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GPU-based Monte Carlo simulation for the Gibbs ensemble

Authors:

Kamel Rushaidat,

Loren Schwiebert,

Jeffery J. PotoffAuthors Info & Claims

HPC '13: Proceedings of the High Performance Computing Symposium

Article No.: 10, Pages 1 - 8

Published: 07 April 2013 Publication History

Abstract

Scientists are interested in simulating large biomolecular systems for longer times to get more accurate results. However, longer running times mean more execution steps with large computation overhead. We present an implementation of Monte Carlo simulation for the Gibbs ensemble using Lennard-Jones atoms on GPUs. Moreover, we use massive multithreading to utilize the large number of cores that the GPU has and hide the parallel setup overhead, such as global memory access and kernel launch overhead. However, this process of porting the code to the GPU includes managing the available resources such as the number of registers, the amount of shared memory, number of threads per Streaming Multiprocessor, and global memory bandwidth used by each thread and kernel. To the best of our knowledge, no other similar work that uses the GPU on this scale has been done for Monte Carlo simulation of the Gibbs ensemble. The evaluation results show over 45 times speedup using a commodity GPU compared to running on a single processor core.

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Cited By

Li YSchwiebert LHailat EMick JPotoff J(2016)Improving performance of GPU code using novel features of the NVIDIA kepler architectureConcurrency and Computation: Practice & Experience10.1002/cpe.374428:13(3586-3605)Online publication date: 10-Sep-2016
https://dl.acm.org/doi/10.1002/cpe.3744
Rushaidat KSchwiebert LJackman BMick JPotoff JDe Rango FRisco Martín JCetinkaya D(2015)Efficient parallel cell list algorithms for Monte Carlo simulations (WIP)Proceedings of the Conference on Summer Computer Simulation10.5555/2874916.2874986(1-7)Online publication date: 26-Jul-2015
https://dl.acm.org/doi/10.5555/2874916.2874986

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Information & Contributors

Information

Published In

cover image ACM Other conferences

HPC '13: Proceedings of the High Performance Computing Symposium

April 2013

142 pages

ISBN:9781627480338

General Chairs:
Fang (Cherry) Liu
Georgia Institute of Technology
,
Karl Rupp
Technische Universitat Wie / Argonne National Laboratory
,
Program Chairs:
Rhonda Phillips
MIT Lincoln Laboratory
,
William I. Thacker
Winthrop University

Sponsors

SCS: Society for Modeling and Simulation International

In-Cooperation

SIGSIM: ACM Special Interest Group on Simulation and Modeling

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Society for Computer Simulation International

San Diego, CA, United States

Publication History

Published: 07 April 2013

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SpringSim '13

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SCS

SpringSim '13: 2013 Spring Simulation Multiconference

April 7 - 10, 2013

California, San Diego

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Cited By

Li YSchwiebert LHailat EMick JPotoff J(2016)Improving performance of GPU code using novel features of the NVIDIA kepler architectureConcurrency and Computation: Practice & Experience10.1002/cpe.374428:13(3586-3605)Online publication date: 10-Sep-2016
https://dl.acm.org/doi/10.1002/cpe.3744
Rushaidat KSchwiebert LJackman BMick JPotoff JDe Rango FRisco Martín JCetinkaya D(2015)Efficient parallel cell list algorithms for Monte Carlo simulations (WIP)Proceedings of the Conference on Summer Computer Simulation10.5555/2874916.2874986(1-7)Online publication date: 26-Jul-2015
https://dl.acm.org/doi/10.5555/2874916.2874986

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