research-article

Acceleration of Monte-Carlo simulation on high performance computing platforms

Authors:

Cheng-Yueh Liu,

Shih-Hao HungAuthors Info & Claims

RACS '18: Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems

Pages 225 - 230

https://doi.org/10.1145/3264746.3264765

Published: 09 October 2018 Publication History

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.

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Cited By

Zhang JChen ZZhang L(2022)ASMAMC: A Specific Microprocessor Architecture for Monte Carlo Method2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys)10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00148(922-927)Online publication date: Dec-2022
https://doi.org/10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00148
Fu SLi TZhang JMa SLiu S(2021)MiniMCTAD: Minimalist Monte Carlo Transport Architecture Design2021 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00016(1-10)Online publication date: Sep-2021
https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00016

Index Terms

Acceleration of Monte-Carlo simulation on high performance computing platforms

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cover image ACM Conferences

RACS '18: Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems

October 2018

355 pages

ISBN:9781450358859

DOI:10.1145/3264746

Conference Chair:
Chih-Cheng Hung
Kennesaw State University
,
General Chair:
Lamjed Ben Said
University of Tunis, Tunisia

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGAPP: ACM Special Interest Group on Applied Computing

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KISM: Korean Institute of Smart Media

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Published: 09 October 2018

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Ministry of Science and Technology of Taiwan

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RACS '18

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SIGAPP

RACS '18: International Conference on Research in Adaptive and Convergent Systems

October 9 - 12, 2018

Hawaii, Honolulu

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Cited By

Zhang JChen ZZhang L(2022)ASMAMC: A Specific Microprocessor Architecture for Monte Carlo Method2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys)10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00148(922-927)Online publication date: Dec-2022
https://doi.org/10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00148
Fu SLi TZhang JMa SLiu S(2021)MiniMCTAD: Minimalist Monte Carlo Transport Architecture Design2021 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00016(1-10)Online publication date: Sep-2021
https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00016

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