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A High Performance SYMV Kernel on a Fermi-core GPU

  • Conference paper
High Performance Computing for Computational Science - VECPAR 2012 (VECPAR 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7851))

Abstract

A high-performance SYMV kernel is implemented on Fermi-core GPUs using an atomic-operation based algorithm. The algorithm is effective for the memory bandwidth and reduced memory usage. On a Tesla C2050, sustained double-precision and single-precision performances of approximately 43 GFLOPS and 78 GFLOPS, respectively, were achieved. The proposed SYMV kernel also performs on a GeForce GTX580 with 72 GFLOPS and 128 GFLOPS in the double-precision and single-precision modes, respectively. The proposed SYMV kernel outperforms major CUDA BLAS kernels, CUBLAS, MAGMABLAS, and CULA-BLAS. This performance improvement has a significant impact when the SYMV kernel is plugged into user codes.

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References

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Author information

Authors and Affiliations

  1. University of Electro-Communications, Chofu-shi, Tokyo, 182-8585, Japan

    Toshiyuki Imamura

  2. RIKEN Advanced Institute for Computational Science, Kobe-shi, Hyogo, 650-0047, Japan

    Toshiyuki Imamura

  3. CCSE, Japan Atomic Energy Agency, Kashiwa-shi, Chiba, 277-8587, Japan

    Susumu Yamada & Masahiko Machida

  4. CREST, Japan Science and Technology Agency, Japan

    Toshiyuki Imamura, Susumu Yamada & Masahiko Machida

Authors
  1. Toshiyuki Imamura
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  2. Susumu Yamada
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  3. Masahiko Machida
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Editor information

Editors and Affiliations

  1. INPT (ENSEEIHT) - IRIT, University of Toulouse, 31062, Toulouse, France

    Michel Daydé

  2. Lawrence Berkeley National Laboratory, 94720-8139, Berkeley, CA, USA

    Osni Marques

  3. Information Technology Center, The University of Tokyo, 113-8658, Tokyo, Japan

    Kengo Nakajima

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© 2013 Springer-Verlag Berlin Heidelberg

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Imamura, T., Yamada, S., Machida, M. (2013). A High Performance SYMV Kernel on a Fermi-core GPU. In: Daydé, M., Marques, O., Nakajima, K. (eds) High Performance Computing for Computational Science - VECPAR 2012. VECPAR 2012. Lecture Notes in Computer Science, vol 7851. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38718-0_9

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  • DOI: https://doi.org/10.1007/978-3-642-38718-0_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38717-3

  • Online ISBN: 978-3-642-38718-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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