CAMPAIGN: An open-source Library of GPU-accelerated Data Clustering Algorithms

Kai J. Kohlhoff, Marc H. Sosnick, William T. Hsu, Vijay S. Pande, Russ B. Altman
Departments of Bioengineering, Stanford University, Stanford CA 94305-5448
Bioinformatics (27 June 2011)


   author={Kohlhoff, Kai J. and Sosnick, Marc H. and Hsu, William T. and Pande, Vijay S. and Altman, Russ B.},

   title={CAMPAIGN: An open-source Library of GPU-accelerated Data Clustering Algorithms},







Source Source   Source codes Source codes




MOTIVATION: Data clustering techniques are an essential component of a good data analysis toolbox. Many current bioinformatics applications are inherently compute-intense and work with very large data sets. Sequential algorithms are inadequate for providing the necessary performance. For this reason, we have created CAMPAIGN, a central resource for data clustering algorithms and tools that are implemented specifically for execution on massively parallel processing architectures. RESULTS: CAMPAIGN is a library of data clustering algorithms and tools, written in ‘C for CUDA’ for Nvidia GPUs. The library provides up to two orders of magnitude speed-up over respective CPU-based clustering algorithms and is intended as an open-source resource. New modules from the community will be accepted into the library and the layout of it is such that it can easily be extended to promising future platforms such as OpenCL. AVAILABILITY: Releases of the CAMPAIGN library are freely available for download under the LGPL from https://simtk.org/home/campaign. Source code can also be obtained through anonymous subversion access as described on https://simtk.org/scm/?group_id=453.
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