Open AccessBayesian Lasso and multinomial logistic regression on GPU
Author(s) -
Rok Češnovar,
Erik Štrumbelj
Publication year - 2017
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0180343
Subject(s) - lasso (programming language) , multinomial logistic regression , bayesian probability , logistic regression , multinomial distribution , statistics , computer science , mathematics , artificial intelligence , world wide web
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.