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.
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.
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
Footnotes (1) In percent, seasonally adjusted. Annual averages are available for Not Seasonally Adjusted data. (2) Number of jobs, in thousands, seasonally adjusted. (3) For production and nonsupervisory workers on private nonfarm payrolls, seasonally adjusted. (4) All items, U.S. city average, all urban consumers, 1982-84=100, 1-month percent change, seasonally adjusted. (5) Finished goods, 1982=100, 1-month percent change, seasonally adjusted. (6) All imports, 1-month percent change, not seasonally adjusted. (R) Revised (P) Preliminary