Speeding Up Hartree–Fock in JuliaChem with Density Fitting

In this work, the density fitting (DF) approximation is added to the restricted Hartree-Fock (RHF) implementation in the JuliaChem computational chemistry code. Utilizing a DF algorithm that uses symmetry and integral screening, a significant reduction in time to compute the Fock matrix is achieved. The symmetry and screening DF-RHF techniques were adapted to be performed on graphics processing units (GPUs), which are well suited to perform the matrix multiplications that comprise the bulk of the Fock build time in DF-RHF. The JuliaChem DF-RHF GPU algorithm employs a novel approach that automatically switches between two DF-RHF algorithms depending on the number of basis functions in the calculation. The JuliaChem GPU DF-RHF implementation demonstrates up to 2× speedup for Fock build times compared to the existing best-in-class GPU DF-RHF implementation by operating directly on screened intermediate matrices. Due to the high portability of the Julia language code, the JuliaChem CPU and GPU DF-RHF implementations could be benchmarked on a variety of CPU and GPU architectures from multiple hardware vendors.

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Publication Details

Journal
Journal of Chemical Theory and Computation
Published
2026-04-06
DOI
https://doi.org/10.1021/acs.jctc.5c02025
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00

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article

Speeding Up Hartree–Fock in JuliaChem with Density Fitting

John J. Hayes
Journal of Chemical Theory and Computation
Machine Learning in Materials Science
article

Speeding Up Hartree–Fock in JuliaChem with Density Fitting

John J. Hayes
article en

Abstract

In this work, the density fitting (DF) approximation is added to the restricted Hartree-Fock (RHF) implementation in the JuliaChem computational chemistry code. Utilizing a DF algorithm that uses symmetry and integral screening, a significant reduction in time to compute the Fock matrix is achieved. The symmetry and screening DF-RHF techniques were adapted to be performed on graphics processing units (GPUs), which are well suited to perform the matrix multiplications that comprise the bulk of the Fock build time in DF-RHF. The JuliaChem DF-RHF GPU algorithm employs a novel approach that automatically switches between two DF-RHF algorithms depending on the number of basis functions in the calculation. The JuliaChem GPU DF-RHF implementation demonstrates up to 2× speedup for Fock build times compared to the existing best-in-class GPU DF-RHF implementation by operating directly on screened intermediate matrices. Due to the high portability of the Julia language code, the JuliaChem CPU and GPU DF-RHF implementations could be benchmarked on a variety of CPU and GPU architectures from multiple hardware vendors.

Journal of Chemical Theory and ComputationVol. 22(8)
Iowa State University (US), Ames National Laboratory (US)
National Energy Research Scientific Computing Center, Ames Laboratory
Openalex Percentile: Top 29%
Machine Learning in Materials Science
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