Josh Buckner <bucknerj@umich.edu>, with contributions from
Justin Wilson <jrw972@gmail.com>
This package provides R interfaces to a handful of common
data-mining algorithms.  These algorithms are implemented in
parallel using a mixture of nVidia's CUDA langauge and cublas
library.  On a computer equiped with an nVidia GPU these
functions may be substantially more efficient than native R
routines.

nvcc (>= release 2.1) nVidia GPU (compute number >=
1.3)
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 and CUBLAS
library.  On a computer equiped with an nVidia GPU some of
these functions may be substantially more efficient than native
R routines.

Josh Buckner <bucknerj@umich.edu>, Mark Seligman
<mseligman@rapidbiologics.com>, Justin Wilson
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.

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.

Josh Buckner <nullsatz@gmail.com>, Mark Seligman
<mseligman@rapidbiologics.com>, Justin Wilson
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.