5688
Thomas Grosser, Alexandros Gremm, Sebastian Veith, Gerald Heim, Wolfgang Rosenstiel, Victor Medeiros, Manoel Eusebio de Lima
Large heterogeneous data centers of today lack methods to appraise the best fitting solutions regarding, among others, hardware acquisition cost, development time, and performance. Especially resource intensive applications benefit from increased data center utilization to leverage heterogeneous resources and accelerators. In this paper, we implement various methods to accelerate a seismic modeling application, which is […]
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W. B. Langdon
A Single Instruction Multiple Thread CUDA interpreter provides SIMD like parallel evaluation of the whole GP population of 1/4 million RPN expressions on graphics cards and nVidia Tesla T10P. Using sub-machine code GP a sustain peak performance of 212 billion GP operations per second (3300 speed up) and an average of 4.5 peta GP ops […]
Jee W. Choi, Amik Singh, Richard W. Vuduc
We present a performance model-driven framework for automated performance tuning (autotuning) of sparse matrix-vector multiply (SpMV) on systems accelerated by graphics processing units (GPU). Our study consists of two parts. First, we describe several carefully hand-tuned SpMV implementations for GPUs, identifying key GPU-specific performance limitations, enhancements, and tuning opportunities. These implementations, which include variants on […]
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William B. Langdon
A Single Instruction Multiple Thread CUDA interpreter provides SIMD like parallel evaluation of the whole GP population of quarter of a million RPN expressions on graphics cards and nVidia Tesla T10P. Using sub-machine code GP a sustain peak performance of 212 billion GP operations per second (3300 speed up) and an average of 4.5 peta […]
Jack Dongarra, Shirley Moore, Gregory Peterson, Stanimire Tomov
Computational Fluid Dynamics (CFD) is an active field of research where the development of faster and more accurate methods is linked to the continuous demand for ever higher computational power. And indeed, for at least two decades, high-performance computing (HPC) programmers have taken for granted that each successive generation of microprocessors would, either immediately or […]
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Sylvain Collange, David Defour, Arnaud Tisserand
GPUs are now considered as serious challengers for high-performance computing solutions. They have power consumptions up to 300 W. This may lead to power supply and thermal dissipation problems in computing centers. In this article we investigate, using measurements, how and where modern GPUs are using energy during various computations in a CUDA environment.
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Mingliang Wang, Hector Klie, Manish Parashar, Hari Sudan
Current many-core GPUs have enormous processing power, and unlocking this power for general-purpose computing is very attractive due to their low cost and efficient power utilization. However, the fine-grained parallelism and the stream-programming model supported by these GPUs require a paradigm shift, especially for algorithm designers. In this paper we present the design of a […]

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