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The performances of R GPU implementations of the GMRES method

Author

Listed:
  • Bogdan Oancea

    (University of Bucharest)

  • Richard Pospisil

    (Palacky University of Olomouc)

Abstract

Although the performance of commodity computers has improved drastically with the introduction of multicore processors and GPU computing, the standard R distribution is still based on single-threaded model of computation, using only a small fraction of the computational power available now for most desktops and laptops. Modern statistical software packages rely on high performance implementations of the linear algebra routines there are at the core of several important leading edge statistical methods. In this paper we present a GPU implementation of the GMRES iterative method for solving linear systems. We compare the performance of this implementation with a pure single threaded version of the CPU. We also investigate the performance of our implementation using different GPU packages available now for R such as gmatrix, gputools or gpuR which are based on CUDA or OpenCL frameworks.

Suggested Citation

  • Bogdan Oancea & Richard Pospisil, 2018. "The performances of R GPU implementations of the GMRES method," Romanian Statistical Review, Romanian Statistical Review, vol. 66(1), pages 121-132, March.
    • Handle: RePEc:rsr:journl:v:66:y:2018:i:1:p:121-132
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    More about this item

    Keywords

    R; GPU; statistical software; GMRES;
    All these keywords.

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

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