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An efficient, model-based CPU-GPU heterogeneous FFT library

Y. Ogata, T. Endo, N. Maruyama, S. Matsuoka
Tokyo Inst. of Technol., Tokyo
Parallel and Distributed Processing, 2008. IPDPS 2008. IEEE International Symposium on In Parallel and Distributed Processing, 2008. IPDPS 2008. IEEE International Symposium on (2008), pp. 1-10.

@conference{ogata2008efficient,

   title={An efficient, model-based CPU-GPU heterogeneous FFT library},

   author={Ogata, Y. and Endo, T. and Maruyama, N. and Matsuoka, S.},

   booktitle={Parallel and Distributed Processing, 2008. IPDPS 2008. IEEE International Symposium on},

   pages={1–10},

   year={2008},

   organization={IEEE}

}

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General-purpose computing on graphics processing units (GPGPU) is becoming popular in HPC because of its high peak performance. However, in spite of the potential performance improvements as well as recent promising results in scientific computing applications, its real performance is not necessarily higher than that of the current high-performance CPUs, especially with recent trends towards increasing the number of cores on a single die. This is because the GPU performance can be severely limited by such restrictions as memory size and bandwidth and programming using graphics-specific APIs. To overcome this problem, we propose a model-based, adaptive library for 2D FFT that automatically achieves optimal performance using available heterogeneous CPU-GPU computing resources. To find optimal load distribution ratios between CPUs and GPUs, we construct a performance model that captures the respective contributions of CPU vs. GPU, and predicts the total execution time of 2D-FFT for arbitrary problem sizes and load distribution. The performance model divides the FFT computation into several small sub steps, and predicts the execution time of each step using profiling results. Preliminary evaluation with our prototype shows that the performance model can predict the execution time of problem sizes that are 16 times as large as the profile runs with less than 20% error, and that the predicted optimal load distribution ratios have less than 1% error. We show that the resulting performance improvement using both CPUs and GPUs can be as high as 50% compared to using either a CPU core or a GPU.
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