Parallel Multicoordinate Descent Methods for Full Configuration Interaction

We develop a multithreaded parallel coordinate descent full configuration interaction algorithm (mCDFCI) for the electronic structure ground-state calculation in the configuration interaction framework. The FCI problem is reformulated as an unconstrained minimization problem and tackled by a modified block coordinate descent method with a deterministic compression strategy. mCDFCI is designed to prioritize determinants based on their importance, with block updates enabling efficient parallelization on shared-memory, multicore computing infrastructure. We demonstrate the efficiency of the algorithm by computing an accurate benchmark energy for the chromium dimer in the Ahlrichs SV basis (48e, 42o), which explicitly includes 2.07 × 10<sup>9</sup> variational determinants. We also provide the binding curve of the nitrogen dimer under the cc-pVQZ basis set (14e, 110o). Benchmarks show up to 79.3% parallel efficiency on 128 cores.

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Publication Details

Journal
Journal of Chemical Theory and Computation
Published
2025-02-28
DOI
https://doi.org/10.1021/acs.jctc.4c01530
Citations
2
Primary Topic
Tensor decomposition and applications
Type
article
Field-Weighted Citation Impact
3.07
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article

Parallel Multicoordinate Descent Methods for Full Configuration Interaction

Weiguo Gao, Y. Y. Zhang, Yingzhou Li
2 citations
Journal of Chemical Theory and Computation
Tensor decomposition and applications
3.07
article

Parallel Multicoordinate Descent Methods for Full Configuration Interaction

Weiguo Gao, Y. Y. Zhang, Yingzhou Li
article en
2 citations

Abstract

We develop a multithreaded parallel coordinate descent full configuration interaction algorithm (mCDFCI) for the electronic structure ground-state calculation in the configuration interaction framework. The FCI problem is reformulated as an unconstrained minimization problem and tackled by a modified block coordinate descent method with a deterministic compression strategy. mCDFCI is designed to prioritize determinants based on their importance, with block updates enabling efficient parallelization on shared-memory, multicore computing infrastructure. We demonstrate the efficiency of the algorithm by computing an accurate benchmark energy for the chromium dimer in the Ahlrichs SV basis (48e, 42o), which explicitly includes 2.07 × 10<sup>9</sup> variational determinants. We also provide the binding curve of the nitrogen dimer under the cc-pVQZ basis set (14e, 110o). Benchmarks show up to 79.3% parallel efficiency on 128 cores.

Journal of Chemical Theory and ComputationVol. 21(5)
Fudan University (CN), Applied Mathematics (United States) (US)
Openalex Percentile: Top 10%
Tensor decomposition and applications
3.07
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