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检索条件"任意字段=Working Conference on Massively Parallel Programming Models"

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massively parallel landscape-evolution modelling using general purpose graphical processing units

Massively parallel landscape-evolution modelling using gener...

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International conference on High Performance Computing

作者： A. S. McGough S. Liang M. Rapoportas R. Grey G. Kumar Vinod D. Maddy A. Trueman J. Wainwright School of Computing Science Newcastle University Newcastle upon Tyne UK School of Geography Politics and Sociology Newcastle University Newcastle upon Tyne UK Department of Geography Durham University Durham UK

As our expectations of what computer systems can do and our ability to capture data improves, the desire to perform ever more computationally intensive tasks increases. Often these tasks, comprising vast numbers of repeated computations, are highly interdependent on each other - a closely coupled problem. The process of Landscape-Evolution Modelling is an example of such a problem. In order to produce realistic models it is necessary to process landscapes containing millions of data points over time periods extending up to millions of years. This leads to non-tractable execution times, often in the order of years. Researchers therefore seek multiple orders of magnitude reduction in the execution time of these models. The massively parallel programming environment offered through General Purpose Graphical Processing Units offers the potential for multiple orders of magnitude speedup in code execution times. In this paper we demonstrate how the time dominant parts of a Landscape- Evolution Model can be recoded for a massively parallel architecture providing two orders of magnitude reduction in execution time.

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parallelization of Virtual Screening in Drug Discovery on massively parallel Architectures

Parallelization of Virtual Screening in Drug Discovery on Ma...

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Euromicro conference on parallel, Distributed and Network-Based Processing

作者： Gines D. Guerrero Horacio E. Perez-S´nchez Jose M. Cecilia Jose M. Garcia Grupo de Arquitectura y Computación Paralela Dpto. de Ingeniería y Tecnología de Computadores Universidad de Murcia Campus de Espinardo Murcia Spain

The current trend in medical research for the discovery of new drugs is the use of Virtual Screening (VS) methods. In these methods, the calculation of the non-bonded interactions, such as electrostatics or van der Waals forces, plays an important role, representing up to 80% of the total execution time. These kernels are computational intensive and massively parallel in nature, and thus they are well suited to be accelerated on parallel architectures. In this work, we discuss the effective parallelization of the non-bonded electrostatic interactions kernel for VS on three different parallel architectures: a shared memory system, a distributed memory system, and a Graphics Processing Units (GPUs). For an efficient handling of the computational intensive and massively parallelism of this kernel, we enable different data policies on those architectures to take advantage of all computational resources offered by them. Four implementations are provided based on MPI, OpenMP, Hybrid MPI Open MP and CUDA programming models. The sequential implementation is defeated by a wide margin by all parallel implementations, obtaining up to 72x speed-up factor on the shared memory system through OpenMP, up to 60x and229x speed-ups factors on the distributed memory system for the MPI implementation and the Hybrid MPI-Open MP implementation respectively, and finally, up to 213x speedup factor for the CUDA implementation on the GPU architecture to offer the best alternative in terms of performance/cost ratio.

关键词： Graphics processing unit Kernel Instruction sets Message systems parallel processing programming Computer architecture

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A GPU-based Architecture for parallel Image-aware Version Control

A GPU-based Architecture for Parallel Image-aware Version Co...

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(CSMR) European conference on Software Maintenance and Reengineering

作者： Jose Ricardo da Silva Junior Toni Pacheco Esteban Clua Leonardo Murta Instituto de Computação Universidade Federal Fluminense (UFF) Niteroi Brazil

Version control is considered a vital component for supporting professional software development and has been widely used for textual artifacts, like source code. However, binary artifacts have received small attention when compared to the former. This fact can impose huge restrictions for projects in the game and media industry, which use large amount of binary data, such as images, videos, graphics, 3D models, and animations, together with source code. For these kinds of artifacts, existing strategies, such as storing the file as a whole for each commit or performing conventional binary delta, consume significant storage space with duplicate data, and even worse, lose vital semantic information. As a response to this problem, this paper introduces an infrastructure to support version control of image artifacts. Due to the amount of data that must be processed, we implemented our proposal using a GPU architecture, allowing a massively parallel approach for version control. The proposed architecture provides speedup over 55 X if compared to the same implementation in CPU.

关键词： Graphics processing unit Computer architecture Image color analysis Process control Image reconstruction Context programming

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High Performance parallel JPEG2000 Streaming Decoder Using GPGPU-CPU Heterogeneous System

High Performance Parallel JPEG2000 Streaming Decoder Using G...

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International conference on Application Specific Systems (ASAP), Architectures and Processors

作者： Roto Le Joseph L. Mundy R. Iris Bahar School of Engineering Brown University Providence RI USA

The JPEG2000 image coding standard provides many superior features compared to JPEG and other compression standards. However, the relatively slow performance of JPEG2000, especially in software implementations, is a critical drawback of the standard. Moreover, as image sizes rapidly grow in size, higher demands on performance for image coding and processing are introduced, making the slow performance of JPEG2000 even further pronounced. While much effort over the past decade has been devoted to accelerating the JPEG2000 encoder, there have been very few studies focusing on improving the performance of the JPEG2000 decoder, despite the fact that the performance of the decoder is just as critical as the encoder. This paper proposes a high-performance JPEG2000 decoder that efficiently exploits the recent improvements of modern parallel programming models and hardware architectures. Specifically, a parallel streaming decoder running on a GPGPU-CPU heterogeneous system is developed to fully exploit the flexibility of the high-performance multi-core CPUs and the massively parallel capability of GPGPUs. In addition, a new task scheduling strategy is developed that exploits the soft-heterogeneity in OpenCL and C/C++ at runtime in order to gain a significant performance boost. Running on a heterogeneous configuration of one Nvidia GTX 480 GPU and one Intel Core i7 CPU, the parallel streaming decoder gains more than 8X speedup in runtime compared to the JasPer JPEG2000 software implementation.

关键词： Decoding Transform coding Streaming media parallel processing Acceleration Runtime Context

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Finite Element based Structural Optimization using Object-Oriented parallel programming

Finite Element based Structural Optimization using Object-Or...

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2nd International conference on parallel, Distributed, Grid and Cloud Computing for Engineering

作者： Masching, H. Fischer, M. Firl, M. Bletzinger, K. -U. Tech Univ Munich Chair Struct Anal D-80290 Munich Germany

ISBN: (纸本)9781905088423

This contribution presents an MPI-based parallel computational framework for simulation and gradient-based structural optimization of geometrically nonlinear and large-scale structural finite element models. The field of computational structural analysis is gaining more and more importance in product design. In order to obtain an impression about possible approaches of design improvement already in an early planning phase, an efficient optimization tool is desired that requires only few modelling effort. This demand can be satisfied by a combined analysis and optimization tool working on the same model. To this purpose the finite element based optimization method is an excellent approach, leading to the highest possible diversity within the optimization process.

关键词： structural optimization finite element based optimization parameter-free optimization parallel programming object-oriented programming finite element method C plus

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Multi-science applications with single codebase - GAMER - for massively parallel architectures

Multi-science applications with single codebase - GAMER - fo...

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2011 International conference for High Performance Computing, Networking, Storage and Analysis, SC11

作者： Shukla, Hemant Schive, Hsi-Yu Woo, Tak-Pong Chiueh, Tzihong Lawrence Berkeley National Laboratory 1 Cyclotron Road Berkeley United States Department of Physics National Taiwan University 106 Taipe Taiwan Department of Physics Soochow University 106 Taipei Taiwan

ISBN: (纸本)9781450307710

The growing need for power efficient extreme-scale high-performance computing (HPC) coupled with plateauing clock-speeds is driving the emergence of massively parallel compute architectures. Tens to many hundreds of cores are increasingly made available as compute units, either as the integral part of the main processor or as coprocessors designed for handling massively parallel workloads. In the case of many-core graphics processing units (GPUs) hundreds of SIMD cores primarily designed for image and video rendering are used for high-performance scientific computations. The new architectures typically offer ANSI standard programming models such as CUDA (NVIDIA) and OpenCL. However, the wide-ranging adoption of these parallel architectures is steeped in difficult learning curve and requires reengineering of existing applications that mostly leads to expensive and error prone code rewrites without prior guarantee and knowledge of any speedups. Broad range of complex scientific applications across many domains use common algorithms and techniques, such as adaptive mesh refinements (AMR), advanced hydrodynamics partial differential equation (PDE) solvers, Poisson-Gravity solvers etc, that have demonstrably performed highly efficiently on GPU based systems. Taking advantage of the commonalities, we use GPU-aware AMR code, GAMER [1], to examine the unique approach of solving multi-science problems in astrophysics, hydrodynamics and particle physics with single codebase. We demonstrate significant speedups in disparate class of scientific applications on 3 separate clusters, viz., Dirac, Laohu and Mole 8.5. By extensively reusing the extendable single codebase we mitigate the impediments of significant code rewrites. We also collect performance and energy consumption benchmark metrics on 50-nodes NVIDIA C2050 GPU and Intel 8-core Nehalem CPU on Dirac cluster at the National Energy Research Supercomputing Center (NERSC). In addition, we propose a strategy and framework for

关键词： Graphics processing unit

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massively parallel programming models Used as Hardware Description Languages: The OpenCL Case

Massively Parallel Programming Models Used as Hardware Descr...

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IEEE/ACM International conference on Computer-Aided Design (ICCAD)

作者： Owaida, Muhsen Bellas, Nikolaos Antonopoulos, Christos D. Daloukas, Konstantis Antoniadis, Charalambos Univ Thessaly Dept Comp & Commun Engn Volos Greece

ISBN: (纸本)9781457713989

The problem of automatically generating hardware modules from high level application representations has been at the forefront of EDA research during the last few years. In this paper, we introduce a methodology to automatically synthesize hardware accelerators from OpenCL applications. OpenCL is a recent industry supported standard for writing programs that execute on multicore platforms and accelerators such as GPUs. Our methodology maps OpenCL kernels into hardware accelerators, based on architectural templates that explicitly decouple computation from memory communication whenever this is possible. The templates can be tuned to provide a wide repertoire of accelerators that meet user performance requirements and FPGA device characteristics. Furthermore, a set of high-and low-level compiler optimizations is applied to generate optimized accelerators. Our experimental evaluation shows that the generated accelerators are tuned efficiently to match the applications memory access pattern and computational complexity, and to achieve user performance requirements. An important objective of our tool is to expand the FPGA development user base to software engineers, thereby expanding the scope of FPGAs beyond the realm of hardware design.

关键词： OpenCL FPGA Electronic Design Automation Reconfigurable Computing Embedded Systems

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Multi-science applications with single codebase — GAMER — For massively parallel architectures

Multi-science applications with single codebase — GAMER — ...

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Supercomputing conference

作者： Hemant Shukla Tak-Pong Woo Hsi-Yu Schive Tzihong Chiueh Lawrence Berkeley National laboratory Berkeley USA Department of Physics National Taiwan University Taipei Taiwan Department of Physics Soochow University Taipei Taiwan

关键词： Graphics processing unit Kernel Computational modeling Instruction sets Mathematical model Memory management

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Rekindling parallelism

Rekindling Parallelism

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IEEE International conference on Self-Adaptive and Self-Organizing Systems Workshops, SASOW

作者： Frederic Gruau Fabien Michel Laboratoire de Recherche en Informatique Université de Paris-Sud 11 France Laboratoire dInformatique de robotique et de Microélectronique de Montpellier CNRS Université Montpelher II France

Computing in parallel means performing computation simultaneously, this generates two distinct views: (1) Performance view A mean to accelerate computation using coarse grain parallelism. (2) Decentralization view A new way of programming by decentralizing massive fine grain parallelism. Researchers on massive parallel models study the programming expressiveness, i.e. new bio-inspired ways of computing such as artificial neural network or multi agent systems solving new kinds of problems, but are usually not directly concerned about high performance. In contrast, researchers on high performance tend to narrow the scope of parallel expressiveness by preserving the sequential model of computation and defining specific language constructs that can lead to parallel run-time performance for more classical parallel algorithms. We argue that parallelism will really fully blossom only when both views get unified through the achievement of a new generic computing model that, while enabling decentralized computation, also supports classical way of programming and incorporates the hardware constraints to provide parallel performance. We are working on such a generic model called self developing self mapping network. This paper first justifies the motivation for such a model, and then sketches the fundamental principles of this model.

关键词： Automata parallel processing programming Computational modeling Hardware Computer architecture Neurons

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TIDeFlow: A parallel Execution Model for High Performance Computing Programs

TIDeFlow: A Parallel Execution Model for High Performance Co...

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International conference on parallel Architecture and Compilation Techniques (PACT)

作者： Daniel Orozco University of Delaware USA

Summary form only given. The popularity of serial execution paradigms in the High Performance Computing (HPC) field greatly hinders the ability of computational scientists to develop and support massively parallel programs. Programmers are left with languages that are inadequate to express parallel constructs, being forced to take decisions that are not directly related to the programs they write. Computer architects are forced to support sequential memory semantics only because serial languages require them and operating system designers are forced to support slow synchronization operations. This poster addresses the development and execution of HPC programs in many-core architectures by introducing the Time Iterated Dependency Flow (TIDeFlow) execution model. In TIDeFlow, programmers specify the precedence relations between computations without dealing with implementation details related to synchronization or scheduling. TIDeFlow is a graph-based model inspired by dataflow: Computations in a program are expressed as actors whose dependencies are represented by arcs. TIDeFlow departs from other dataflow models (1) in that actors represent parallel loops as the basic building block of HPC programs, (2) in that arcs between actors represent dependencies of any kind (data, control or other) and (3) in that arc weights allow delaying tokens to provide support for pipelining. TIDeFlow is related to other dataflow models (an excellent survey can be found in [1]) in the idea of executing several computations into a single actor as in Macro Dataflow[2] and in allowing multiple, concurrent executions of the same actor through coloring of tokens as in Dynamic Dataflow[3]. The resulting TIDeFlow model expresses HPC programs as directed graphs with weighted nodes and weighted arcs that represent parallel loops and loop carried dependencies respectively. The model is useful to support task pipelining, task migration, and distributed control. An implementation of TIDeFlow was de

关键词： Computational modeling Runtime Scalability Computer architecture Synchronization Time domain analysis programming

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