May 4, 2016 · The gpuR package has been created to bring GPU computing to as many R users as possible. It is the intention to use gpuR to more easily supplement current and future algorithms that could benefit from GPU acceleration.

Instead, we will rely on rpud and other R packages for studying GPU computing. We will compare the performance of GPU functions with their regular R counterparts and verify the performance advantage.

Jan 27, 2015 · In this tutorial, we'll see how this is done, both in passive ways (you write only R), and in more direct ways, where you write C/C++ code and interface it to R. What Are GPUs? A GPU is basically just a graphics card.

GPUmatrix mimics the behavior of the Matrix package and extends R to use the GPU for computations. It includes single (FP32) and double (FP64) precision data types, and provides support for sparse matrices. It is easy to learn, and requires very few code changes to perform the operations on the GPU.

In this article, I will introduce the computation model of R with GPU acceleration, focusing on three topics: accelerating R computations using CUDA libraries; calling your own parallel algorithms written in CUDA C/C++ or CUDA Fortran from R; and; profiling GPU-accelerated R applications using the CUDA Profiler. The GPU-Accelerated R Software Stack

class: title-slide, center, middle, remark-slide-content, inverse, title-slide, hljs-github # GPU Computing with R ### Jared P. Lander ### Chief Data Scientist <img ...

Provides GPU enabled functions for 'R' objects in a simple and approachable manner. New 'gpu*' and 'vcl*' classes have been provided to wrap typical 'R' objects (e.g. vector, matrix), in both host and device spaces, to mirror typical 'R' syntax without the need to know 'OpenCL'.

Jun 3, 2015 · The rpud package for R implements a few algorithms in R that will use a CUDA-compatible NVIDIA GPU for the computations. The algorithms include support vector machines, bayesian classification, and hierarchical linear models.

May 4, 2016 · The ‘gpuR’ package was created to bring the power of GPU computing to any R user with a GPU device. Although there are a handful of packages that provide some GPU capability (e.g. gputools , cudaBayesreg , HiPLARM , HiPLARb , and gmatrix ) all are strictly limited to NVIDIA GPUs.

The goal of this package is to provide the user a very simple R API that can be used with any GPU (via an OpenCL backend). This is accomplished by interfacing with the ViennaCL library that I have packaged in the R package RViennaCL .