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Tuning and Optimization for a Variety of Many-Core Architectures Without Changing a Single Line of Implementation Code Using the Alpaka Library

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High Performance Computing (ISC High Performance 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10524))

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Abstract

We present an analysis on optimizing performance of a single C++11 source code using the Alpaka hardware abstraction library. For this we use the general matrix multiplication (GEMM) algorithm in order to show that compilers can optimize Alpaka code effectively when tuning key parameters of the algorithm. We do not intend to rival existing, highly optimized DGEMM versions, but merely choose this example to prove that Alpaka allows for platform-specific tuning with a single source code. In addition we analyze the optimization potential available with vendor-specific compilers when confronted with the heavily templated abstractions of Alpaka. We specifically test the code for bleeding edge architectures such as Nvidia’s Tesla P100, Intel’s Knights Landing (KNL) and Haswell architecture as well as IBM’s Power8 system. On some of these we are able to reach almost 50% of the peak floating point operation performance using the aforementioned means. When adding compiler-specific we are able to reach 5 on a P100 and over 1 on a KNL system.

This project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No 654220. This project received funding within the MEPHISTO project (BMBF-Förderkennzeichen 01IH16006C). Research leading to these results has in parts been carried out on the Human Brain Project PCP Pilot System JURON at the Juelich Supercomputing Centre, which received co-funding from the European Union (Grant Agreement no. 604102). We thank for the access to and support for the HPC cluster Taurus at the Centre for Information Services and High Performance Computing (ZIH), Technical University Dresden, as well as the cluster Hypnos at the Helmholtz-Zentrum Dresden – Rossendorf.

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Authors and Affiliations

  1. Helmholtz-Zentrum Dresden – Rossendorf, Dresden, Germany

    Alexander Matthes, René Widera, Axel Huebl & Michael Bussmann

  2. Technische Universität Dresden, Dresden, Germany

    Alexander Matthes & Axel Huebl

  3. LogMeIn, Inc., Boston, USA

    Erik Zenker & Benjamin Worpitz

Authors
  1. Alexander Matthes
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  2. René Widera
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  3. Erik Zenker
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  4. Benjamin Worpitz
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  5. Axel Huebl
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  6. Michael Bussmann
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Correspondence to Alexander Matthes .

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Editors and Affiliations

  1. Deutsches Klimarechenzentrum (DKRZ), Hamburg, Hamburg, Germany

    Julian M. Kunkel

  2. TITECH, Tokyo, Japan

    Rio Yokota

  3. Department of Computer Science, University of Delaware, Newark, Delaware, USA

    Michela Taufer

  4. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    John Shalf

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Matthes, A., Widera, R., Zenker, E., Worpitz, B., Huebl, A., Bussmann, M. (2017). Tuning and Optimization for a Variety of Many-Core Architectures Without Changing a Single Line of Implementation Code Using the Alpaka Library. In: Kunkel, J., Yokota, R., Taufer, M., Shalf, J. (eds) High Performance Computing. ISC High Performance 2017. Lecture Notes in Computer Science(), vol 10524. Springer, Cham. https://doi.org/10.1007/978-3-319-67630-2_36

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  • DOI: https://doi.org/10.1007/978-3-319-67630-2_36

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