Andrew Cron
Duke University

Wes McKinney
Duke University

Abstract

In this work we discuss gpustats, a new Python library for assisting in \textquotedbl{}big data\textquotedbl{} statistical computing applications, particularly Monte Carlo-based 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 demonstrate their use in an Bayesian MCMC application and discuss avenues for future work.

Keywords

GPU, CUDA, OpenCL, Python, statistical inference, statistics, metaprogramming, sampling, Markov Chain Monte Carlo (MCMC), PyMC, big data

DOI

10.25080/Majora-ebaa42b7-003

Bibtex entry

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Copyright © 2011 Andrew Cron et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.