Accelerating Time Integration for the Shallow Water Equations on the Sphere Using GPUs
Abstract
The push towards larger and larger computational platforms has made it possible for climate simulations to resolve climate dynamics across multiple spatial and temporal scales. This direction in climate simulation has created a strong need to develop scalable timestepping methods capable of accelerating throughput on high performance computing. This study details the recent advances in the implementation of implicit time stepping of the spectral element dynamical core within the United States Department of Energy (DOE) Accelerated Climate Model for Energy (ACME) on graphical processing units (GPU) based machines. We demonstrate how solvers in the Trilinos project are interfaced with ACME and GPU kernels to increase computational speed of the residual calculations in the implicit time stepping method for the atmosphere dynamics. We demonstrate the optimization gains and data structure reorganization that facilitates the performance improvements.
- Authors:
-
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Publication Date:
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
- OSTI Identifier:
- 1214700
- Alternate Identifier(s):
- OSTI ID: 1265329
- Grant/Contract Number:
- AC04-94AL85000; AC05-00OR22725
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Procedia Computer Science
- Additional Journal Information:
- Journal Volume: 51; Journal Issue: C; Journal ID: ISSN 1877-0509
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; 97 MATHEMATICS AND COMPUTING; Implicit Timestepping; GPU; Trilinos
Citation Formats
Archibald, R., Evans, K. J., and Salinger, A. Accelerating Time Integration for the Shallow Water Equations on the Sphere Using GPUs. United States: N. p., 2015.
Web. doi:10.1016/j.procs.2015.05.470.
Archibald, R., Evans, K. J., & Salinger, A. Accelerating Time Integration for the Shallow Water Equations on the Sphere Using GPUs. United States. https://doi.org/10.1016/j.procs.2015.05.470
Archibald, R., Evans, K. J., and Salinger, A. Mon .
"Accelerating Time Integration for the Shallow Water Equations on the Sphere Using GPUs". United States. https://doi.org/10.1016/j.procs.2015.05.470. https://www.osti.gov/servlets/purl/1214700.
@article{osti_1214700,
title = {Accelerating Time Integration for the Shallow Water Equations on the Sphere Using GPUs},
author = {Archibald, R. and Evans, K. J. and Salinger, A.},
abstractNote = {The push towards larger and larger computational platforms has made it possible for climate simulations to resolve climate dynamics across multiple spatial and temporal scales. This direction in climate simulation has created a strong need to develop scalable timestepping methods capable of accelerating throughput on high performance computing. This study details the recent advances in the implementation of implicit time stepping of the spectral element dynamical core within the United States Department of Energy (DOE) Accelerated Climate Model for Energy (ACME) on graphical processing units (GPU) based machines. We demonstrate how solvers in the Trilinos project are interfaced with ACME and GPU kernels to increase computational speed of the residual calculations in the implicit time stepping method for the atmosphere dynamics. We demonstrate the optimization gains and data structure reorganization that facilitates the performance improvements.},
doi = {10.1016/j.procs.2015.05.470},
journal = {Procedia Computer Science},
number = C,
volume = 51,
place = {United States},
year = {Mon Jun 01 00:00:00 EDT 2015},
month = {Mon Jun 01 00:00:00 EDT 2015}
}
Web of Science
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