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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 2018 Research in Adaptive and Convergent Systems, RACS 2018
PublisherAssociation for Computing Machinery, Inc
Pages225-230
Number of pages6
ISBN (Electronic)9781450358859
DOIs
Publication statusPublished - 2018 Oct 9
Event2018 Conference Research in Adaptive and Convergent Systems, RACS 2018 - Honolulu, United States
Duration: 2018 Oct 92018 Oct 12

Publication series

NameProceedings of the 2018 Research in Adaptive and Convergent Systems, RACS 2018

Other

Other2018 Conference Research in Adaptive and Convergent Systems, RACS 2018
CountryUnited States
CityHonolulu
Period18-10-0918-10-12

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Control and Systems Engineering

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