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Cited by 417

The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.

Vectorization of the general Monte Carlo classical trajectory program VENUS

Hu, Xiche, Hase, William L., Pirraglia, Tony·1991·Journal of Computational Chemistry

Abstract

The general chemical dynamics computer program VENUS is used to perform classical trajectory simulations for large polyatomic systems, with many atoms and complicated potential energy functions. To simulate an ensemble of many trajectories requires a large amount of CPU time. Since each trajectory is independent, it is possible to parallel process a large set of trajectories instead of processing the trajectories by the conventional sequential approach. This enhances the vectorizability of the VENUS program, since the integration of Hamilton's equations of motion and the gradient evaluation, which comprise 97.8% of the CPU, can each be parallel processed. In this article, the vectorization and ensuing optimization of VENUS on the CRAY‐YMP and IBM‐3090 are presented in terms of both global strategies and technical details. A switching algorithm is designed to enhance the vector performance and to minimize the memory storage. A performance of 140 MFLOPS and a vector/scalar execution rate ratio of 10.6 are observed when this new version of VENUS is used to study the association of CH 3 with the H(Ar)12 cluster on the CRAY‐YMP.

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Metadata

Authors
Hu, X., Hase, W. L., & Pirraglia, T.
Venue
Journal of Computational Chemistry
Publisher
Wiley
Published
1991-10
Volume
12
Issue
8
Pages
1014-1024
ISSN
  • 0192-8651 (ISSN-L)
  • 1096-987X
OpenAlex
W2057820912
Cited by
417
References
11
FWCI
4.11
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Vectorization of the general Monte Carlo classical trajectory program VENUS. https://app.cochat.ai/papers/10.1002/jcc.540120814
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Hu, X., Hase, W. L., & Pirraglia, T. (1991). Vectorization of the general Monte Carlo classical trajectory program VENUS. Journal of Computational Chemistry. https://doi.org/10.1002/jcc.540120814

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