A High-Performance Design for Hierarchical Parallelism in the QMCPACK Monte Carlo code

Ye Luo; Peter Doak; Paul Kent

doi:10.1109/HiPar56574.2022.00008

A High-Performance Design for Hierarchical Parallelism in the QMCPACK Monte Carlo code

Ye Luo, Peter Doak, Paul Kent

Computational Chemistry and Nanomaterial

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

We introduce a new high-performance design for parallelism within the Quantum Monte Carlo code QMCPACK. We demonstrate that the new design is better able to exploit the hierarchical parallelism of heterogeneous architectures compared to the previous GPU implementation. The new version is able to achieve higher GPU occupancy via the new concept of crowds of Monte Carlo walkers, and by enabling more host CPU threads to effectively offload to the GPU. The higher performance is expected to be achieved independent of the underlying hardware, significantly improving developer productivity and reducing code maintenance costs. Scientific productivity is also improved with full support for fallback to CPU execution when GPU implementations are not available or CPU execution is more optimal.

Original language	English
Title of host publication	Proceedings of HiPar 2022
Subtitle of host publication	3rd Workshop on Hierarchical Parallelism for Exascale Computing, Held in conjunction with SC 2022: The International Conference for High Performance Computing, Networking, Storage and Analysis
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	22-27
Number of pages	6
ISBN (Electronic)	9781665463454
DOIs	https://doi.org/10.1109/HiPar56574.2022.00008
State	Published - 2022
Event	3rd IEEE/ACM International Workshop on Hierarchical Parallelism for Exascale Computing, HiPar 2022 - Dallas, United States Duration: Nov 13 2022 → Nov 18 2022

Publication series

Name	Proceedings of HiPar 2022: 3rd Workshop on Hierarchical Parallelism for Exascale Computing, Held in conjunction with SC 2022: The International Conference for High Performance Computing, Networking, Storage and Analysis

Conference

Conference	3rd IEEE/ACM International Workshop on Hierarchical Parallelism for Exascale Computing, HiPar 2022
Country/Territory	United States
City	Dallas
Period	11/13/22 → 11/18/22

Funding

ACKNOWLEDGMENT This research was supported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration. This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725. This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. This manuscript has been authored in part by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Funders	Funder number
DOE Public Access Plan
U.S. Department of Energy
Office of Science	DE-AC05-00OR22725, DE-AC02-06CH11357
National Nuclear Security Administration

Keywords

GPUs
Heterogeneous computing
Monte Carlo

Access to Document

10.1109/HiPar56574.2022.00008

Cite this

Luo, Y., Doak, P., & Kent, P. (2022). A High-Performance Design for Hierarchical Parallelism in the QMCPACK Monte Carlo code. In Proceedings of HiPar 2022: 3rd Workshop on Hierarchical Parallelism for Exascale Computing, Held in conjunction with SC 2022: The International Conference for High Performance Computing, Networking, Storage and Analysis (pp. 22-27). (Proceedings of HiPar 2022: 3rd Workshop on Hierarchical Parallelism for Exascale Computing, Held in conjunction with SC 2022: The International Conference for High Performance Computing, Networking, Storage and Analysis). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/HiPar56574.2022.00008

Luo, Ye ; Doak, Peter ; Kent, Paul. / A High-Performance Design for Hierarchical Parallelism in the QMCPACK Monte Carlo code. Proceedings of HiPar 2022: 3rd Workshop on Hierarchical Parallelism for Exascale Computing, Held in conjunction with SC 2022: The International Conference for High Performance Computing, Networking, Storage and Analysis. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 22-27 (Proceedings of HiPar 2022: 3rd Workshop on Hierarchical Parallelism for Exascale Computing, Held in conjunction with SC 2022: The International Conference for High Performance Computing, Networking, Storage and Analysis).

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abstract = "We introduce a new high-performance design for parallelism within the Quantum Monte Carlo code QMCPACK. We demonstrate that the new design is better able to exploit the hierarchical parallelism of heterogeneous architectures compared to the previous GPU implementation. The new version is able to achieve higher GPU occupancy via the new concept of crowds of Monte Carlo walkers, and by enabling more host CPU threads to effectively offload to the GPU. The higher performance is expected to be achieved independent of the underlying hardware, significantly improving developer productivity and reducing code maintenance costs. Scientific productivity is also improved with full support for fallback to CPU execution when GPU implementations are not available or CPU execution is more optimal.",

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Luo, Y, Doak, P & Kent, P 2022, A High-Performance Design for Hierarchical Parallelism in the QMCPACK Monte Carlo code. in Proceedings of HiPar 2022: 3rd Workshop on Hierarchical Parallelism for Exascale Computing, Held in conjunction with SC 2022: The International Conference for High Performance Computing, Networking, Storage and Analysis. Proceedings of HiPar 2022: 3rd Workshop on Hierarchical Parallelism for Exascale Computing, Held in conjunction with SC 2022: The International Conference for High Performance Computing, Networking, Storage and Analysis, Institute of Electrical and Electronics Engineers Inc., pp. 22-27, 3rd IEEE/ACM International Workshop on Hierarchical Parallelism for Exascale Computing, HiPar 2022, Dallas, United States, 11/13/22. https://doi.org/10.1109/HiPar56574.2022.00008

A High-Performance Design for Hierarchical Parallelism in the QMCPACK Monte Carlo code. / Luo, Ye; Doak, Peter ; Kent, Paul.
Proceedings of HiPar 2022: 3rd Workshop on Hierarchical Parallelism for Exascale Computing, Held in conjunction with SC 2022: The International Conference for High Performance Computing, Networking, Storage and Analysis. Institute of Electrical and Electronics Engineers Inc., 2022. p. 22-27 (Proceedings of HiPar 2022: 3rd Workshop on Hierarchical Parallelism for Exascale Computing, Held in conjunction with SC 2022: The International Conference for High Performance Computing, Networking, Storage and Analysis).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Luo Y, Doak P , Kent P. A High-Performance Design for Hierarchical Parallelism in the QMCPACK Monte Carlo code. In Proceedings of HiPar 2022: 3rd Workshop on Hierarchical Parallelism for Exascale Computing, Held in conjunction with SC 2022: The International Conference for High Performance Computing, Networking, Storage and Analysis. Institute of Electrical and Electronics Engineers Inc. 2022. p. 22-27. (Proceedings of HiPar 2022: 3rd Workshop on Hierarchical Parallelism for Exascale Computing, Held in conjunction with SC 2022: The International Conference for High Performance Computing, Networking, Storage and Analysis). doi: 10.1109/HiPar56574.2022.00008

A High-Performance Design for Hierarchical Parallelism in the QMCPACK Monte Carlo code

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