Discrete event performance prediction of speculatively parallel temperature-accelerated dynamics
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States). Theoretical Division
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States). Computer, Computational & Statistical Sciences Division
Due to its unrivaled ability to predict the dynamical evolution of interacting atoms, molecular dynamics (MD) is a widely used computational method in theoretical chemistry, physics, biology, and engineering. Despite its success, MD is only capable of modeling time scales within several orders of magnitude of thermal vibrations, leaving out many important phenomena that occur at slower rates. The Temperature Accelerated Dynamics (TAD) method overcomes this limitation by thermally accelerating the state-to-state evolution captured by MD. Due to the algorithmically complex nature of the serial TAD procedure, implementations have yet to improve performance by parallelizing the concurrent exploration of multiple states. Here we utilize a discrete event-based application simulator to introduce and explore a new Speculatively Parallel TAD (SpecTAD) method. We investigate the SpecTAD algorithm, without a full-scale implementation, by constructing an application simulator proxy (SpecTADSim). Finally, following this method, we discover that a nontrivial relationship exists between the optimal SpecTAD parameter set and the number of CPU cores available at run-time. Furthermore, we find that a majority of the available SpecTAD boost can be achieved within an existing TAD application using relatively simple algorithm modifications.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE
- Grant/Contract Number:
- AC52-06NA25396
- OSTI ID:
- 1335595
- Report Number(s):
- LA-UR-15-23844
- Journal Information:
- Simulation, Vol. 92, Issue 12; ISSN 0037-5497
- Publisher:
- SAGECopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
Algorithmic developments of the kinetic activation-relaxation technique: Accessing long-time kinetics of larger and more complex systems
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journal | October 2017 |
Speculation and replication in temperature accelerated dynamics
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journal | February 2018 |
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