Compute Pairwise Manhattan Distance and Pearson Correlation Coefficient of Data Points with GPU

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 progr...

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Vydáno v:SNPD 2009 : 10th ACIS International Conference on Software Engineering, Artificial Intelligences, Networking and Parallel Distributed Computing : proceedings : 27-29 May 2009 Daegu, Korea s. 501 - 506
Hlavní autoři: Dar-Jen Chang, Desoky, A.H., Ming Ouyang, Rouchka, E.C.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.05.2009
Témata:
Bioinformatics
Central Processing Unit
Computer networks
Concurrent computing
Data analysis
Distributed computing
DNA
Graphics
hierarchical clustering
Parallel and distributed computation
Power engineering computing
Sequences
similarity and dissimilarity metrics
ISBN:0769536425, 9780769536422
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Shrnutí: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.
ISBN:0769536425
9780769536422
DOI:10.1109/SNPD.2009.34