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Improving the Performance of Task-Based Linear Algebra Software with Autotuning Techniques on Heterogeneous Architectures

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Computational Science – ICCS 2023 (ICCS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14073))

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Abstract

This work presents several self-optimization strategies to improve the performance of task-based linear algebra software on heterogeneous systems. The study focuses on Chameleon, a task-based dense linear algebra software whose routines are computed using a tile-based algorithmic scheme and executed in the available computing resources of the system using a scheduler which dynamically handles data dependencies among the basic computational kernels of each linear algebra routine. The proposed strategies are applied to select the best values for the parameters that affect the performance of the routines, such as the tile size or the scheduling policy, among others. Also, parallel optimized implementations provided by existing linear algebra libraries, such as Intel MKL (on multicore CPU) or cuBLAS (on GPU) are used to execute each of the computational kernels of the routines. Results obtained on a heterogeneous system composed of several multicore and multiGPU are satisfactory, with performances close to the experimental optimum.

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Acknowledgements

Grant RTI2018-098156-B-C53 funded by MCIN/AEI and by “ERDF A way of making Europe”.

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Authors and Affiliations

  1. Department of Informatics, University of Valladolid, Valladolid, Spain

    Jesús Cámara

  2. Department of Engineering and Technology of Computers, University of Murcia, Murcia, Spain

    Javier Cuenca

  3. Department of Sciences, University of Bahia, Salvador, Brazil

    Murilo Boratto

Authors
  1. Jesús Cámara
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  2. Javier Cuenca
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  3. Murilo Boratto
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Corresponding author

Correspondence to Jesús Cámara .

Editor information

Editors and Affiliations

  1. Czech Technical University in Prague, Prague, Czech Republic

    Jiří Mikyška

  2. University of Amsterdam, Amsterdam, The Netherlands

    Clélia de Mulatier

  3. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  4. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  5. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  6. University of Amsterdam, Amsterdam, The Netherlands

    Peter M.A. Sloot

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Cámara, J., Cuenca, J., Boratto, M. (2023). Improving the Performance of Task-Based Linear Algebra Software with Autotuning Techniques on Heterogeneous Architectures. In: Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2023. ICCS 2023. Lecture Notes in Computer Science, vol 14073. Springer, Cham. https://doi.org/10.1007/978-3-031-35995-8_47

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  • DOI: https://doi.org/10.1007/978-3-031-35995-8_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-35994-1

  • Online ISBN: 978-3-031-35995-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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