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Mathematica latest software to offer built-in Integration with R #rstats
Just got a message from the good chaps at Wolfram Alpha/Mathematica
Mathematica 9 offers built-in ways to integrate R code into your Mathematica workflow, combining Mathematica‘s broad range of capabilities with the statistical computing language. RLink uses J/Link and rJava/JRI Java libraries to allow the user to exchange data between Mathematica and R and to execute R code from within Mathematica. With RLink, R users can use thousands of functions from across the full Mathematica system.
see more at
http://www.wolfram.com/mathematica/new-in-9/built-in-integration-with-r/
Matlab-Mathematica-R and GPU Computing
Matlab announced they have a parallel computing toolbox- specially to enable GPU computing as well
http://www.mathworks.com/products/parallel-computing/
Parallel Computing Toolbox™ lets you solve computationally and data-intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs—parallel for-loops, special array types, and parallelized numerical algorithms—let you parallelize MATLAB® applications without CUDA or MPI programming. You can use the toolbox with Simulink® to run multiple simulations of a model in parallel.
The toolbox provides eight workers (MATLAB computational engines) to execute applications locally on a multicore desktop. Without changing the code, you can run the same application on a computer cluster or a grid computing service (using MATLAB Distributed Computing Server™). You can run parallel applications interactively or in batch.
Parallel Computing with MATLAB on Amazon Elastic Compute Cloud (EC2)
Also a video of using Mathematica and GPU
Also R has many packages for GPU computing
Parallel computing: GPUs
from http://cran.r-project.org/web/views/HighPerformanceComputing.html
- The gputools package by Buckner provides several common data-mining algorithms which are implemented using a mixture of nVidia‘s CUDA langauge and cublas library. Given a computer with an nVidia GPU these functions may be substantially more efficient than native R routines. The rpud package provides an optimised distance metric for NVidia-based GPUs.
- The cudaBayesreg package by da Silva implements the rhierLinearModel from the bayesm package using nVidia’s CUDA langauge and tools to provide high-performance statistical analysis of fMRI voxels.
- The rgpu package (see below for link) aims to speed up bioinformatics analysis by using the GPU.
- The magma package provides an interface to the hybrid GPU/CPU library Magma (see below for link).
- The gcbd package implements a benchmarking framework for BLAS and GPUs (using gputools).
I tried to search for SAS and GPU and SPSS and GPU but got nothing. Maybe they would do well to atleast test these alternative hardwares-
Also see Matlab on GPU comparison for the product Jacket vs Parallel Computing Toolbox
