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gputools (version 0.27)

A few GPU enabled functions

Description

This package provides R interfaces to a handful of common statistical algorithms. These algorithms are implemented in parallel using a mixture of Nvidia's CUDA language, Nvidia's CUBLAS library, and EMI Photonics' CULA libraries. On a computer equipped with an Nvidia GPU some of these functions may be substantially more efficient than native R routines. Thanks to Craig Stark at UC Irvine for donating time on his lab's Mac hardware.

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Install

install.packages('gputools')

Monthly Downloads

74

Version

0.27

License

GPL-3

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Repository

https://github.com/nullsatz/gputools/wiki

Maintainer

Josh Buckner

Last Published

April 4th, 2013

Functions in gputools (0.27)

GPU enabled FastICA algorithm

Discover the Id of the current GPU device

Perform Hierarchical Clustering for Vectors with a GPU

B spline based mutual information

Estimate the QR decomposition for a matrix

Choose which GPU device to use

Compute Distances Between Vectors on a GPU

Perform Matrix Cross-product with a GPU

gpuLm.defaultTol

Function to switch tolerance depending on precision

Estimate the AUC of the ROC

Perform Matrix Multiplication with a GPU

Singular Value Decomposition of a Matrix with a GPU

T-Test Estimator with a GPU

Compute Distances and Hierarchical Clustering for Vectors on a GPU

Perform Granger Causality Tests for Vectors on a GPU

Calculate Various Correlation Coefficients With a GPU

Fitter functions for gpu enabled linear models

Estimate the solution to a matrix vector equation

Least squares fit using GPU--enabled QR decomposition

Perform Matrix Transposed Cross-product with a GPU

Fitting generalized linear models using GPU--enabled QR decomposition

Fitting Linear Models using a GPU--enabled QR