Benchmarking Parallel Performance on Many-Core Processors

  • Bryant C. Lam
  • Ajay Barboza
  • Ravi Agrawal
  • Alan D. George
  • Herman Lam
Conference paper

DOI: 10.1007/978-3-319-05215-1_3

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8356)
Cite this paper as:
Lam B.C., Barboza A., Agrawal R., George A.D., Lam H. (2014) Benchmarking Parallel Performance on Many-Core Processors. In: Poole S., Hernandez O., Shamis P. (eds) OpenSHMEM and Related Technologies. Experiences, Implementations, and Tools. OpenSHMEM 2014. Lecture Notes in Computer Science, vol 8356. Springer, Cham

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 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Bryant C. Lam
    • 1
  • Ajay Barboza
    • 1
  • Ravi Agrawal
    • 1
  • Alan D. George
    • 1
  • Herman Lam
    • 1
  1. 1.NSF Center for High-Performance Reconfigurable Computing (CHREC), Department of Electrical and Computer EngineeringUniversity of FloridaGainesvilleUSA

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