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BMCBI
2010

A highly efficient multi-core algorithm for clustering extremely large datasets

9 years 2 months ago
A highly efficient multi-core algorithm for clustering extremely large datasets
Background: In recent years, the demand for computational power in computational biology has increased due to rapidly growing data sets from microarray and other high-throughput technologies. This demand is likely to increase. Standard algorithms for analyzing data, such as cluster algorithms, need to be parallelized for fast processing. Unfortunately, most approaches for parallelizing algorithms largely rely on network communication protocols connecting and requiring multiple computers. One answer to this problem is to utilize the intrinsic capabilities in current multi-core hardware to distribute the tasks among the different cores of one computer. Results: We introduce a multi-core parallelization of the k-means and k-modes cluster algorithms based on the design principles of transactional memory for clustering gene expression microarray type data and categorial SNP data. Our new shared memory parallel algorithms show to be highly efficient. We demonstrate their computational power...
Johann M. Kraus, Hans A. Kestler
Added 08 Dec 2010
Updated 08 Dec 2010
Type Journal
Year 2010
Where BMCBI
Authors Johann M. Kraus, Hans A. Kestler
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