Implementation of McMurchie–Davidson Algorithm for Gaussian AO Integrals Suited for SIMD Processors

We report an implementation of the McMurchie-Davidson evaluation scheme for 1- and 2-particle Gaussian AO integrals designed for processors with Single Instruction Multiple Data (SIMD) instruction sets. Like in our recent MD implementation for graphical processing units (GPUs) [Asadchev, A.; Valeev, E. F.. <i>J. Chem. Phys.</i> 2024, 160, 244109.], variable-sized batches of shellsets of integrals are evaluated at a time. By optimizing for the floating point instruction throughput rather than minimizing the number of operations, this approach achieves up to 50% of the theoretical hardware peak FP64 performance for many common SIMD-equipped platforms (AVX2, AVX512, NEON), which translates to speedups of up to 30 over the state-of-the-art one-shellset-at-a-time implementation of Obara-Saika-type schemes in Libint for a variety of primitive and contracted integrals. As with our previous work, we rely on the standard C++ programming language─such as the std::simd standard library feature to be included in the 2026 ISO C++ standard─without any explicit code generation to keep the code base small and portable. The implementation is part of the open source LibintX library freely available at https://github.com/ValeevGroup/libintx.

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Publication Details

Journal
The Journal of Physical Chemistry A
Published
2025-10-13
DOI
https://doi.org/10.1021/acs.jpca.5c04136
Primary Topic
Blind Source Separation Techniques
Type
article
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article

Implementation of McMurchie–Davidson Algorithm for Gaussian AO Integrals Suited for SIMD Processors

Andrey Asadchev, Edward F. Valeev
The Journal of Physical Chemistry A
Blind Source Separation Techniques
article

Implementation of McMurchie–Davidson Algorithm for Gaussian AO Integrals Suited for SIMD Processors

Andrey Asadchev, Edward F. Valeev
article en

Abstract

We report an implementation of the McMurchie-Davidson evaluation scheme for 1- and 2-particle Gaussian AO integrals designed for processors with Single Instruction Multiple Data (SIMD) instruction sets. Like in our recent MD implementation for graphical processing units (GPUs) [Asadchev, A.; Valeev, E. F.. <i>J. Chem. Phys.</i> 2024, 160, 244109.], variable-sized batches of shellsets of integrals are evaluated at a time. By optimizing for the floating point instruction throughput rather than minimizing the number of operations, this approach achieves up to 50% of the theoretical hardware peak FP64 performance for many common SIMD-equipped platforms (AVX2, AVX512, NEON), which translates to speedups of up to 30 over the state-of-the-art one-shellset-at-a-time implementation of Obara-Saika-type schemes in Libint for a variety of primitive and contracted integrals. As with our previous work, we rely on the standard C++ programming language─such as the std::simd standard library feature to be included in the 2026 ISO C++ standard─without any explicit code generation to keep the code base small and portable. The implementation is part of the open source LibintX library freely available at https://github.com/ValeevGroup/libintx.

The Journal of Physical Chemistry AVol. 129(42)
Virginia Tech (US)
Openalex Percentile: Top 65%
Blind Source Separation Techniques
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Implementation of McMurchie–Davidson Algorithm for Gaussian AO Integrals Suited for SIMD Processors — Andrey Asadchev, Edward F. Valeev · The Journal of Physical Chemistry A (2025) | TGRS Research Map | TGRS