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Accelerating R with high performance linear algebra libraries

Author

Listed:
  • Bogdan Oancea

    (“Nicolae Titulescu” University of Bucharest)

  • Tudorel Andrei

    (National Statistics Institute of Romania)

  • Raluca Mariana Dragoescu

    (The Bucharest University of Economic Studies)

Abstract

Linear algebra routines are basic building blocks for the statistical software. In this paper we analyzed how can we improve R performance for matrix computations. We benchmarked few matrix operations using the standard linear algebra libraries included in the R distribution and high performance libraries like OpenBLAS, GotoBLAS and MKL. Our tests showed the best results are obtained with the MKL library, the other two libraries having similar performances, but lower than MKL.

Suggested Citation

  • Bogdan Oancea & Tudorel Andrei & Raluca Mariana Dragoescu, 2015. "Accelerating R with high performance linear algebra libraries," Romanian Statistical Review, Romanian Statistical Review, vol. 63(3), pages 109-117, September.
    • Handle: RePEc:rsr:journl:v:63:y:2015:i:3:p:109-117
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    References listed on IDEAS

    as
    1. Eddelbuettel, Dirk & Sanderson, Conrad, 2014. "RcppArmadillo: Accelerating R with high-performance C++ linear algebra," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1054-1063.
    2. Bates, Douglas & Eddelbuettel, Dirk, 2013. "Fast and Elegant Numerical Linear Algebra Using the RcppEigen Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 52(i05).
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    Cited by:

    1. Bogdan Oancea & Tudorel Andrei & Raluca Mariana Dragoescu, 2016. "An R implementation of a Recurrent Neural Network Trained by Extended Kalman Filter," Romanian Statistical Review, Romanian Statistical Review, vol. 64(2), pages 125-133, June.

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