Saturday, 27 June 2009


I am interested in high performance computing for both data-mining and implmenting stochastic models. An exciting new possibility is the use of Graphical Processing Units for parallel processing.

The Biomanycores Project

nVidia have released a beta release of a complier for their cards in May which makes development easier. CUDA is available as open source with extensive documentation but the prefered method will the OpenCL when this becomes more widely available as this will not be hardware dependent.

supported by nVIDIA professor partnership.

An early example of code was the implementation of Smith-Waterman in SWcuda (~10 papers on implementations in 2007-2009). Can be built into any of the Bio* projects which insures portability (java, python, perl) but requires CUDA or OpenCL SDKs.

Incorporating GPUs into the R statistical environment

Josh Buchner ***
nVidia programmed with CUDA, GTX260,295.
New project(2 months) designed for exploratory data analysis of large-scale biomedical datasets. Implemented Grangers causality test, Pearson Correlation Coefficient.
  • gpuGranger
  • gpuCor
  • gpuHClust
  • gpuDistClust
  • gpuMi
  • gpuSolve
  • Also built an interface to SVMs but currently only available in linux and Mac versions.

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