Implicit Unstructured Aerodynamics on Emerging Multi- and Many-Core HPC Architectures

Handle URI:
http://hdl.handle.net/10754/623355
Title:
Implicit Unstructured Aerodynamics on Emerging Multi- and Many-Core HPC Architectures
Authors:
Al Farhan, Mohammed A.; Kaushik, Dinesh K.; Keyes, David E. ( 0000-0002-4052-7224 )
Abstract:
Shared memory parallelization of PETSc-FUN3D, an unstructured tetrahedral mesh Euler code previously characterized for distributed memory Single Program, Multiple Data (SPMD) for thousands of nodes, is hybridized with shared memory Single Instruction, Multiple Data (SIMD) for hundreds of threads per node. We explore thread-level performance optimizations on state-of-the-art multi- and many-core Intel processors, including the second generation of Xeon Phi, Knights Landing (KNL). We study the performance on the KNL with different configurations of memory and cluster modes, with code optimizations to minimize indirect addressing and enhance the cache locality. The optimizations employed are expected to be of value other unstructured applications as many-core architecture evolves.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Extreme Computing Research Center
Conference/Event name:
High Performance Computing Saudi Arabia (HPC Saudi) 2017
Issue Date:
13-Mar-2017
Type:
Poster
Appears in Collections:
Posters; High Performance Computing Saudi Arabia (HPC Saudi) 2017

Full metadata record

DC FieldValue Language
dc.contributor.authorAl Farhan, Mohammed A.en
dc.contributor.authorKaushik, Dinesh K.en
dc.contributor.authorKeyes, David E.en
dc.date.accessioned2017-05-04T12:33:23Z-
dc.date.available2017-05-04T12:33:23Z-
dc.date.issued2017-03-13-
dc.identifier.urihttp://hdl.handle.net/10754/623355-
dc.description.abstractShared memory parallelization of PETSc-FUN3D, an unstructured tetrahedral mesh Euler code previously characterized for distributed memory Single Program, Multiple Data (SPMD) for thousands of nodes, is hybridized with shared memory Single Instruction, Multiple Data (SIMD) for hundreds of threads per node. We explore thread-level performance optimizations on state-of-the-art multi- and many-core Intel processors, including the second generation of Xeon Phi, Knights Landing (KNL). We study the performance on the KNL with different configurations of memory and cluster modes, with code optimizations to minimize indirect addressing and enhance the cache locality. The optimizations employed are expected to be of value other unstructured applications as many-core architecture evolves.en
dc.titleImplicit Unstructured Aerodynamics on Emerging Multi- and Many-Core HPC Architecturesen
dc.typePosteren
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentExtreme Computing Research Centeren
dc.conference.dateMarch 13-15, 2017en
dc.conference.nameHigh Performance Computing Saudi Arabia (HPC Saudi) 2017en
dc.conference.locationKAUSTen
dc.contributor.institutionQatar Environment and Energy Research Instituteen
kaust.authorAl Farhan, Mohammed A.en
kaust.authorKeyes, David E.en
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