iterates to the solution is proved for a quadratic functional on a real separable Hilbert space. For a finite-dimensional space the convergence is in one cycle when p equals the dimension of the space. Results of numerical experiments indicate that the new algorithm will exploit parallel or pipeline computing capabilities to effect faster convergence than serial techniques.

Endpoint-based parallel data processing with non-blocking collective instructions in a parallel active messaging interface of a parallel computer

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

Archer, Charles J; Blocksome, Michael A; Cernohous, Bob R

Endpoint-based parallel data processing with non-blocking collective instructions in a PAMI of a parallel computer is disclosed. The PAMI is composed of data communications endpoints, each including a specification of data communications parameters for a thread of execution on a compute node, including specifications of a client, a context, and a task. The compute nodes are coupled for data communications through the PAMI. The parallel application establishes a data communications geometry specifying a set of endpoints that are used in collective operations of the PAMI by associating with the geometry a list of collective algorithms valid for use with themore » endpoints of the geometry; registering in each endpoint in the geometry a dispatch callback function for a collective operation; and executing without blocking, through a single one of the endpoints in the geometry, an instruction for the collective operation.« less

Template based parallel checkpointing in a massively parallel computer system

DOEpatents

Archer, Charles Jens [Rochester, MN; Inglett, Todd Alan [Rochester, MN

2009-01-13

A method and apparatus for a template based parallel checkpoint save for a massively parallel super computer system using a parallel variation of the rsync protocol, and network broadcast. In preferred embodiments, the checkpoint data for each node is compared to a template checkpoint file that resides in the storage and that was previously produced. Embodiments herein greatly decrease the amount of data that must be transmitted and stored for faster checkpointing and increased efficiency of the computer system. Embodiments are directed to a parallel computer system with nodes arranged in a cluster with a high speed interconnect that can perform broadcast communication. The checkpoint contains a set of actual small data blocks with their corresponding checksums from all nodes in the system. The data blocks may be compressed using conventional non-lossy data compression algorithms to further reduce the overall checkpoint size.

Computational strategies for three-dimensional flow simulations on distributed computer systems. Ph.D. Thesis Semiannual Status Report, 15 Aug. 1993 - 15 Feb. 1994

NASA Technical Reports Server (NTRS)

Weed, Richard Allen; Sankar, L. N.

1994-01-01

An increasing amount of research activity in computational fluid dynamics has been devoted to the development of efficient algorithms for parallel computing systems. The increasing performance to price ratio of engineering workstations has led to research to development procedures for implementing a parallel computing system composed of distributed workstations. This thesis proposal outlines an ongoing research program to develop efficient strategies for performing three-dimensional flow analysis on distributed computing systems. The PVM parallel programming interface was used to modify an existing three-dimensional flow solver, the TEAM code developed by Lockheed for the Air Force, to function as a parallel flow solver on clusters of workstations. Steady flow solutions were generated for three different wing and body geometries to validate the code and evaluate code performance. The proposed research will extend the parallel code development to determine the most efficient strategies for unsteady flow simulations.

Concurrent extensions to the FORTRAN language for parallel programming of computational fluid dynamics algorithms

NASA Technical Reports Server (NTRS)

Weeks, Cindy Lou

1986-01-01

Experiments were conducted at NASA Ames Research Center to define multi-tasking software requirements for multiple-instruction, multiple-data stream (MIMD) computer architectures. The focus was on specifying solutions for algorithms in the field of computational fluid dynamics (CFD). The program objectives were to allow researchers to produce usable parallel application software as soon as possible after acquiring MIMD computer equipment, to provide researchers with an easy-to-learn and easy-to-use parallel software language which could be implemented on several different MIMD machines, and to enable researchers to list preferred design specifications for future MIMD computer architectures. Analysis of CFD algorithms indicated that extensions of an existing programming language, adaptable to new computer architectures, provided the best solution to meeting program objectives. The CoFORTRAN Language was written in response to these objectives and to provide researchers a means to experiment with parallel software solutions to CFD algorithms on machines with parallel architectures.

Parallelization of interpolation, solar radiation and water flow simulation modules in GRASS GIS using OpenMP

NASA Astrophysics Data System (ADS)

Hofierka, Jaroslav; Lacko, Michal; Zubal, Stanislav

2017-10-01

In this paper, we describe the parallelization of three complex and computationally intensive modules of GRASS GIS using the OpenMP application programming interface for multi-core computers. These include the v.surf.rst module for spatial interpolation, the r.sun module for solar radiation modeling and the r.sim.water module for water flow simulation. We briefly describe the functionality of the modules and parallelization approaches used in the modules. Our approach includes the analysis of the module's functionality, identification of source code segments suitable for parallelization and proper application of OpenMP parallelization code to create efficient threads processing the subtasks. We document the efficiency of the solutions using the airborne laser scanning data representing land surface in the test area and derived high-resolution digital terrain model grids. We discuss the performance speed-up and parallelization efficiency depending on the number of processor threads. The study showed a substantial increase in computation speeds on a standard multi-core computer while maintaining the accuracy of results in comparison to the output from original modules. The presented parallelization approach showed the simplicity and efficiency of the parallelization of open-source GRASS GIS modules using OpenMP, leading to an increased performance of this geospatial software on standard multi-core computers.

Fast hydrological model calibration based on the heterogeneous parallel computing accelerated shuffled complex evolution method

NASA Astrophysics Data System (ADS)

Kan, Guangyuan; He, Xiaoyan; Ding, Liuqian; Li, Jiren; Hong, Yang; Zuo, Depeng; Ren, Minglei; Lei, Tianjie; Liang, Ke

2018-01-01

Hydrological model calibration has been a hot issue for decades. The shuffled complex evolution method developed at the University of Arizona (SCE-UA) has been proved to be an effective and robust optimization approach. However, its computational efficiency deteriorates significantly when the amount of hydrometeorological data increases. In recent years, the rise of heterogeneous parallel computing has brought hope for the acceleration of hydrological model calibration. This study proposed a parallel SCE-UA method and applied it to the calibration of a watershed rainfall-runoff model, the Xinanjiang model. The parallel method was implemented on heterogeneous computing systems using OpenMP and CUDA. Performance testing and sensitivity analysis were carried out to verify its correctness and efficiency. Comparison results indicated that heterogeneous parallel computing-accelerated SCE-UA converged much more quickly than the original serial version and possessed satisfactory accuracy and stability for the task of fast hydrological model calibration.

Implementation of Parallel Dynamic Simulation on Shared-Memory vs. Distributed-Memory Environments

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

Jin, Shuangshuang; Chen, Yousu; Wu, Di

2015-12-09

Power system dynamic simulation computes the system response to a sequence of large disturbance, such as sudden changes in generation or load, or a network short circuit followed by protective branch switching operation. It consists of a large set of differential and algebraic equations, which is computational intensive and challenging to solve using single-processor based dynamic simulation solution. High-performance computing (HPC) based parallel computing is a very promising technology to speed up the computation and facilitate the simulation process. This paper presents two different parallel implementations of power grid dynamic simulation using Open Multi-processing (OpenMP) on shared-memory platform, and Messagemore » Passing Interface (MPI) on distributed-memory clusters, respectively. The difference of the parallel simulation algorithms and architectures of the two HPC technologies are illustrated, and their performances for running parallel dynamic simulation are compared and demonstrated.« less

Developing eThread pipeline using SAGA-pilot abstraction for large-scale structural bioinformatics.

PubMed

Ragothaman, Anjani; Boddu, Sairam Chowdary; Kim, Nayong; Feinstein, Wei; Brylinski, Michal; Jha, Shantenu; Kim, Joohyun

2014-01-01

While most of computational annotation approaches are sequence-based, threading methods are becoming increasingly attractive because of predicted structural information that could uncover the underlying function. However, threading tools are generally compute-intensive and the number of protein sequences from even small genomes such as prokaryotes is large typically containing many thousands, prohibiting their application as a genome-wide structural systems biology tool. To leverage its utility, we have developed a pipeline for eThread--a meta-threading protein structure modeling tool, that can use computational resources efficiently and effectively. We employ a pilot-based approach that supports seamless data and task-level parallelism and manages large variation in workload and computational requirements. Our scalable pipeline is deployed on Amazon EC2 and can efficiently select resources based upon task requirements. We present runtime analysis to characterize computational complexity of eThread and EC2 infrastructure. Based on results, we suggest a pathway to an optimized solution with respect to metrics such as time-to-solution or cost-to-solution. Our eThread pipeline can scale to support a large number of sequences and is expected to be a viable solution for genome-scale structural bioinformatics and structure-based annotation, particularly, amenable for small genomes such as prokaryotes. The developed pipeline is easily extensible to other types of distributed cyberinfrastructure.

Developing eThread Pipeline Using SAGA-Pilot Abstraction for Large-Scale Structural Bioinformatics

PubMed Central

Ragothaman, Anjani; Feinstein, Wei; Jha, Shantenu; Kim, Joohyun

2014-01-01

While most of computational annotation approaches are sequence-based, threading methods are becoming increasingly attractive because of predicted structural information that could uncover the underlying function. However, threading tools are generally compute-intensive and the number of protein sequences from even small genomes such as prokaryotes is large typically containing many thousands, prohibiting their application as a genome-wide structural systems biology tool. To leverage its utility, we have developed a pipeline for eThread—a meta-threading protein structure modeling tool, that can use computational resources efficiently and effectively. We employ a pilot-based approach that supports seamless data and task-level parallelism and manages large variation in workload and computational requirements. Our scalable pipeline is deployed on Amazon EC2 and can efficiently select resources based upon task requirements. We present runtime analysis to characterize computational complexity of eThread and EC2 infrastructure. Based on results, we suggest a pathway to an optimized solution with respect to metrics such as time-to-solution or cost-to-solution. Our eThread pipeline can scale to support a large number of sequences and is expected to be a viable solution for genome-scale structural bioinformatics and structure-based annotation, particularly, amenable for small genomes such as prokaryotes. The developed pipeline is easily extensible to other types of distributed cyberinfrastructure. PMID:24995285

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.

Concrete resource analysis of the quantum linear-system algorithm used to compute the electromagnetic scattering cross section of a 2D target

NASA Astrophysics Data System (ADS)

Scherer, Artur; Valiron, Benoît; Mau, Siun-Chuon; Alexander, Scott; van den Berg, Eric; Chapuran, Thomas E.

2017-03-01

We provide a detailed estimate for the logical resource requirements of the quantum linear-system algorithm (Harrow et al. in Phys Rev Lett 103:150502, 2009) including the recently described elaborations and application to computing the electromagnetic scattering cross section of a metallic target (Clader et al. in Phys Rev Lett 110:250504, 2013). Our resource estimates are based on the standard quantum-circuit model of quantum computation; they comprise circuit width (related to parallelism), circuit depth (total number of steps), the number of qubits and ancilla qubits employed, and the overall number of elementary quantum gate operations as well as more specific gate counts for each elementary fault-tolerant gate from the standard set { X, Y, Z, H, S, T, { CNOT } }. In order to perform these estimates, we used an approach that combines manual analysis with automated estimates generated via the Quipper quantum programming language and compiler. Our estimates pertain to the explicit example problem size N=332{,}020{,}680 beyond which, according to a crude big-O complexity comparison, the quantum linear-system algorithm is expected to run faster than the best known classical linear-system solving algorithm. For this problem size, a desired calculation accuracy ɛ =0.01 requires an approximate circuit width 340 and circuit depth of order 10^{25} if oracle costs are excluded, and a circuit width and circuit depth of order 10^8 and 10^{29}, respectively, if the resource requirements of oracles are included, indicating that the commonly ignored oracle resources are considerable. In addition to providing detailed logical resource estimates, it is also the purpose of this paper to demonstrate explicitly (using a fine-grained approach rather than relying on coarse big-O asymptotic approximations) how these impressively large numbers arise with an actual circuit implementation of a quantum algorithm. While our estimates may prove to be conservative as more efficient advanced quantum-computation techniques are developed, they nevertheless provide a valid baseline for research targeting a reduction of the algorithmic-level resource requirements, implying that a reduction by many orders of magnitude is necessary for the algorithm to become practical.

Cooperative storage of shared files in a parallel computing system with dynamic block size

DOEpatents

Bent, John M.; Faibish, Sorin; Grider, Gary

2015-11-10

Improved techniques are provided for parallel writing of data to a shared object in a parallel computing system. A method is provided for storing data generated by a plurality of parallel processes to a shared object in a parallel computing system. The method is performed by at least one of the processes and comprises: dynamically determining a block size for storing the data; exchanging a determined amount of the data with at least one additional process to achieve a block of the data having the dynamically determined block size; and writing the block of the data having the dynamically determined block size to a file system. The determined block size comprises, e.g., a total amount of the data to be stored divided by the number of parallel processes. The file system comprises, for example, a log structured virtual parallel file system, such as a Parallel Log-Structured File System (PLFS).

Implementation of Helioseismic Data Reduction and Diagnostic Techniques on Massively Parallel Architectures

NASA Technical Reports Server (NTRS)

Korzennik, Sylvain

1997-01-01

Under the direction of Dr. Rhodes, and the technical supervision of Dr. Korzennik, the data assimilation of high spatial resolution solar dopplergrams has been carried out throughout the program on the Intel Delta Touchstone supercomputer. With the help of a research assistant, partially supported by this grant, and under the supervision of Dr. Korzennik, code development was carried out at SAO, using various available resources. To ensure cross-platform portability, PVM was selected as the message passing library. A parallel implementation of power spectra computation for helioseismology data reduction, using PVM was successfully completed. It was successfully ported to SMP architectures (i.e. SUN), and to some MPP architectures (i.e. the CM5). Due to limitation of the implementation of PVM on the Cray T3D, the port to that architecture was not completed at the time.

Algorithmic synthesis using Python compiler

NASA Astrophysics Data System (ADS)

Cieszewski, Radoslaw; Romaniuk, Ryszard; Pozniak, Krzysztof; Linczuk, Maciej

2015-09-01

This paper presents a python to VHDL compiler. The compiler interprets an algorithmic description of a desired behavior written in Python and translate it to VHDL. FPGA combines many benefits of both software and ASIC implementations. Like software, the programmed circuit is flexible, and can be reconfigured over the lifetime of the system. FPGAs have the potential to achieve far greater performance than software as a result of bypassing the fetch-decode-execute operations of traditional processors, and possibly exploiting a greater level of parallelism. This can be achieved by using many computational resources at the same time. Creating parallel programs implemented in FPGAs in pure HDL is difficult and time consuming. Using higher level of abstraction and High-Level Synthesis compiler implementation time can be reduced. The compiler has been implemented using the Python language. This article describes design, implementation and results of created tools.

Efficient Parallel Kernel Solvers for Computational Fluid Dynamics Applications

NASA Technical Reports Server (NTRS)

Sun, Xian-He

1997-01-01

Distributed-memory parallel computers dominate today's parallel computing arena. These machines, such as Intel Paragon, IBM SP2, and Cray Origin2OO, have successfully delivered high performance computing power for solving some of the so-called "grand-challenge" problems. Despite initial success, parallel machines have not been widely accepted in production engineering environments due to the complexity of parallel programming. On a parallel computing system, a task has to be partitioned and distributed appropriately among processors to reduce communication cost and to attain load balance. More importantly, even with careful partitioning and mapping, the performance of an algorithm may still be unsatisfactory, since conventional sequential algorithms may be serial in nature and may not be implemented efficiently on parallel machines. In many cases, new algorithms have to be introduced to increase parallel performance. In order to achieve optimal performance, in addition to partitioning and mapping, a careful performance study should be conducted for a given application to find a good algorithm-machine combination. This process, however, is usually painful and elusive. The goal of this project is to design and develop efficient parallel algorithms for highly accurate Computational Fluid Dynamics (CFD) simulations and other engineering applications. The work plan is 1) developing highly accurate parallel numerical algorithms, 2) conduct preliminary testing to verify the effectiveness and potential of these algorithms, 3) incorporate newly developed algorithms into actual simulation packages. The work plan has well achieved. Two highly accurate, efficient Poisson solvers have been developed and tested based on two different approaches: (1) Adopting a mathematical geometry which has a better capacity to describe the fluid, (2) Using compact scheme to gain high order accuracy in numerical discretization. The previously developed Parallel Diagonal Dominant (PDD) algorithm and Reduced Parallel Diagonal Dominant (RPDD) algorithm have been carefully studied on different parallel platforms for different applications, and a NASA simulation code developed by Man M. Rai and his colleagues has been parallelized and implemented based on data dependency analysis. These achievements are addressed in detail in the paper.

Reducing power consumption while synchronizing a plurality of compute nodes during execution of a parallel application

DOEpatents

Archer, Charles J [Rochester, MN; Blocksome, Michael A [Rochester, MN; Peters, Amanda A [Rochester, MN; Ratterman, Joseph D [Rochester, MN; Smith, Brian E [Rochester, MN

2012-01-10

Methods, apparatus, and products are disclosed for reducing power consumption while synchronizing a plurality of compute nodes during execution of a parallel application that include: beginning, by each compute node, performance of a blocking operation specified by the parallel application, each compute node beginning the blocking operation asynchronously with respect to the other compute nodes; reducing, for each compute node, power to one or more hardware components of that compute node in response to that compute node beginning the performance of the blocking operation; and restoring, for each compute node, the power to the hardware components having power reduced in response to all of the compute nodes beginning the performance of the blocking operation.

Reducing power consumption while synchronizing a plurality of compute nodes during execution of a parallel application

DOEpatents

Archer, Charles J [Rochester, MN; Blocksome, Michael A [Rochester, MN; Peters, Amanda E [Cambridge, MA; Ratterman, Joseph D [Rochester, MN; Smith, Brian E [Rochester, MN

2012-04-17

Methods, apparatus, and products are disclosed for reducing power consumption while synchronizing a plurality of compute nodes during execution of a parallel application that include: beginning, by each compute node, performance of a blocking operation specified by the parallel application, each compute node beginning the blocking operation asynchronously with respect to the other compute nodes; reducing, for each compute node, power to one or more hardware components of that compute node in response to that compute node beginning the performance of the blocking operation; and restoring, for each compute node, the power to the hardware components having power reduced in response to all of the compute nodes beginning the performance of the blocking operation.

MPI implementation of PHOENICS: A general purpose computational fluid dynamics code

NASA Astrophysics Data System (ADS)

Simunovic, S.; Zacharia, T.; Baltas, N.; Spalding, D. B.

1995-03-01

PHOENICS is a suite of computational analysis programs that are used for simulation of fluid flow, heat transfer, and dynamical reaction processes. The parallel version of the solver EARTH for the Computational Fluid Dynamics (CFD) program PHOENICS has been implemented using Message Passing Interface (MPI) standard. Implementation of MPI version of PHOENICS makes this computational tool portable to a wide range of parallel machines and enables the use of high performance computing for large scale computational simulations. MPI libraries are available on several parallel architectures making the program usable across different architectures as well as on heterogeneous computer networks. The Intel Paragon NX and MPI versions of the program have been developed and tested on massively parallel supercomputers Intel Paragon XP/S 5, XP/S 35, and Kendall Square Research, and on the multiprocessor SGI Onyx computer at Oak Ridge National Laboratory. The preliminary testing results of the developed program have shown scalable performance for reasonably sized computational domains.

MPI implementation of PHOENICS: A general purpose computational fluid dynamics code

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

Simunovic, S.; Zacharia, T.; Baltas, N.

1995-04-01

PHOENICS is a suite of computational analysis programs that are used for simulation of fluid flow, heat transfer, and dynamical reaction processes. The parallel version of the solver EARTH for the Computational Fluid Dynamics (CFD) program PHOENICS has been implemented using Message Passing Interface (MPI) standard. Implementation of MPI version of PHOENICS makes this computational tool portable to a wide range of parallel machines and enables the use of high performance computing for large scale computational simulations. MPI libraries are available on several parallel architectures making the program usable across different architectures as well as on heterogeneous computer networks. Themore » Intel Paragon NX and MPI versions of the program have been developed and tested on massively parallel supercomputers Intel Paragon XP/S 5, XP/S 35, and Kendall Square Research, and on the multiprocessor SGI Onyx computer at Oak Ridge National Laboratory. The preliminary testing results of the developed program have shown scalable performance for reasonably sized computational domains.« less

Biocellion: accelerating computer simulation of multicellular biological system models

PubMed Central

Kang, Seunghwa; Kahan, Simon; McDermott, Jason; Flann, Nicholas; Shmulevich, Ilya

2014-01-01

Motivation: Biological system behaviors are often the outcome of complex interactions among a large number of cells and their biotic and abiotic environment. Computational biologists attempt to understand, predict and manipulate biological system behavior through mathematical modeling and computer simulation. Discrete agent-based modeling (in combination with high-resolution grids to model the extracellular environment) is a popular approach for building biological system models. However, the computational complexity of this approach forces computational biologists to resort to coarser resolution approaches to simulate large biological systems. High-performance parallel computers have the potential to address the computing challenge, but writing efficient software for parallel computers is difficult and time-consuming. Results: We have developed Biocellion, a high-performance software framework, to solve this computing challenge using parallel computers. To support a wide range of multicellular biological system models, Biocellion asks users to provide their model specifics by filling the function body of pre-defined model routines. Using Biocellion, modelers without parallel computing expertise can efficiently exploit parallel computers with less effort than writing sequential programs from scratch. We simulate cell sorting, microbial patterning and a bacterial system in soil aggregate as case studies. Availability and implementation: Biocellion runs on x86 compatible systems with the 64 bit Linux operating system and is freely available for academic use. Visit http://biocellion.com for additional information. Contact: seunghwa.kang@pnnl.gov PMID:25064572

Research in Parallel Algorithms and Software for Computational Aerosciences

NASA Technical Reports Server (NTRS)

Domel, Neal D.

1996-01-01

Phase I is complete for the development of a Computational Fluid Dynamics parallel code with automatic grid generation and adaptation for the Euler analysis of flow over complex geometries. SPLITFLOW, an unstructured Cartesian grid code developed at Lockheed Martin Tactical Aircraft Systems, has been modified for a distributed memory/massively parallel computing environment. The parallel code is operational on an SGI network, Cray J90 and C90 vector machines, SGI Power Challenge, and Cray T3D and IBM SP2 massively parallel machines. Parallel Virtual Machine (PVM) is the message passing protocol for portability to various architectures. A domain decomposition technique was developed which enforces dynamic load balancing to improve solution speed and memory requirements. A host/node algorithm distributes the tasks. The solver parallelizes very well, and scales with the number of processors. Partially parallelized and non-parallelized tasks consume most of the wall clock time in a very fine grain environment. Timing comparisons on a Cray C90 demonstrate that Parallel SPLITFLOW runs 2.4 times faster on 8 processors than its non-parallel counterpart autotasked over 8 processors.

Research in Parallel Algorithms and Software for Computational Aerosciences

NASA Technical Reports Server (NTRS)

Domel, Neal D.

1996-01-01

Phase 1 is complete for the development of a computational fluid dynamics CFD) parallel code with automatic grid generation and adaptation for the Euler analysis of flow over complex geometries. SPLITFLOW, an unstructured Cartesian grid code developed at Lockheed Martin Tactical Aircraft Systems, has been modified for a distributed memory/massively parallel computing environment. The parallel code is operational on an SGI network, Cray J90 and C90 vector machines, SGI Power Challenge, and Cray T3D and IBM SP2 massively parallel machines. Parallel Virtual Machine (PVM) is the message passing protocol for portability to various architectures. A domain decomposition technique was developed which enforces dynamic load balancing to improve solution speed and memory requirements. A host/node algorithm distributes the tasks. The solver parallelizes very well, and scales with the number of processors. Partially parallelized and non-parallelized tasks consume most of the wall clock time in a very fine grain environment. Timing comparisons on a Cray C90 demonstrate that Parallel SPLITFLOW runs 2.4 times faster on 8 processors than its non-parallel counterpart autotasked over 8 processors.

Performance analysis of three dimensional integral equation computations on a massively parallel computer. M.S. Thesis

NASA Technical Reports Server (NTRS)

Logan, Terry G.

1994-01-01

The purpose of this study is to investigate the performance of the integral equation computations using numerical source field-panel method in a massively parallel processing (MPP) environment. A comparative study of computational performance of the MPP CM-5 computer and conventional Cray-YMP supercomputer for a three-dimensional flow problem is made. A serial FORTRAN code is converted into a parallel CM-FORTRAN code. Some performance results are obtained on CM-5 with 32, 62, 128 nodes along with those on Cray-YMP with a single processor. The comparison of the performance indicates that the parallel CM-FORTRAN code near or out-performs the equivalent serial FORTRAN code for some cases.

Parallel aeroelastic computations for wing and wing-body configurations

NASA Technical Reports Server (NTRS)

Byun, Chansup

1994-01-01

The objective of this research is to develop computationally efficient methods for solving fluid-structural interaction problems by directly coupling finite difference Euler/Navier-Stokes equations for fluids and finite element dynamics equations for structures on parallel computers. This capability will significantly impact many aerospace projects of national importance such as Advanced Subsonic Civil Transport (ASCT), where the structural stability margin becomes very critical at the transonic region. This research effort will have direct impact on the High Performance Computing and Communication (HPCC) Program of NASA in the area of parallel computing.

Implementation of a 3D mixing layer code on parallel computers

NASA Technical Reports Server (NTRS)

Roe, K.; Thakur, R.; Dang, T.; Bogucz, E.

1995-01-01

This paper summarizes our progress and experience in the development of a Computational-Fluid-Dynamics code on parallel computers to simulate three-dimensional spatially-developing mixing layers. In this initial study, the three-dimensional time-dependent Euler equations are solved using a finite-volume explicit time-marching algorithm. The code was first programmed in Fortran 77 for sequential computers. The code was then converted for use on parallel computers using the conventional message-passing technique, while we have not been able to compile the code with the present version of HPF compilers.

NASA Workshop on Computational Structural Mechanics 1987, part 1

NASA Technical Reports Server (NTRS)

Sykes, Nancy P. (Editor)

1989-01-01

Topics in Computational Structural Mechanics (CSM) are reviewed. CSM parallel structural methods, a transputer finite element solver, architectures for multiprocessor computers, and parallel eigenvalue extraction are among the topics discussed.

Evaluation of LANDSAT-2 (ERTS) images applied to geologic structures and mineral resources of South America. [Salar de Coposa, Chile and Salar of Uyuni, Bolivia

NASA Technical Reports Server (NTRS)

Carter, W. D. (Principal Investigator); Kowalik, W. S.

1976-01-01

The author has identified the following significant results. The Salar of Coposa is located in northern Chile along the frontier with Bolivia. The surface was divided into six general classes of materials. Analysis of LANDSAT image 1243-14001 by use of interactive multispectral computer (Image 100) enabled accurate repetition of these general classes based on reflectance. The Salar of Uyuni is the largest of the South American evaporite deposits. Using image 1243-13595, and parallel piped computer classification of reflectance units, the Salar was divided into nine classes ranging from deep to shallow water, water over salt, salt saturated with water, and several classes of dry salt.

Parallel computation with molecular-motor-propelled agents in nanofabricated networks.

PubMed

Nicolau, Dan V; Lard, Mercy; Korten, Till; van Delft, Falco C M J M; Persson, Malin; Bengtsson, Elina; Månsson, Alf; Diez, Stefan; Linke, Heiner; Nicolau, Dan V

2016-03-08

The combinatorial nature of many important mathematical problems, including nondeterministic-polynomial-time (NP)-complete problems, places a severe limitation on the problem size that can be solved with conventional, sequentially operating electronic computers. There have been significant efforts in conceiving parallel-computation approaches in the past, for example: DNA computation, quantum computation, and microfluidics-based computation. However, these approaches have not proven, so far, to be scalable and practical from a fabrication and operational perspective. Here, we report the foundations of an alternative parallel-computation system in which a given combinatorial problem is encoded into a graphical, modular network that is embedded in a nanofabricated planar device. Exploring the network in a parallel fashion using a large number of independent, molecular-motor-propelled agents then solves the mathematical problem. This approach uses orders of magnitude less energy than conventional computers, thus addressing issues related to power consumption and heat dissipation. We provide a proof-of-concept demonstration of such a device by solving, in a parallel fashion, the small instance {2, 5, 9} of the subset sum problem, which is a benchmark NP-complete problem. Finally, we discuss the technical advances necessary to make our system scalable with presently available technology.

ADHydro: A Large-scale High Resolution Multi-Physics Distributed Water Resources Model for Water Resource Simulations in a Parallel Computing Environment

NASA Astrophysics Data System (ADS)

lai, W.; Steinke, R. C.; Ogden, F. L.

2013-12-01

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 development of a 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. The model, which we call ADHydro, is aimed at simulating important processes in the Rocky Mountain west, includes: rainfall and infiltration, snowfall and snowmelt in complex terrain, vegetation and evapotranspiration, soil heat flux and freezing, overland flow, channel flow, groundwater flow and water management. The ADHydro model uses the explicit finite volume method to solve PDEs for 2D overland flow, 2D saturated groundwater flow coupled to 1D channel flow. The model has a quasi-3D formulation that couples 2D overland flow and 2D saturated groundwater flow using the 1D Talbot-Ogden finite water-content infiltration and redistribution model. This eliminates difficulties in solving the highly nonlinear 3D Richards equation, while the finite volume Talbot-Ogden infiltration solution is computationally efficient, guaranteed to conserve mass, and allows simulation of the effect of near-surface groundwater tables on runoff generation. The process-level components of the model are being individually tested and validated. The model as a whole will be tested on the Green River basin in Wyoming and ultimately applied to the entire Upper Colorado River basin. ADHydro development has necessitated development of tools for large-scale watershed modeling, including open-source workflow steps to extract hydromorphological information from GIS data, integrate hydrometeorological and water management forcing input, and post-processing and visualization of large output data sets. The ADHydro model will be coupled with relevant components of the NOAH-MP land surface scheme and the WRF mesoscale meteorological model. Model objectives include well documented Application Programming Interfaces (APIs) to facilitate modifications and additions by others. We will release the model as open-source in 2014 and begin establishing a users' community.

Costa - Introduction to 2015 Annual Report

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

Costa, James E.

In parallel with Sandia National Laboratories having two major locations (NM and CA), along with a number of smaller facilities across the nation, so too is the distribution of scientific, engineering and computing resources. As a part of Sandia’s Institutional Computing Program, CA site-based Sandia computer scientists and engineers have been providing mission and research staff with local CA resident expertise on computing options while also focusing on two growing high performance computing research problems. The first is how to increase system resilience to failure, as machines grow larger, more complex and heterogeneous. The second is how to ensure thatmore » computer hardware and configurations are optimized for specialized data analytical mission needs within the overall Sandia computing environment, including the HPC subenvironment. All of these activities support the larger Sandia effort in accelerating development and integration of high performance computing into national security missions. Sandia continues to both promote national R&D objectives, including the recent Presidential Executive Order establishing the National Strategic Computing Initiative and work to ensure that the full range of computing services and capabilities are available for all mission responsibilities, from national security to energy to homeland defense.« less

Parameters that affect parallel processing for computational electromagnetic simulation codes on high performance computing clusters

NASA Astrophysics Data System (ADS)

Moon, Hongsik

What is the impact of multicore and associated advanced technologies on computational software for science? Most researchers and students have multicore laptops or desktops for their research and they need computing power to run computational software packages. Computing power was initially derived from Central Processing Unit (CPU) clock speed. That changed when increases in clock speed became constrained by power requirements. Chip manufacturers turned to multicore CPU architectures and associated technological advancements to create the CPUs for the future. Most software applications benefited by the increased computing power the same way that increases in clock speed helped applications run faster. However, for Computational ElectroMagnetics (CEM) software developers, this change was not an obvious benefit - it appeared to be a detriment. Developers were challenged to find a way to correctly utilize the advancements in hardware so that their codes could benefit. The solution was parallelization and this dissertation details the investigation to address these challenges. Prior to multicore CPUs, advanced computer technologies were compared with the performance using benchmark software and the metric was FLoting-point Operations Per Seconds (FLOPS) which indicates system performance for scientific applications that make heavy use of floating-point calculations. Is FLOPS an effective metric for parallelized CEM simulation tools on new multicore system? Parallel CEM software needs to be benchmarked not only by FLOPS but also by the performance of other parameters related to type and utilization of the hardware, such as CPU, Random Access Memory (RAM), hard disk, network, etc. The codes need to be optimized for more than just FLOPs and new parameters must be included in benchmarking. In this dissertation, the parallel CEM software named High Order Basis Based Integral Equation Solver (HOBBIES) is introduced. This code was developed to address the needs of the changing computer hardware platforms in order to provide fast, accurate and efficient solutions to large, complex electromagnetic problems. The research in this dissertation proves that the performance of parallel code is intimately related to the configuration of the computer hardware and can be maximized for different hardware platforms. To benchmark and optimize the performance of parallel CEM software, a variety of large, complex projects are created and executed on a variety of computer platforms. The computer platforms used in this research are detailed in this dissertation. The projects run as benchmarks are also described in detail and results are presented. The parameters that affect parallel CEM software on High Performance Computing Clusters (HPCC) are investigated. This research demonstrates methods to maximize the performance of parallel CEM software code.

A highly efficient multi-core algorithm for clustering extremely large datasets

PubMed Central

2010-01-01

Background In recent years, the demand for computational power in computational biology has increased due to rapidly growing data sets from microarray and other high-throughput technologies. This demand is likely to increase. Standard algorithms for analyzing data, such as cluster algorithms, need to be parallelized for fast processing. Unfortunately, most approaches for parallelizing algorithms largely rely on network communication protocols connecting and requiring multiple computers. One answer to this problem is to utilize the intrinsic capabilities in current multi-core hardware to distribute the tasks among the different cores of one computer. Results We introduce a multi-core parallelization of the k-means and k-modes cluster algorithms based on the design principles of transactional memory for clustering gene expression microarray type data and categorial SNP data. Our new shared memory parallel algorithms show to be highly efficient. We demonstrate their computational power and show their utility in cluster stability and sensitivity analysis employing repeated runs with slightly changed parameters. Computation speed of our Java based algorithm was increased by a factor of 10 for large data sets while preserving computational accuracy compared to single-core implementations and a recently published network based parallelization. Conclusions Most desktop computers and even notebooks provide at least dual-core processors. Our multi-core algorithms show that using modern algorithmic concepts, parallelization makes it possible to perform even such laborious tasks as cluster sensitivity and cluster number estimation on the laboratory computer. PMID:20370922

Cost-Benefit Analysis of Computer Resources for Machine Learning

USGS Publications Warehouse

Champion, Richard A.

2007-01-01

Machine learning describes pattern-recognition algorithms - in this case, probabilistic neural networks (PNNs). These can be computationally intensive, in part because of the nonlinear optimizer, a numerical process that calibrates the PNN by minimizing a sum of squared errors. This report suggests efficiencies that are expressed as cost and benefit. The cost is computer time needed to calibrate the PNN, and the benefit is goodness-of-fit, how well the PNN learns the pattern in the data. There may be a point of diminishing returns where a further expenditure of computer resources does not produce additional benefits. Sampling is suggested as a cost-reduction strategy. One consideration is how many points to select for calibration and another is the geometric distribution of the points. The data points may be nonuniformly distributed across space, so that sampling at some locations provides additional benefit while sampling at other locations does not. A stratified sampling strategy can be designed to select more points in regions where they reduce the calibration error and fewer points in regions where they do not. Goodness-of-fit tests ensure that the sampling does not introduce bias. This approach is illustrated by statistical experiments for computing correlations between measures of roadless area and population density for the San Francisco Bay Area. The alternative to training efficiencies is to rely on high-performance computer systems. These may require specialized programming and algorithms that are optimized for parallel performance.

Support for Debugging Automatically Parallelized Programs

NASA Technical Reports Server (NTRS)

Hood, Robert; Jost, Gabriele; Biegel, Bryan (Technical Monitor)

2001-01-01

This viewgraph presentation provides information on the technical aspects of debugging computer code that has been automatically converted for use in a parallel computing system. Shared memory parallelization and distributed memory parallelization entail separate and distinct challenges for a debugging program. A prototype system has been developed which integrates various tools for the debugging of automatically parallelized programs including the CAPTools Database which provides variable definition information across subroutines as well as array distribution information.

Parallel language constructs for tensor product computations on loosely coupled architectures

NASA Technical Reports Server (NTRS)

Mehrotra, Piyush; Van Rosendale, John

1989-01-01

A set of language primitives designed to allow the specification of parallel numerical algorithms at a higher level is described. The authors focus on tensor product array computations, a simple but important class of numerical algorithms. They consider first the problem of programming one-dimensional kernel routines, such as parallel tridiagonal solvers, and then look at how such parallel kernels can be combined to form parallel tensor product algorithms.

Graphics Processing Unit–Enhanced Genetic Algorithms for Solving the Temporal Dynamics of Gene Regulatory Networks

PubMed Central

García-Calvo, Raúl; Guisado, JL; Diaz-del-Rio, Fernando; Córdoba, Antonio; Jiménez-Morales, Francisco

2018-01-01

Understanding the regulation of gene expression is one of the key problems in current biology. A promising method for that purpose is the determination of the temporal dynamics between known initial and ending network states, by using simple acting rules. The huge amount of rule combinations and the nonlinear inherent nature of the problem make genetic algorithms an excellent candidate for finding optimal solutions. As this is a computationally intensive problem that needs long runtimes in conventional architectures for realistic network sizes, it is fundamental to accelerate this task. In this article, we study how to develop efficient parallel implementations of this method for the fine-grained parallel architecture of graphics processing units (GPUs) using the compute unified device architecture (CUDA) platform. An exhaustive and methodical study of various parallel genetic algorithm schemes—master-slave, island, cellular, and hybrid models, and various individual selection methods (roulette, elitist)—is carried out for this problem. Several procedures that optimize the use of the GPU’s resources are presented. We conclude that the implementation that produces better results (both from the performance and the genetic algorithm fitness perspectives) is simulating a few thousands of individuals grouped in a few islands using elitist selection. This model comprises 2 mighty factors for discovering the best solutions: finding good individuals in a short number of generations, and introducing genetic diversity via a relatively frequent and numerous migration. As a result, we have even found the optimal solution for the analyzed gene regulatory network (GRN). In addition, a comparative study of the performance obtained by the different parallel implementations on GPU versus a sequential application on CPU is carried out. In our tests, a multifold speedup was obtained for our optimized parallel implementation of the method on medium class GPU over an equivalent sequential single-core implementation running on a recent Intel i7 CPU. This work can provide useful guidance to researchers in biology, medicine, or bioinformatics in how to take advantage of the parallelization on massively parallel devices and GPUs to apply novel metaheuristic algorithms powered by nature for real-world applications (like the method to solve the temporal dynamics of GRNs). PMID:29662297

Graphics Processing Unit-Enhanced Genetic Algorithms for Solving the Temporal Dynamics of Gene Regulatory Networks.

PubMed

García-Calvo, Raúl; Guisado, J L; Diaz-Del-Rio, Fernando; Córdoba, Antonio; Jiménez-Morales, Francisco

2018-01-01

Understanding the regulation of gene expression is one of the key problems in current biology. A promising method for that purpose is the determination of the temporal dynamics between known initial and ending network states, by using simple acting rules. The huge amount of rule combinations and the nonlinear inherent nature of the problem make genetic algorithms an excellent candidate for finding optimal solutions. As this is a computationally intensive problem that needs long runtimes in conventional architectures for realistic network sizes, it is fundamental to accelerate this task. In this article, we study how to develop efficient parallel implementations of this method for the fine-grained parallel architecture of graphics processing units (GPUs) using the compute unified device architecture (CUDA) platform. An exhaustive and methodical study of various parallel genetic algorithm schemes-master-slave, island, cellular, and hybrid models, and various individual selection methods (roulette, elitist)-is carried out for this problem. Several procedures that optimize the use of the GPU's resources are presented. We conclude that the implementation that produces better results (both from the performance and the genetic algorithm fitness perspectives) is simulating a few thousands of individuals grouped in a few islands using elitist selection. This model comprises 2 mighty factors for discovering the best solutions: finding good individuals in a short number of generations, and introducing genetic diversity via a relatively frequent and numerous migration. As a result, we have even found the optimal solution for the analyzed gene regulatory network (GRN). In addition, a comparative study of the performance obtained by the different parallel implementations on GPU versus a sequential application on CPU is carried out. In our tests, a multifold speedup was obtained for our optimized parallel implementation of the method on medium class GPU over an equivalent sequential single-core implementation running on a recent Intel i7 CPU. This work can provide useful guidance to researchers in biology, medicine, or bioinformatics in how to take advantage of the parallelization on massively parallel devices and GPUs to apply novel metaheuristic algorithms powered by nature for real-world applications (like the method to solve the temporal dynamics of GRNs).

Integration and Exposure of Large Scale Computational Resources Across the Earth System Grid Federation (ESGF)

NASA Astrophysics Data System (ADS)

Duffy, D.; Maxwell, T. P.; Doutriaux, C.; Williams, D. N.; Chaudhary, A.; Ames, S.

2015-12-01

As the size of remote sensing observations and model output data grows, the volume of the data has become overwhelming, even to many scientific experts. As societies are forced to better understand, mitigate, and adapt to climate changes, the combination of Earth observation data and global climate model projects is crucial to not only scientists but to policy makers, downstream applications, and even the public. Scientific progress on understanding climate is critically dependent on the availability of a reliable infrastructure that promotes data access, management, and provenance. The Earth System Grid Federation (ESGF) has created such an environment for the Intergovernmental Panel on Climate Change (IPCC). ESGF provides a federated global cyber infrastructure for data access and management of model outputs generated for the IPCC Assessment Reports (AR). The current generation of the ESGF federated grid allows consumers of the data to find and download data with limited capabilities for server-side processing. Since the amount of data for future AR is expected to grow dramatically, ESGF is working on integrating server-side analytics throughout the federation. The ESGF Compute Working Team (CWT) has created a Web Processing Service (WPS) Application Programming Interface (API) to enable access scalable computational resources. The API is the exposure point to high performance computing resources across the federation. Specifically, the API allows users to execute simple operations, such as maximum, minimum, average, and anomalies, on ESGF data without having to download the data. These operations are executed at the ESGF data node site with access to large amounts of parallel computing capabilities. This presentation will highlight the WPS API, its capabilities, provide implementation details, and discuss future developments.

Explorationists and dinosaurs

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

French, W.S.

1993-02-01

The exploration industry is changing, exploration technology is changing and the explorationist's job is changing. Resource companies are diversifying internationally and their central organizations are providing advisors rather than services. As a result, the relationship between the resource company and the contractor is changing. Resource companies are promoting standards so that all contract services in all parts of the world will look the same to their advisors. Contractors, for competitive reasons, want to look [open quotes]different[close quotes] from other contractors. The resource companies must encourage competition between contractors to insure the availability of new technology but must also resist themore » current trend of burdening the contractor with more and more of the risk involved in exploration. It is becoming more and more obvious that geophysical expenditures represent the best [open quotes]value added[close quotes] expenditures in exploration and development budgets. As a result, seismic-related contractors represent the growth component of our industry. The predominant growth is in 3-D seismic technology, and this growth is being further propelled by the computational power of the new generation of massively parallel computers and by recent advances in computer graphic techniques. Interpretation of seismic data involves the analysis of wavelet shapes and amplitudes prior to stacking the data. Thus, modern interpretation involves understanding compressional waves, shear waves, and propagating modes which create noise and interference. Modern interpretation and processing are carried out simultaneously, iteratively, and interactively and involve many physics-related concepts. These concepts are not merely tools for the interpretation, they are the interpretation. Explorationists who do not recognize this fact are going the way of the dinosaurs.« less

Redundant binary number representation for an inherently parallel arithmetic on optical computers.

PubMed

De Biase, G A; Massini, A

1993-02-10

A simple redundant binary number representation suitable for digital-optical computers is presented. By means of this representation it is possible to build an arithmetic with carry-free parallel algebraic sums carried out in constant time and parallel multiplication in log N time. This redundant number representation naturally fits the 2's complement binary number system and permits the construction of inherently parallel arithmetic units that are used in various optical technologies. Some properties of this number representation and several examples of computation are presented.

Backtracking and Re-execution in the Automatic Debugging of Parallelized Programs

NASA Technical Reports Server (NTRS)

Matthews, Gregory; Hood, Robert; Johnson, Stephen; Leggett, Peter; Biegel, Bryan (Technical Monitor)

2002-01-01

In this work we describe a new approach using relative debugging to find differences in computation between a serial program and a parallel version of th it program. We use a combination of re-execution and backtracking in order to find the first difference in computation that may ultimately lead to an incorrect value that the user has indicated. In our prototype implementation we use static analysis information from a parallelization tool in order to perform the backtracking as well as the mapping required between serial and parallel computations.

Modularized Parallel Neutron Instrument Simulation on the TeraGrid

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

Chen, Meili; Cobb, John W; Hagen, Mark E

2007-01-01

In order to build a bridge between the TeraGrid (TG), a national scale cyberinfrastructure resource, and neutron science, the Neutron Science TeraGrid Gateway (NSTG) is focused on introducing productive HPC usage to the neutron science community, primarily the Spallation Neutron Source (SNS) at Oak Ridge National Laboratory (ORNL). Monte Carlo simulations are used as a powerful tool for instrument design and optimization at SNS. One of the successful efforts of a collaboration team composed of NSTG HPC experts and SNS instrument scientists is the development of a software facility named PSoNI, Parallelizing Simulations of Neutron Instruments. Parallelizing the traditional serialmore » instrument simulation on TeraGrid resources, PSoNI quickly computes full instrument simulation at sufficient statistical levels in instrument de-sign. Upon SNS successful commissioning, to the end of 2007, three out of five commissioned instruments in SNS target station will be available for initial users. Advanced instrument study, proposal feasibility evalua-tion, and experiment planning are on the immediate schedule of SNS, which pose further requirements such as flexibility and high runtime efficiency on fast instrument simulation. PSoNI has been redesigned to meet the new challenges and a preliminary version is developed on TeraGrid. This paper explores the motivation and goals of the new design, and the improved software structure. Further, it describes the realized new fea-tures seen from MPI parallelized McStas running high resolution design simulations of the SEQUOIA and BSS instruments at SNS. A discussion regarding future work, which is targeted to do fast simulation for automated experiment adjustment and comparing models to data in analysis, is also presented.« less

A Multi-Level Parallelization Concept for High-Fidelity Multi-Block Solvers

NASA Technical Reports Server (NTRS)

Hatay, Ferhat F.; Jespersen, Dennis C.; Guruswamy, Guru P.; Rizk, Yehia M.; Byun, Chansup; Gee, Ken; VanDalsem, William R. (Technical Monitor)

1997-01-01

The integration of high-fidelity Computational Fluid Dynamics (CFD) analysis tools with the industrial design process benefits greatly from the robust implementations that are transportable across a wide range of computer architectures. In the present work, a hybrid domain-decomposition and parallelization concept was developed and implemented into the widely-used NASA multi-block Computational Fluid Dynamics (CFD) packages implemented in ENSAERO and OVERFLOW. The new parallel solver concept, PENS (Parallel Euler Navier-Stokes Solver), employs both fine and coarse granularity in data partitioning as well as data coalescing to obtain the desired load-balance characteristics on the available computer platforms. This multi-level parallelism implementation itself introduces no changes to the numerical results, hence the original fidelity of the packages are identically preserved. The present implementation uses the Message Passing Interface (MPI) library for interprocessor message passing and memory accessing. By choosing an appropriate combination of the available partitioning and coalescing capabilities only during the execution stage, the PENS solver becomes adaptable to different computer architectures from shared-memory to distributed-memory platforms with varying degrees of parallelism. The PENS implementation on the IBM SP2 distributed memory environment at the NASA Ames Research Center obtains 85 percent scalable parallel performance using fine-grain partitioning of single-block CFD domains using up to 128 wide computational nodes. Multi-block CFD simulations of complete aircraft simulations achieve 75 percent perfect load-balanced executions using data coalescing and the two levels of parallelism. SGI PowerChallenge, SGI Origin 2000, and a cluster of workstations are the other platforms where the robustness of the implementation is tested. The performance behavior on the other computer platforms with a variety of realistic problems will be included as this on-going study progresses.

Parallel computing on Unix workstation arrays

NASA Astrophysics Data System (ADS)

Reale, F.; Bocchino, F.; Sciortino, S.

1994-12-01

We have tested arrays of general-purpose Unix workstations used as MIMD systems for massive parallel computations. In particular we have solved numerically a demanding test problem with a 2D hydrodynamic code, generally developed to study astrophysical flows, by exucuting it on arrays either of DECstations 5000/200 on Ethernet LAN, or of DECstations 3000/400, equipped with powerful Alpha processors, on FDDI LAN. The code is appropriate for data-domain decomposition, and we have used a library for parallelization previously developed in our Institute, and easily extended to work on Unix workstation arrays by using the PVM software toolset. We have compared the parallel efficiencies obtained on arrays of several processors to those obtained on a dedicated MIMD parallel system, namely a Meiko Computing Surface (CS-1), equipped with Intel i860 processors. We discuss the feasibility of using non-dedicated parallel systems and conclude that the convenience depends essentially on the size of the computational domain as compared to the relative processor power and network bandwidth. We point out that for future perspectives a parallel development of processor and network technology is important, and that the software still offers great opportunities of improvement, especially in terms of latency times in the message-passing protocols. In conditions of significant gain in terms of speedup, such workstation arrays represent a cost-effective approach to massive parallel computations.

Parallelization strategies for continuum-generalized method of moments on the multi-thread systems

NASA Astrophysics Data System (ADS)

Bustamam, A.; Handhika, T.; Ernastuti, Kerami, D.

2017-07-01

Continuum-Generalized Method of Moments (C-GMM) covers the Generalized Method of Moments (GMM) shortfall which is not as efficient as Maximum Likelihood estimator by using the continuum set of moment conditions in a GMM framework. However, this computation would take a very long time since optimizing regularization parameter. Unfortunately, these calculations are processed sequentially whereas in fact all modern computers are now supported by hierarchical memory systems and hyperthreading technology, which allowing for parallel computing. This paper aims to speed up the calculation process of C-GMM by designing a parallel algorithm for C-GMM on the multi-thread systems. First, parallel regions are detected for the original C-GMM algorithm. There are two parallel regions in the original C-GMM algorithm, that are contributed significantly to the reduction of computational time: the outer-loop and the inner-loop. Furthermore, this parallel algorithm will be implemented with standard shared-memory application programming interface, i.e. Open Multi-Processing (OpenMP). The experiment shows that the outer-loop parallelization is the best strategy for any number of observations.

Multirate-based fast parallel algorithms for 2-D DHT-based real-valued discrete Gabor transform.

PubMed

Tao, Liang; Kwan, Hon Keung

2012-07-01

Novel algorithms for the multirate and fast parallel implementation of the 2-D discrete Hartley transform (DHT)-based real-valued discrete Gabor transform (RDGT) and its inverse transform are presented in this paper. A 2-D multirate-based analysis convolver bank is designed for the 2-D RDGT, and a 2-D multirate-based synthesis convolver bank is designed for the 2-D inverse RDGT. The parallel channels in each of the two convolver banks have a unified structure and can apply the 2-D fast DHT algorithm to speed up their computations. The computational complexity of each parallel channel is low and is independent of the Gabor oversampling rate. All the 2-D RDGT coefficients of an image are computed in parallel during the analysis process and can be reconstructed in parallel during the synthesis process. The computational complexity and time of the proposed parallel algorithms are analyzed and compared with those of the existing fastest algorithms for 2-D discrete Gabor transforms. The results indicate that the proposed algorithms are the fastest, which make them attractive for real-time image processing.

Parallel Application Performance on Two Generations of Intel Xeon HPC Platforms

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

Chang, Christopher H.; Long, Hai; Sides, Scott

2015-10-15

Two next-generation node configurations hosting the Haswell microarchitecture were tested with a suite of microbenchmarks and application examples, and compared with a current Ivy Bridge production node on NREL" tm s Peregrine high-performance computing cluster. A primary conclusion from this study is that the additional cores are of little value to individual task performance--limitations to application parallelism, or resource contention among concurrently running but independent tasks, limits effective utilization of these added cores. Hyperthreading generally impacts throughput negatively, but can improve performance in the absence of detailed attention to runtime workflow configuration. The observations offer some guidance to procurement ofmore » future HPC systems at NREL. First, raw core count must be balanced with available resources, particularly memory bandwidth. Balance-of-system will determine value more than processor capability alone. Second, hyperthreading continues to be largely irrelevant to the workloads that are commonly seen, and were tested here, at NREL. Finally, perhaps the most impactful enhancement to productivity might occur through enabling multiple concurrent jobs per node. Given the right type and size of workload, more may be achieved by doing many slow things at once, than fast things in order.« less

Planning and Resource Management in an Intelligent Automated Power Management System

NASA Technical Reports Server (NTRS)

Morris, Robert A.

1991-01-01

Power system management is a process of guiding a power system towards the objective of continuous supply of electrical power to a set of loads. Spacecraft power system management requires planning and scheduling, since electrical power is a scarce resource in space. The automation of power system management for future spacecraft has been recognized as an important R&D goal. Several automation technologies have emerged including the use of expert systems for automating human problem solving capabilities such as rule based expert system for fault diagnosis and load scheduling. It is questionable whether current generation expert system technology is applicable for power system management in space. The objective of the ADEPTS (ADvanced Electrical Power management Techniques for Space systems) is to study new techniques for power management automation. These techniques involve integrating current expert system technology with that of parallel and distributed computing, as well as a distributed, object-oriented approach to software design. The focus of the current study is the integration of new procedures for automatically planning and scheduling loads with procedures for performing fault diagnosis and control. The objective is the concurrent execution of both sets of tasks on separate transputer processors, thus adding parallelism to the overall management process.

CAMPAIGN: an open-source library of GPU-accelerated data clustering algorithms.

PubMed

Kohlhoff, Kai J; Sosnick, Marc H; Hsu, William T; Pande, Vijay S; Altman, Russ B

2011-08-15

Data clustering techniques are an essential component of a good data analysis toolbox. Many current bioinformatics applications are inherently compute-intense and work with very large datasets. Sequential algorithms are inadequate for providing the necessary performance. For this reason, we have created Clustering Algorithms for Massively Parallel Architectures, Including GPU Nodes (CAMPAIGN), a central resource for data clustering algorithms and tools that are implemented specifically for execution on massively parallel processing architectures. CAMPAIGN is a library of data clustering algorithms and tools, written in 'C for CUDA' for Nvidia GPUs. The library provides up to two orders of magnitude speed-up over respective CPU-based clustering algorithms and is intended as an open-source resource. New modules from the community will be accepted into the library and the layout of it is such that it can easily be extended to promising future platforms such as OpenCL. Releases of the CAMPAIGN library are freely available for download under the LGPL from https://simtk.org/home/campaign. Source code can also be obtained through anonymous subversion access as described on https://simtk.org/scm/?group_id=453. kjk33@cantab.net.

Unclassified Computing Capability: User Responses to a Multiprogrammatic and Institutional Computing Questionnaire

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

McCoy, M; Kissel, L

2002-01-29

We are experimenting with a new computing model to be applied to a new computer dedicated to that model. Several LLNL science teams now have computational requirements, evidenced by the mature scientific applications that have been developed over the past five plus years, that far exceed the capability of the institution's computing resources. Thus, there is increased demand for dedicated, powerful parallel computational systems. Computation can, in the coming year, potentially field a capability system that is low cost because it will be based on a model that employs open source software and because it will use PC (IA32-P4) hardware.more » This incurs significant computer science risk regarding stability and system features but also presents great opportunity. We believe the risks can be managed, but the existence of risk cannot be ignored. In order to justify the budget for this system, we need to make the case that it serves science and, through serving science, serves the institution. That is the point of the meeting and the White Paper that we are proposing to prepare. The questions are listed and the responses received are in this report.« less

Parallel Computational Fluid Dynamics: Current Status and Future Requirements

NASA Technical Reports Server (NTRS)

Simon, Horst D.; VanDalsem, William R.; Dagum, Leonardo; Kutler, Paul (Technical Monitor)

1994-01-01

One or the key objectives of the Applied Research Branch in the Numerical Aerodynamic Simulation (NAS) Systems Division at NASA Allies Research Center is the accelerated introduction of highly parallel machines into a full operational environment. In this report we discuss the performance results obtained from the implementation of some computational fluid dynamics (CFD) applications on the Connection Machine CM-2 and the Intel iPSC/860. We summarize some of the experiences made so far with the parallel testbed machines at the NAS Applied Research Branch. Then we discuss the long term computational requirements for accomplishing some of the grand challenge problems in computational aerosciences. We argue that only massively parallel machines will be able to meet these grand challenge requirements, and we outline the computer science and algorithm research challenges ahead.

Scalable and massively parallel Monte Carlo photon transport simulations for heterogeneous computing platforms

NASA Astrophysics Data System (ADS)

Yu, Leiming; Nina-Paravecino, Fanny; Kaeli, David; Fang, Qianqian

2018-01-01

We present a highly scalable Monte Carlo (MC) three-dimensional photon transport simulation platform designed for heterogeneous computing systems. Through the development of a massively parallel MC algorithm using the Open Computing Language framework, this research extends our existing graphics processing unit (GPU)-accelerated MC technique to a highly scalable vendor-independent heterogeneous computing environment, achieving significantly improved performance and software portability. A number of parallel computing techniques are investigated to achieve portable performance over a wide range of computing hardware. Furthermore, multiple thread-level and device-level load-balancing strategies are developed to obtain efficient simulations using multiple central processing units and GPUs.

A parallel-processing approach to computing for the geographic sciences; applications and systems enhancements

USGS Publications Warehouse

Crane, Michael; Steinwand, Dan; Beckmann, Tim; Krpan, Greg; Liu, Shu-Guang; Nichols, Erin; Haga, Jim; Maddox, Brian; Bilderback, Chris; Feller, Mark; Homer, George

2001-01-01

The overarching goal of this project is to build a spatially distributed infrastructure for information science research by forming a team of information science researchers and providing them with similar hardware and software tools to perform collaborative research. Four geographically distributed Centers of the U.S. Geological Survey (USGS) are developing their own clusters of low-cost, personal computers into parallel computing environments that provide a costeffective way for the USGS to increase participation in the high-performance computing community. Referred to as Beowulf clusters, these hybrid systems provide the robust computing power required for conducting information science research into parallel computing systems and applications.

Computational Particle Dynamic Simulations on Multicore Processors (CPDMu) Final Report Phase I

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

Schmalz, Mark S

2011-07-24

Statement of Problem - Department of Energy has many legacy codes for simulation of computational particle dynamics and computational fluid dynamics applications that are designed to run on sequential processors and are not easily parallelized. Emerging high-performance computing architectures employ massively parallel multicore architectures (e.g., graphics processing units) to increase throughput. Parallelization of legacy simulation codes is a high priority, to achieve compatibility, efficiency, accuracy, and extensibility. General Statement of Solution - A legacy simulation application designed for implementation on mainly-sequential processors has been represented as a graph G. Mathematical transformations, applied to G, produce a graph representation {und G}more » for a high-performance architecture. Key computational and data movement kernels of the application were analyzed/optimized for parallel execution using the mapping G {yields} {und G}, which can be performed semi-automatically. This approach is widely applicable to many types of high-performance computing systems, such as graphics processing units or clusters comprised of nodes that contain one or more such units. Phase I Accomplishments - Phase I research decomposed/profiled computational particle dynamics simulation code for rocket fuel combustion into low and high computational cost regions (respectively, mainly sequential and mainly parallel kernels), with analysis of space and time complexity. Using the research team's expertise in algorithm-to-architecture mappings, the high-cost kernels were transformed, parallelized, and implemented on Nvidia Fermi GPUs. Measured speedups (GPU with respect to single-core CPU) were approximately 20-32X for realistic model parameters, without final optimization. Error analysis showed no loss of computational accuracy. Commercial Applications and Other Benefits - The proposed research will constitute a breakthrough in solution of problems related to efficient parallel computation of particle and fluid dynamics simulations. These problems occur throughout DOE, military and commercial sectors: the potential payoff is high. We plan to license or sell the solution to contractors for military and domestic applications such as disaster simulation (aerodynamic and hydrodynamic), Government agencies (hydrological and environmental simulations), and medical applications (e.g., in tomographic image reconstruction). Keywords - High-performance Computing, Graphic Processing Unit, Fluid/Particle Simulation. Summary for Members of Congress - Department of Energy has many simulation codes that must compute faster, to be effective. The Phase I research parallelized particle/fluid simulations for rocket combustion, for high-performance computing systems.« less

Parallelizing flow-accumulation calculations on graphics processing units—From iterative DEM preprocessing algorithm to recursive multiple-flow-direction algorithm

NASA Astrophysics Data System (ADS)

Qin, Cheng-Zhi; Zhan, Lijun

2012-06-01

As one of the important tasks in digital terrain analysis, the calculation of flow accumulations from gridded digital elevation models (DEMs) usually involves two steps in a real application: (1) using an iterative DEM preprocessing algorithm to remove the depressions and flat areas commonly contained in real DEMs, and (2) using a recursive flow-direction algorithm to calculate the flow accumulation for every cell in the DEM. Because both algorithms are computationally intensive, quick calculation of the flow accumulations from a DEM (especially for a large area) presents a practical challenge to personal computer (PC) users. In recent years, rapid increases in hardware capacity of the graphics processing units (GPUs) provided in modern PCs have made it possible to meet this challenge in a PC environment. Parallel computing on GPUs using a compute-unified-device-architecture (CUDA) programming model has been explored to speed up the execution of the single-flow-direction algorithm (SFD). However, the parallel implementation on a GPU of the multiple-flow-direction (MFD) algorithm, which generally performs better than the SFD algorithm, has not been reported. Moreover, GPU-based parallelization of the DEM preprocessing step in the flow-accumulation calculations has not been addressed. This paper proposes a parallel approach to calculate flow accumulations (including both iterative DEM preprocessing and a recursive MFD algorithm) on a CUDA-compatible GPU. For the parallelization of an MFD algorithm (MFD-md), two different parallelization strategies using a GPU are explored. The first parallelization strategy, which has been used in the existing parallel SFD algorithm on GPU, has the problem of computing redundancy. Therefore, we designed a parallelization strategy based on graph theory. The application results show that the proposed parallel approach to calculate flow accumulations on a GPU performs much faster than either sequential algorithms or other parallel GPU-based algorithms based on existing parallelization strategies.

Parallelized Stochastic Cutoff Method for Long-Range Interacting Systems

NASA Astrophysics Data System (ADS)

Endo, Eishin; Toga, Yuta; Sasaki, Munetaka

2015-07-01

We present a method of parallelizing the stochastic cutoff (SCO) method, which is a Monte-Carlo method for long-range interacting systems. After interactions are eliminated by the SCO method, we subdivide a lattice into noninteracting interpenetrating sublattices. This subdivision enables us to parallelize the Monte-Carlo calculation in the SCO method. Such subdivision is found by numerically solving the vertex coloring of a graph created by the SCO method. We use an algorithm proposed by Kuhn and Wattenhofer to solve the vertex coloring by parallel computation. This method was applied to a two-dimensional magnetic dipolar system on an L × L square lattice to examine its parallelization efficiency. The result showed that, in the case of L = 2304, the speed of computation increased about 102 times by parallel computation with 288 processors.

Identifying failure in a tree network of a parallel computer

DOEpatents

Archer, Charles J.; Pinnow, Kurt W.; Wallenfelt, Brian P.

2010-08-24

Methods, parallel computers, and products are provided for identifying failure in a tree network of a parallel computer. The parallel computer includes one or more processing sets including an I/O node and a plurality of compute nodes. For each processing set embodiments include selecting a set of test compute nodes, the test compute nodes being a subset of the compute nodes of the processing set; measuring the performance of the I/O node of the processing set; measuring the performance of the selected set of test compute nodes; calculating a current test value in dependence upon the measured performance of the I/O node of the processing set, the measured performance of the set of test compute nodes, and a predetermined value for I/O node performance; and comparing the current test value with a predetermined tree performance threshold. If the current test value is below the predetermined tree performance threshold, embodiments include selecting another set of test compute nodes. If the current test value is not below the predetermined tree performance threshold, embodiments include selecting from the test compute nodes one or more potential problem nodes and testing individually potential problem nodes and links to potential problem nodes.

Optical Symbolic Computing

NASA Astrophysics Data System (ADS)

Neff, John A.

1989-12-01

Experiments originating from Gestalt psychology have shown that representing information in a symbolic form provides a more effective means to understanding. Computer scientists have been struggling for the last two decades to determine how best to create, manipulate, and store collections of symbolic structures. In the past, much of this struggling led to software innovations because that was the path of least resistance. For example, the development of heuristics for organizing the searching through knowledge bases was much less expensive than building massively parallel machines that could search in parallel. That is now beginning to change with the emergence of parallel architectures which are showing the potential for handling symbolic structures. This paper will review the relationships between symbolic computing and parallel computing architectures, and will identify opportunities for optics to significantly impact the performance of such computing machines. Although neural networks are an exciting subset of massively parallel computing structures, this paper will not touch on this area since it is receiving a great deal of attention in the literature. That is, the concepts presented herein do not consider the distributed representation of knowledge.

GPU-accelerated algorithms for many-particle continuous-time quantum walks

NASA Astrophysics Data System (ADS)

Piccinini, Enrico; Benedetti, Claudia; Siloi, Ilaria; Paris, Matteo G. A.; Bordone, Paolo

2017-06-01

Many-particle continuous-time quantum walks (CTQWs) represent a resource for several tasks in quantum technology, including quantum search algorithms and universal quantum computation. In order to design and implement CTQWs in a realistic scenario, one needs effective simulation tools for Hamiltonians that take into account static noise and fluctuations in the lattice, i.e. Hamiltonians containing stochastic terms. To this aim, we suggest a parallel algorithm based on the Taylor series expansion of the evolution operator, and compare its performances with those of algorithms based on the exact diagonalization of the Hamiltonian or a 4th order Runge-Kutta integration. We prove that both Taylor-series expansion and Runge-Kutta algorithms are reliable and have a low computational cost, the Taylor-series expansion showing the additional advantage of a memory allocation not depending on the precision of calculation. Both algorithms are also highly parallelizable within the SIMT paradigm, and are thus suitable for GPGPU computing. In turn, we have benchmarked 4 NVIDIA GPUs and 3 quad-core Intel CPUs for a 2-particle system over lattices of increasing dimension, showing that the speedup provided by GPU computing, with respect to the OPENMP parallelization, lies in the range between 8x and (more than) 20x, depending on the frequency of post-processing. GPU-accelerated codes thus allow one to overcome concerns about the execution time, and make it possible simulations with many interacting particles on large lattices, with the only limit of the memory available on the device.