Resource allocation for task-level speculative scientific applications: A proof of concept using Parallel Trajectory Splicing
The constant increase in parallelism available on large-scale distributed computers poses major scalability challenges to many scientific applications. A common strategy to improve scalability is to express algorithms in terms of independent tasks that can be executed concurrently on a runtime syste...
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| Published in: | Parallel computing Vol. 112; p. 102936 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.09.2022
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| ISSN: | 0167-8191, 1872-7336 |
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| Abstract | The constant increase in parallelism available on large-scale distributed computers poses major scalability challenges to many scientific applications. A common strategy to improve scalability is to express algorithms in terms of independent tasks that can be executed concurrently on a runtime system. In this manuscript, we consider a generalization of this approach where task-level speculation is allowed. In this context, a probability is attached to each task which corresponds to the likelihood that the output of the speculative task will be consumed as part of the larger calculation. We consider the problem of optimal resource allocation to each of the possible tasks so as to maximize the total expected computational throughput. The power of this approach is demonstrated by analyzing its application to Parallel Trajectory Splicing, a massively-parallel long-time-dynamics method for atomistic simulations.
•Efficiently utilizing large-scale HPC machines has become increasingly difficult.•Speculative task-based execution serves as a means of increasing parallelism.•Performance relies on optimal resource allocation among speculative tasks.•Improved scaling of traditionally scale-limited applications; Molecular Dynamics. |
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| AbstractList | The constant increase in parallelism available on large-scale distributed computers poses major scalability challenges to many scientific applications. A common strategy to improve scalability is to express algorithms in terms of independent tasks that can be executed concurrently on a runtime system. In this manuscript, we consider a generalization of this approach where task-level speculation is allowed. In this context, a probability is attached to each task which corresponds to the likelihood that the output of the speculative task will be consumed as part of the larger calculation. We consider the problem of optimal resource allocation to each of the possible tasks so as to maximize the total expected computational throughput. The power of this approach is demonstrated by analyzing its application to Parallel Trajectory Splicing, a massively-parallel long-time-dynamics method for atomistic simulations.
•Efficiently utilizing large-scale HPC machines has become increasingly difficult.•Speculative task-based execution serves as a means of increasing parallelism.•Performance relies on optimal resource allocation among speculative tasks.•Improved scaling of traditionally scale-limited applications; Molecular Dynamics. |
| ArticleNumber | 102936 |
| Author | Perez, Danny Garmon, Andrew Ramakrishnaiah, Vinay |
| Author_xml | – sequence: 1 givenname: Andrew orcidid: 0000-0002-5478-5160 surname: Garmon fullname: Garmon, Andrew email: agarmon@lanl.gov organization: Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA – sequence: 2 givenname: Vinay surname: Ramakrishnaiah fullname: Ramakrishnaiah, Vinay email: vinayr@lanl.gov organization: Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA – sequence: 3 givenname: Danny surname: Perez fullname: Perez, Danny email: danny_perez@lanl.gov organization: Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA |
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| Cites_doi | 10.1109/JSYST.2017.2722476 10.1016/0021-9991(75)90060-1 10.1021/acs.jctc.5b00916 10.7717/peerj-cs.183 10.1016/S0167-8191(02)00216-8 10.1145/2742347 10.1145/224538.224564 10.1098/rsta.2019.0056 10.1137/18M1177792 10.1177/1094342007078442 10.1016/0377-2217(93)90177-O 10.1142/S0129626411000060 10.1145/2821505 10.1557/jmr.2017.456 10.1063/5.0014475 10.1287/trsc.36.2.231.561 10.1063/1.1415500 10.1177/0037549716674806 10.1016/j.cpc.2020.107262 10.1103/PhysRevLett.78.3908 10.1109/TCC.2015.2481400 10.1145/1465482.1465560 10.1145/165854.165874 10.1147/JRD.2011.2109230 10.1006/jcph.1995.1039 10.1145/2699715 10.1016/S1574-1400(09)00504-0 10.1016/j.commatsci.2014.12.011 10.1016/S0377-2217(99)00287-8 10.1063/1.481576 10.1088/1361-651X/aba511 10.1145/2400682.2400698 |
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| Keywords | Task-based programming Discrete event simulation Accelerated molecular dynamics Speculation Resource allocation |
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