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Demonstrating GPU code portability and scalability for radiative heat transfer computations

Journal Article · · Journal of Computational Science
 [1];  [2];  [2];  [2];  [2];  [3];  [4]
  1. Univ. of Utah, Salt Lake City, UT (United States); University of Utah
  2. Univ. of Utah, Salt Lake City, UT (United States)
  3. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  4. NVIDIA Corp., Santa Clara, CA (United States)
High performance computing frameworks utilizing CPUs, Nvidia GPUs, and/or Intel Xeon Phis necessitate portable and scalable solutions for application developers. Nvidia GPUs in particular present numerous portability challenges with a different programming model, additional memory hierarchies, and partitioned execution units among streaming multiprocessors. Here, this work presents modifications to the Uintah asynchronous many-task runtime and the Kokkos portability library to enable one single codebase for complex multiphysics applications to run across different architectures. Scalability and performance results are shown on multiple architectures for a globally coupled radiation heat transfer simulation, ranging from a single node to 16384 Titan compute nodes.
Research Organization:
Univ. of Utah, Salt Lake City, UT (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC02-06CH11357; AC05-00OR22725; NA0002375
OSTI ID:
1582435
Alternate ID(s):
OSTI ID: 1694247
OSTI ID: 1565656
Journal Information:
Journal of Computational Science, Journal Name: Journal of Computational Science Journal Issue: C Vol. 27; ISSN 1877-7503
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

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