Abstract
Grit is a software library designed specifically for conducting particle-based Lagrangian simulations with CPU/GPUperformance portability. This library allows researchers and engineers to perform a wide range of simulations, including multiphase flow, and more. Grit employs Message Passing Interface (MPI) for distributed memory parallelism and Kokkos programming model for on-node shared memory parallelism with performance portability across different architectures of GPUs and multi-core/manycore CPUs.
- Release Date:
- 2023-06-22
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Programming Languages:
-
C++
- Licenses:
-
Other (Commercial or Open-Source): https://github.com/ORNL/grit/blob/master/LICENSE
- Sponsoring Org.:
-
USDOE Office of Science (SC)Primary Award/Contract Number:AC05-00OR22725
- Code ID:
- 108669
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)Sandia National Laboratories (SNL-CA), Livermore, CA (United States)
- Country of Origin:
- United States
Citation Formats
Ge, Wenjun, and Sankaran, Ramanan.
ORNL/grit.
Computer Software.
https://github.com/ORNL/grit.
USDOE Office of Science (SC).
22 Jun. 2023.
Web.
doi:10.11578/dc.20230622.1.
Ge, Wenjun, & Sankaran, Ramanan.
(2023, June 22).
ORNL/grit.
[Computer software].
https://github.com/ORNL/grit.
https://doi.org/10.11578/dc.20230622.1.
Ge, Wenjun, and Sankaran, Ramanan.
"ORNL/grit." Computer software.
June 22, 2023.
https://github.com/ORNL/grit.
https://doi.org/10.11578/dc.20230622.1.
@misc{
doecode_108669,
title = {ORNL/grit},
author = {Ge, Wenjun and Sankaran, Ramanan},
abstractNote = {Grit is a software library designed specifically for conducting particle-based Lagrangian simulations with CPU/GPUperformance portability. This library allows researchers and engineers to perform a wide range of simulations, including multiphase flow, and more. Grit employs Message Passing Interface (MPI) for distributed memory parallelism and Kokkos programming model for on-node shared memory parallelism with performance portability across different architectures of GPUs and multi-core/manycore CPUs. },
doi = {10.11578/dc.20230622.1},
url = {https://doi.org/10.11578/dc.20230622.1},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20230622.1}},
year = {2023},
month = {jun}
}
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