Massively Parallel Computational Chemistry with the Super Instruction Architecture and ACES4

The task of developing high-performing parallel software must be made easier and more cost-effective in order to fully exploit existing and emerging large-scale computer systems for the advancement of science. The Super Instruction Architecture (SIA) is a parallel programming platform geared toward applications that need to manage large amounts of data stored in potentially sparse multidimensional arrays during calculations. The SIA platform was originally designed for the quantum chemistry software package ACESIII. More recently, the SIA was reimplemented to overcome the limitations in the original ACESIII program. It has now been successfully employed in the new ACES4 quantum chemistry software package. This paper describes the SIA and ACES4 and illustrates their capabilities with some difficult quantum chemistry open-shell coupled-cluster benchmark calculations.

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Publication Details

Journal
The Journal of Physical Chemistry A
Published
2024-08-23
DOI
https://doi.org/10.1021/acs.jpca.4c04146
Citations
2
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.23

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article

Massively Parallel Computational Chemistry with the Super Instruction Architecture and ACES4

Jason N. Byrd, Victor F. Lotrich, Beverly A. Sanders
2 citations
The Journal of Physical Chemistry A
Machine Learning in Materials Science
0.23
article

Massively Parallel Computational Chemistry with the Super Instruction Architecture and ACES4

Jason N. Byrd, Victor F. Lotrich, Beverly A. Sanders
article en
2 citations

Abstract

The task of developing high-performing parallel software must be made easier and more cost-effective in order to fully exploit existing and emerging large-scale computer systems for the advancement of science. The Super Instruction Architecture (SIA) is a parallel programming platform geared toward applications that need to manage large amounts of data stored in potentially sparse multidimensional arrays during calculations. The SIA platform was originally designed for the quantum chemistry software package ACESIII. More recently, the SIA was reimplemented to overcome the limitations in the original ACESIII program. It has now been successfully employed in the new ACES4 quantum chemistry software package. This paper describes the SIA and ACES4 and illustrates their capabilities with some difficult quantum chemistry open-shell coupled-cluster benchmark calculations.

The Journal of Physical Chemistry AVol. 128(35)
Ensco (United States) (US), University of Florida (US)
Air Force Research Laboratory
Openalex Percentile: Top 52%
Machine Learning in Materials Science
0.23
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