Performance of Molecular Dynamics Acceleration Strategies on Composable Cyberinfrastructure
Authors: 
Richard Lawrence, Dhruva K. Chakravorty, Lisa M. Perez, Honggao Liu, Zhenhua He, Wesley Brashear, Joshua Winchell, James X. Mao, Chun-Yaung LuAuthors Info & Claims
Richard Lawrence, Dhruva K. Chakravorty, Lisa M. Perez, Honggao Liu, Zhenhua He, Wesley Brashear, Joshua Winchell, James X. Mao, Chun-Yaung LuAuthors Info & ClaimsArticle No.: 46, Pages 1 - 5
Abstract
Modern powerful accelerators and composable infrastructures put our simulation frameworks to the test. We will show that the acceleration of a simulation framework is absolutely critical for good performance and scaling. Building on our previous work using research software as a benchmark for computing clusters, the High Performance Research Computing Group (HPRC)1 compares the Kokkos and GPU acceleration packages of LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator) molecular dynamics software with NVIDIA H100 and Intel Data Center GPU Max 1100 accelerators on the composable ACES cyber-infrastructure at Texas A&M University. We observe different computational and communication patterns emerge from the different codes, which in turn result in different performance and scaling characteristics across these accelerators. We observe an opportunity for growth in the synergy between Intel’s oneAPI toolchain and the Kokkos framework to enable effective scaling of molecular dynamics simulations on composable infrastructure.
References
[1]
W. Michael Brown, Peng Wang, Steven J. Plimpton, and Arnold N. Tharrington. 2011. Implementing molecular dynamics on hybrid high performance computers – short range forces. Computer Physics Communications 182, 4 (2011), 898–911. https://doi.org/10.1016/j.cpc.2010.12.021
[2]
Zhenhua He, Aditi Saluja, Richard Lawrence, Dhruva K. Chakravorty, Francis Dang, Lisa M. Perez, and Honggao Liu. 2023. Performance of Distributed Deep Learning Workloads on a Composable Cyberinfrastructure. In Practice and Experience in Advanced Research Computing (Portland, OR, USA) (PEARC ’23). Association for Computing Machinery, New York, NY, USA, 12 pages. https://doi.org/10.1145/3569951.3603632
[3]
Intel. 2024. Data Parallel C++: the oneAPI Implementation of SYCL*. Retrieved April 2024 from https://www.intel.com/content/www/us/en/developer/tools/oneapi/data-parallel-c-plus-plus.html
[4]
Druva Gunda Kumar, Zhenhua He, Lujun Zhai, Dhruva K. Chakravorty, Francis Dang, Lisa M. Perez, and Honggao Liu. 2024. Accelerator Performance for AI/ML Workloads on Composable Infrastructure. In Practice and Experience in Advanced Research Computing(PEARC ’24). Association for Computing Machinery, New York, NY, USA, 12 pages.
[5]
LAMMPS. 2024. LAMMPS Source Code. https://github.com/lammps/lammps "develop (Wed Feb 21)".
[6]
Richard Lawrence. 2024. Supplemental Documents for PEARC24 Proceedings paper 139: Performance of Molecular Dynamics Acceleration Strategies on Composable Cyberinfrastructure. https://github.com/rarensu/pearc24-LAMMPS-supplement
[7]
Richard E Lawrence, Dhruva K Chakravorty, Zhenhua He, Francis Dang, Lisa M Perez, Wesley A Brashear, and Honggao Liu. 2023. Developing Synthetic Applications Benchmarks on Composable Cyberinfrastructure: A Study of Scaling Molecular Dynamics Applications on GPUs. In Practice and Experience in Advanced Research Computing (Portland, OR, USA) (PEARC ’23). Association for Computing Machinery, New York, NY, USA, 216–220. https://doi.org/10.1145/3569951.3597556
[8]
Sambit Mishra, Freddie Witherden, Dhruva K. Chakravorty, Lisa M. Perez, and Francis Dang. 2023. Scaling Study of Flow Simulations on Composable Cyberinfrastructure. In Practice and Experience in Advanced Research Computing (Portland, OR, USA) (PEARC ’23). Association for Computing Machinery, New York, NY, USA, 6 pages. https://doi.org/10.1145/3569951.3597565
[9]
Sambit Mishra, Freddie Witherden, Dhruva K. Chakravorty, Lisa M. Perez, and Francis Dang. 2024. Memory Bandwidth Performance across Accelerators. In Practice and Experience in Advanced Research Computing(PEARC ’24). Association for Computing Machinery, New York, NY, USA, 6 pages.
[10]
Stan Gerald Moore. 2019. LAMMPS KOKKOS Package: The quest for performance portable MD. (8 2019). https://www.osti.gov/biblio/1641575
[11]
Abhinand Nasari, Hieu Le, Richard Lawrence, Zhenhua He, Xin Yang, Mario Krell, Alex Tsyplikhin, Mahidhar Tatineni, Tim Cockerill, Lisa Perez, Dhruva Chakravorty, and Honggao Liu. 2022. Benchmarking the Performance of Accelerators on National Cyberinfrastructure Resources for Artificial Intelligence / Machine Learning Workloads. In Practice and Experience in Advanced Research Computing (Boston, MA, USA) (PEARC ’22). Association for Computing Machinery, New York, NY, USA, Article 19, 9 pages. https://doi.org/10.1145/3491418.3530772
[12]
NVIDIA. 2023. NVIDIA Container Registry: LAMMPS. https://catalog.ngc.nvidia.com/orgs/hpc/containers/lammps "tag:patch_15Jun2023".
[13]
Steve Plimpton, Aidan Thompson, Stan Moore, Axel Kohlmeyer, and Richard Berger. 2016. LAMMPS Benchmarks. Retrieved April 2023 from https://www.lammps.org/bench.html
[14]
Steve Plimpton, Aidan Thompson, Stan Moore, Axel Kohlmeyer, and Richard Berger. 2024. LAMMPS Accelerator Packages Comparison. Retrieved April 2024 from https://docs.lammps.org/Speed_compare.html
[15]
Aidan P. Thompson, H. Metin Aktulga, Richard Berger, Dan S. Bolintineanu, W. Michael Brown, Paul S. Crozier, Pieter J. in ’t Veld, Axel Kohlmeyer, Stan G. Moore, Trung Dac Nguyen, Ray Shan, Mark J. Stevens, Julien Tranchida, Christian Trott, and Steven J. Plimpton. 2022. LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. Computer Physics Communications 271 (2022), 108171. https://doi.org/10.1016/j.cpc.2021.108171
[16]
Christian R. Trott, Damien Lebrun-Grandié, Daniel Arndt, Jan Ciesko, Vinh Dang, Nathan Ellingwood, Rahulkumar Gayatri, Evan Harvey, Daisy S. Hollman, Dan Ibanez, Nevin Liber, Jonathan Madsen, Jeff Miles, David Poliakoff, Amy Powell, Sivasankaran Rajamanickam, Mikael Simberg, Dan Sunderland, Bruno Turcksin, and Jeremiah Wilke. 2022. Kokkos 3: Programming Model Extensions for the Exascale Era. IEEE Transactions on Parallel and Distributed Systems 33, 4 (2022), 805–817. https://doi.org/10.1109/TPDS.2021.3097283
Index Terms
- Performance of Molecular Dynamics Acceleration Strategies on Composable Cyberinfrastructure
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July 2024
608 pages
ISBN:9798400704192
DOI:10.1145/3626203
Copyright © 2024 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
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Published: 17 July 2024
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PEARC '24: Practice and Experience in Advanced Research Computing
July 21 - 25, 2024
RI, Providence, USA
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