ERmod‐OpenACC: GPU Acceleration of Solvation Free Energy Calculation With Energy‐Representation Theory
ERmod-OpenACC is a GPU-accelerated implementation of a calculation package for the solvation free energy. In the implementation, the most computationally intensive processes-the calculation of the pair energies and the construction of the correlation function-have been optimized for GPUs. ERmod-OpenACC also supports single precision to maximize performance on RTX series GPUs, which are commonly used in molecular dynamics simulations. A common problem with OpenACC is data transfer between the CPU and GPU. By minimizing data transfer, the package's performance has been significantly improved. Compared to a single Xeon core (3.1 GHz), ERmod-OpenACC achieves a speedup of up to approximately <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mn>16</mml:mn> <mml:mo>.</mml:mo> <mml:mn>5</mml:mn> <mml:mo>×</mml:mo></mml:mrow> <mml:annotation>$$ 16.5\times $$</mml:annotation></mml:semantics> </mml:math> when running on RTX 4080 SUPER. The package also supports multiple GPUs via MPI, achieving a speedup of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mn>2</mml:mn> <mml:mo>×</mml:mo></mml:mrow> <mml:annotation>$$ 2\times $$</mml:annotation></mml:semantics> </mml:math> with 2 GPUs and approximately <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mn>14</mml:mn> <mml:mo>.</mml:mo> <mml:mn>5</mml:mn> <mml:mo>×</mml:mo></mml:mrow> <mml:annotation>$$ 14.5\times $$</mml:annotation></mml:semantics> </mml:math> with 16 GPUs. The only external library required to compile ERmod-ACC is NVIDIA HPC SDK. The package is available at https://github.com/drmaruyama/ermod-openacc.
Authors
- Yutaka Maruyama (ORCID: https://orcid.org/0000-0003-4035-4885)
- Nobuyuki Matubayasi (ORCID: https://orcid.org/0000-0001-7176-441X)
- H. Kojima (ORCID: https://orcid.org/0009-0001-7716-9010)
Institutions
- Maruho (Japan) (JP)
- The University of Osaka (JP)
Publication Details
- Journal
- Journal of Computational Chemistry
- Published
- 2025-06-08
- DOI
- https://doi.org/10.1002/jcc.70152
- Citations
- 2
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 1.04
Funders
- Ministry of Education, Culture, Sports, Science and Technology
- University of Tsukuba
- Japan Society for the Promotion of Science