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 |
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| Hlavní autoři: | , , , |
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| Jazyk: | angličtina |
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IEEE
01.05.2009
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| ISBN: | 0769536425, 9780769536422 |
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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. |
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| 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 |
| Author_xml | – sequence: 1 surname: Dar-Jen Chang fullname: Dar-Jen Chang organization: Comput. Eng. & Comput. Sci. Dept., Univ. of Louisville, Louisville, KY, USA – sequence: 2 givenname: A.H. surname: Desoky fullname: Desoky, A.H. organization: Comput. Eng. & Comput. Sci. Dept., Univ. of Louisville, Louisville, KY, USA – sequence: 3 surname: Ming Ouyang fullname: Ming Ouyang organization: Comput. Eng. & Comput. Sci. Dept., Univ. of Louisville, Louisville, KY, USA – sequence: 4 givenname: E.C. surname: Rouchka fullname: Rouchka, E.C. organization: Comput. Eng. & Comput. Sci. Dept., Univ. of Louisville, Louisville, KY, USA |
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| PublicationTitle | SNPD 2009 : 10th ACIS International Conference on Software Engineering, Artificial Intelligences, Networking and Parallel Distributed Computing : proceedings : 27-29 May 2009 Daegu, Korea |
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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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