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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Published in:SNPD 2009 : 10th ACIS International Conference on Software Engineering, Artificial Intelligences, Networking and Parallel Distributed Computing : proceedings : 27-29 May 2009 Daegu, Korea pp. 501 - 506
Main Authors: Dar-Jen Chang, Desoky, A.H., Ming Ouyang, Rouchka, E.C.
Format: Conference Proceeding
Language:English
Published: IEEE 01.05.2009
Subjects:
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
Online Access:Get full text
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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.
AbstractList 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.
Author Desoky, A.H.
Rouchka, E.C.
Ming Ouyang
Dar-Jen Chang
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  surname: Rouchka
  fullname: Rouchka, E.C.
  organization: Comput. Eng. & Comput. Sci. Dept., Univ. of Louisville, Louisville, KY, USA
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SubjectTerms 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
Title Compute Pairwise Manhattan Distance and Pearson Correlation Coefficient of Data Points with GPU
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