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gputools (version 0.26)

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 langauge, Nvidia's CUBLAS library, and EMI Photonics' CULA libraries. On a computer equiped 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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Version

Install

install.packages('gputools')

Monthly Downloads

74

Version

0.26

License

GPL-3

Maintainer

Josh Buckner

Last Published

November 3rd, 2011

Functions in gputools (0.26)

B spline based mutual information

Calculate Various Correlation Coefficients With a GPU

Choose which GPU device to use

Perform Granger Causality Tests for Vectors on a GPU

Fitting Linear Models using a GPU--enabled QR

Singular Value Decomposition of a Matrix with a GPU

Estimate the QR decomposition for a matrix

Compute Distances Between Vectors on a GPU

Perform Hierarchical Clustering for Vectors with a GPU

Fitting generalized linear models using GPU--enabled QR decomposition

Compute Distances and Hierarchical Clustering for Vectors on a GPU

Fitter functions for gpu enabled linear models

Discover the Id of the current GPU device

Perform Matrix Multiplication with a GPU

Least squares fit using GPU--enabled QR decomposition

A support vector machine style binary classifier

GPU enabled FastICA algorithm

gpuLm.defaultTol

Function to switch tolerance depending on precision

Perform Matrix Cross-product with a GPU

Perform Matrix Transposed Cross-product with a GPU

Estimate the solution to a matrix vector equation

Train a support vector machine on a data set

Estimate the AUC of the ROC

T-Test Estimator with a GPU