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Demonstrating GPU code portability and scalability for radiative heat transfer computations

Journal Article · · Journal of Computational Science

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. 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 16,384 Titan compute nodes.

Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); UT-Battelle LLC/ORNL, Oak Ridge, TN (Unted States); Argonne National Lab. (ANL), Argonne, IL (United States); Univ. of Utah, Salt Lake City, UT (United States)
Sponsoring Organization:
USDOE Office of Science; National Nuclear Security Administration (NNSA)
DOE Contract Number:
AC05-00OR22725; AC02-06CH11357; NA0002375
OSTI ID:
1565656
Journal Information:
Journal of Computational Science, Journal Name: Journal of Computational Science Journal Issue: C Vol. 27; ISSN 1877-7503
Publisher:
Elsevier
Country of Publication:
United States
Language:
English

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