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Title: Discrete event performance prediction of speculatively parallel temperature-accelerated dynamics

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.
Authors:
 [1] ;  [1] ;  [1] ;  [2] ;  [2] ;  [2] ;  [2]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States). Theoretical Division
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States). Computer, Computational & Statistical Sciences Division
Publication Date:
Report Number(s):
LA-UR-15-23844
Journal ID: ISSN 0037-5497
Grant/Contract Number:
AC52-06NA25396
Type:
Accepted Manuscript
Journal Name:
Simulation
Additional Journal Information:
Journal Volume: 92; Journal Issue: 12; Journal ID: ISSN 0037-5497
Publisher:
SAGE
Research Org:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Computer Science; Material Science
OSTI Identifier:
1335595