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Bayesian Lasso and multinomial logistic regression on GPU

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  • Rok Češnovar
  • Erik Štrumbelj

Abstract

We describe an efficient Bayesian parallel GPU implementation of two classic statistical models—the Lasso and multinomial logistic regression. We focus on parallelizing the key components: matrix multiplication, matrix inversion, and sampling from the full conditionals. Our GPU implementations of Bayesian Lasso and multinomial logistic regression achieve 100-fold speedups on mid-level and high-end GPUs. Substantial speedups of 25 fold can also be achieved on older and lower end GPUs. Samplers are implemented in OpenCL and can be used on any type of GPU and other types of computational units, thereby being convenient and advantageous in practice compared to related work.

Suggested Citation

  • Rok Češnovar & Erik Štrumbelj, 2017. "Bayesian Lasso and multinomial logistic regression on GPU," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-17, June.
    • Handle: RePEc:plo:pone00:0180343
    DOI: 10.1371/journal.pone.0180343
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    References listed on IDEAS

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    1. Devroye, Luc, 2009. "On exact simulation algorithms for some distributions related to Jacobi theta functions," Statistics & Probability Letters, Elsevier, vol. 79(21), pages 2251-2259, November.
    2. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
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