Chapter

OpenSHMEM and Related Technologies. Experiences, Implementations, and Tools

Volume 8356 of the series Lecture Notes in Computer Science pp 29-43

Benchmarking Parallel Performance on Many-Core Processors

  • Bryant C. LamAffiliated withLancaster UniversityNSF Center for High-Performance Reconfigurable Computing (CHREC), Department of Electrical and Computer Engineering, University of Florida
  • , Ajay BarbozaAffiliated withLancaster UniversityNSF Center for High-Performance Reconfigurable Computing (CHREC), Department of Electrical and Computer Engineering, University of Florida
  • , Ravi AgrawalAffiliated withLancaster UniversityNSF Center for High-Performance Reconfigurable Computing (CHREC), Department of Electrical and Computer Engineering, University of Florida
  • , Alan D. GeorgeAffiliated withLancaster UniversityNSF Center for High-Performance Reconfigurable Computing (CHREC), Department of Electrical and Computer Engineering, University of Florida
  • , Herman LamAffiliated withLancaster UniversityNSF Center for High-Performance Reconfigurable Computing (CHREC), Department of Electrical and Computer Engineering, University of Florida

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Abstract

With the emergence of many-core processor architectures onto the HPC scene, concerns arise regarding the performance and productivity of numerous existing parallel-programming tools, models, and languages. As these devices begin augmenting conventional distributed cluster systems in an evolving age of heterogeneous supercomputing, proper evaluation and profiling of many-core processors must occur in order to understand their performance and architectural strengths with existing parallel-programming environments and HPC applications. This paper presents and evaluates the comparative performance between two many-core processors, the Tilera TILE-Gx8036 and the Intel Xeon Phi 5110P, in the context of their applications performance with the SHMEM and OpenMP parallel-programming environments. Several applications written or provided in SHMEM and OpenMP are evaluated in order to analyze the scalability of existing tools and libraries on these many-core platforms. Our results show that SHMEM and OpenMP parallel applications scale well on the TILE-Gx and Xeon Phi, but heavily depend on optimized libraries and instrumentation.

Keywords

PGAS SHMEM OpenMP many-core parallel programming performance analysis high-performance computing parallel architectures