Acceleration of Monte-Carlo simulation on high performance computing platforms

Pei Jen Wang, Cheng Yueh Liu, Chia Heng Tu, Chen Pang Lee, Shih Hao Hung

研究成果: Conference contribution

摘要

Monte Carlo methods are often used to solve computational problems with randomness. The random sampling helps avoid the deterministic results, but it requires intensive computations to obtain the results. Several attempts have been made to boost the performance of the Monte Carlo based algorithms by taking advantage of the parallel computers. In this paper, we use the photonic simulation application, MCML, as a case study to 1) parallelize the Monte Carlo method with OpenMP and vectorization, 2) compare the parallelization techniques, and 3) evaluate the parallelized programs on the platforms with the Xeon Phi processor. In particular, the OpenMP version incorporates the vectorization technique that utilizes the AVX-512 vector instructions on the Xeon Phi processor. Our experimental results show that the OpenMP code achieves up to 345x speedup on the Xeon Phi processor, compared with the original code runs on the Xeon E5 processor.

原文English
主出版物標題Proceedings of the 2018 Research in Adaptive and Convergent Systems, RACS 2018
發行者Association for Computing Machinery, Inc
頁面225-230
頁數6
ISBN(電子)9781450358859
DOIs
出版狀態Published - 2018 十月 9
事件2018 Conference Research in Adaptive and Convergent Systems, RACS 2018 - Honolulu, United States
持續時間: 2018 十月 92018 十月 12

出版系列

名字Proceedings of the 2018 Research in Adaptive and Convergent Systems, RACS 2018

Other

Other2018 Conference Research in Adaptive and Convergent Systems, RACS 2018
國家United States
城市Honolulu
期間18-10-0918-10-12

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Control and Systems Engineering

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