Multi-Hybrid Accelerated Simulation by GPU and FPGA on Radiative Transfer Simulation in Astrophysics

DOI Web Site 被引用文献5件 参考文献15件 オープンアクセス
  • Kobayashi Ryohei
    Center for Computational Sciences, University of Tsukuba Degree Programs in Systems and Information Engineering, University of Tsukuba
  • Fujita Norihisa
    Center for Computational Sciences, University of Tsukuba Degree Programs in Systems and Information Engineering, University of Tsukuba
  • Yamaguchi Yoshiki
    Center for Computational Sciences, University of Tsukuba Degree Programs in Systems and Information Engineering, University of Tsukuba
  • Boku Taisuke
    Center for Computational Sciences, University of Tsukuba Degree Programs in Systems and Information Engineering, University of Tsukuba
  • Yoshikawa Kohji
    Center for Computational Sciences, University of Tsukuba Degree Programs in Pure and Applied Sciences, University of Tsukuba
  • Abe Makito
    Center for Computational Sciences, University of Tsukuba
  • Umemura Masayuki
    Center for Computational Sciences, University of Tsukuba Degree Programs in Pure and Applied Sciences, University of Tsukuba

抄録

<p>Field-programmable gate arrays (FPGAs) have garnered significant interest in research on high-performance computing because their computation and communication capabilities have drastically improved in recent years due to advances in semiconductor integration technologies that rely on Moore's Law. In addition to improving FPGA performance, toolchains for the development of FPGAs in OpenCL have been developed and offered by FPGA vendors that reduce the programming effort required. These improvements reveal the possibility of implementing a concept to enable on-the-fly offloading computation at which CPUs/GPUs perform poorly to FPGAs while performing low-latency data movement. We think that this concept is key to improving the performance of heterogeneous supercomputers using accelerators such as the GPU. In this paper, we propose a GPU-FPGA-accelerated simulation based on the concept and show our implementation with CUDA and OpenCL mixed programming for the proposed method. The results of experiments show that our proposed method can always achieve a better performance than GPU-based implementation and we believe that realizing GPU-FPGA-accelerated simulation is the most significant difference between our work and previous studies.</p>

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