Abstract
GPU Monte Carlo Simulation Code with a taste of RASPA
We present enhancements in Monte Carlo simulation speed and functionality within an open-source code, gRASPA, which uses graphical processing units (GPUs) to achieve significant performance improvements compared to serial, CPU implementations of Monte Carlo. The code supports a wide range of Monte Carlo simulations, including canonical ensemble (NVT), grand canonical, NVT Gibbs, Widom test particle insertions, and continuous-fractional component Monte Carlo. Implementation of grand canonical transition matrix Monte Carlo (GC-TMMC) and a novel feature to allow different moves for the different components of metal-organic framework (MOF) structures exemplify the capabilities of gRASPA for precise free energy calculations and enhanced adsorption studies, respectively. The introduction of a High-Throughput Computing (HTC) mode permits many Monte Carlo simulations on a single GPU device for accelerated materials discovery. The code can incorporate machine learning (ML) potentials. The open-source nature of gRASPA promotes reproducibility and openness in science, and users may add features to the code and optimize it for their own purposes. The code is written in CUDA/C++ and SYCL/C++ to support different GPU vendors. The gRASPA code is publicly available at
https://github.com/snurr-group/gRASPA.
- Developers:
-
Li, Zhao [1] ; Shi, Kaihang [2][3] ; Dubbeldam, David [4] ; Dewing, Mark [5] ; Knight, Christopher [5] ; Vazquez Mayagoitia, Alvaro [5] ; Snurr, Randall [2]
- Purdue/Northwestern/Notre Dame University
- Northwestern Univ., Evanston, IL (United States)
- University at Buffalo, SUNY
- University of Amsterdam
- Argonne National Laboratory (ANL), Argonne, IL (United States)
- Release Date:
- 2025-03-08
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Licenses:
-
MIT License
- Sponsoring Org.:
-
USDOE Office of Science (SC), Basic Energy Sciences (BES)Primary Award/Contract Number:SC0023454Other Award/Contract Number:SC0008688
- Code ID:
- 172693
- Research Org.:
- Northwestern University
- Country of Origin:
- United States
Citation Formats
Li, Zhao, Shi, Kaihang, Dubbeldam, David, Dewing, Mark, Knight, Christopher, Vazquez Mayagoitia, Alvaro, and Snurr, Randall Q.
gRASPA.
Computer Software.
https://github.com/snurr-group/gRASPA.
USDOE Office of Science (SC), Basic Energy Sciences (BES).
08 Mar. 2025.
Web.
doi:10.1021/acs.jctc.4c01058.
Li, Zhao, Shi, Kaihang, Dubbeldam, David, Dewing, Mark, Knight, Christopher, Vazquez Mayagoitia, Alvaro, & Snurr, Randall Q.
(2025, March 08).
gRASPA.
[Computer software].
https://github.com/snurr-group/gRASPA.
https://doi.org/10.1021/acs.jctc.4c01058.
Li, Zhao, Shi, Kaihang, Dubbeldam, David, Dewing, Mark, Knight, Christopher, Vazquez Mayagoitia, Alvaro, and Snurr, Randall Q.
"gRASPA." Computer software.
March 08, 2025.
https://github.com/snurr-group/gRASPA.
https://doi.org/10.1021/acs.jctc.4c01058.
@misc{
doecode_172693,
title = {gRASPA},
author = {Li, Zhao and Shi, Kaihang and Dubbeldam, David and Dewing, Mark and Knight, Christopher and Vazquez Mayagoitia, Alvaro and Snurr, Randall Q.},
abstractNote = {GPU Monte Carlo Simulation Code with a taste of RASPA
We present enhancements in Monte Carlo simulation speed and functionality within an open-source code, gRASPA, which uses graphical processing units (GPUs) to achieve significant performance improvements compared to serial, CPU implementations of Monte Carlo. The code supports a wide range of Monte Carlo simulations, including canonical ensemble (NVT), grand canonical, NVT Gibbs, Widom test particle insertions, and continuous-fractional component Monte Carlo. Implementation of grand canonical transition matrix Monte Carlo (GC-TMMC) and a novel feature to allow different moves for the different components of metal-organic framework (MOF) structures exemplify the capabilities of gRASPA for precise free energy calculations and enhanced adsorption studies, respectively. The introduction of a High-Throughput Computing (HTC) mode permits many Monte Carlo simulations on a single GPU device for accelerated materials discovery. The code can incorporate machine learning (ML) potentials. The open-source nature of gRASPA promotes reproducibility and openness in science, and users may add features to the code and optimize it for their own purposes. The code is written in CUDA/C++ and SYCL/C++ to support different GPU vendors. The gRASPA code is publicly available at
https://github.com/snurr-group/gRASPA.},
doi = {10.1021/acs.jctc.4c01058},
url = {https://doi.org/10.1021/acs.jctc.4c01058},
howpublished = {[Computer Software] \url{https://doi.org/10.1021/acs.jctc.4c01058}},
year = {2025},
month = {mar}
}