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2010 11th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (2009)
Catholic University of Daegu, Daegu, Korea
May 27, 2009 to May 29, 2009
ISBN: 978-0-7695-3642-2
pp: 501-506
ABSTRACT
Graphics processing units (GPUs) are powerful computational devices tailored towards the needs of the 3-D gaming industry for high-performance, real-time graphics engines. Nvidia Corporation released a new generation of GPUs designed for general-purpose computing in 2006, and it released a GPU programming language called CUDA in 2007. The DNA microarray technology is a high throughput tool for assaying mRNA abundance in cell samples. In data analysis, scientists often apply hierarchical clustering of the genes, where a fundamental operation is to calculate all pairwise distances. If there are n genes, it takes O(n^2) time. In this work, GPUs and the CUDA language are used to calculate pairwise distances. For Manhattan distance, GPU/CUDA achieves a 40 to 90 times speed-up compared to the central processing unit implementation; for Pearson correlation coefficient, the speed-up is 28 to 38 times.
INDEX TERMS
Parallel and distributed computation, hierarchical clustering, similarity and dissimilarity metrics
CITATION
Dar-Jen Chang, Eric C. Rouchka, Ming Ouyang, Ahmed H. Desoky, "Compute Pairwise Manhattan Distance and Pearson Correlation Coefficient of Data Points with GPU", 2010 11th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, vol. 00, no. , pp. 501-506, 2009, doi:10.1109/SNPD.2009.34
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