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Energy-aware metrics for benchmarking heterogeneous systems

Energy-aware metrics for benchmarking heterogeneous systems Energy-Aware Metrics for Benchmarking Heterogeneous Systems Simon McIntosh-Smith, Terry Wilson simonm@cs.bris.ac.uk Dept. of Computer Science University of Bristol Woodland Road, Bristol, UK ´ Jon Crisp, Amaurys Avila Ibarra, Richard B. Sessions j.crisp@bris.ac.uk Department of Biochemistry University of Bristol Woodland Road, Bristol, UK tous. They will be used everywhere from HPC systems to the smartphones in our pockets. Today ™s GPUs are a signpost for what is to come, with hybrid CPU-GPU processors soon to become the norm. In HPC we will need to embrace this next major architectural paradigm in order to maximise the performance bene ts we receive from future systems. GPUs were the rst massively parallel processor technologies to be explored for use in HPC. The term œGeneralPurpose computation on Graphics Processing Units,  or œGPGPU , was rst coined by Mark Harris as early as 2002 [7]. By 2006 the rst fully programmable GPUs emerged, with Nvidia ™s introduction of CUDA and its G80 architecture. Since then the ‚oodgates have opened, with multiple competing GPU architectures from major vendors and even an emerging open standard for GPU programming, OpenCL [8]. ABSTRACT With the advent of heterogeneous computing systems consisting of multi-core CPUs and many-core GPUs, http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM SIGMETRICS Performance Evaluation Review Association for Computing Machinery

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