Open Accessgpustats: GPU Library for Statistical Computing in Python
Author(s) -
Andrew Cron,
Wes McKinney
Publication year - 2011
Publication title -
proceedings of the python in science conferences
Language(s) - English
Resource type - Conference proceedings
ISSN - 2575-9752
DOI - 10.25080/majora-ebaa42b7-003
Subject(s) - computer science , python (programming language) , speedup , parallel computing , general purpose computing on graphics processing units , inference , computational statistics , monte carlo method , markov chain monte carlo , statistical inference , programming language , theoretical computer science , bayesian probability , computational science , artificial intelligence , computer graphics (images) , machine learning , mathematics , graphics , statistics
In this talk we will discuss gpustats, a new Python library for assisting in “big data” statistical computing applications, particularly Monte Carlobased inference algorithms. The library provides a general code generation / metaprogramming framework for easily implementing discrete and continuous probability density functions and random variable samplers. These functions can be utilized to achieve more than 100x speedup over their CPU equivalents. We will demonstrate their use in an Bayesian MCMC application and discuss avenues for future work.
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