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Performance comparison of GPU programming frameworks with the striped Smith-Waterman algorithm

Published:25 March 2012Publication History
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Abstract

This paper evaluates and discusses how different GPU programming frameworks affect the performance obtained from GPU acceleration of the striped smith-waterman algorithm used for biological sequence alignment. A total of 6 GPU implementations of the algorithm on NVIDIA GT200b and AMD RV870 using the CUDA and the OpenCL frameworks are compared to analyze cons and pros of explicit descriptions for architecture specific hardware mechanisms in the code. The evaluation results show that the primitive descriptions with the CUDA are still efficient especially for small size data, while better instruction scheduling and optimizations are carried out by the OpenCL compiler. On the other hand, the combination of OpenCL and RV870 which provides a relatively simple view of the architecture is efficient for the large data size.

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            cover image ACM SIGARCH Computer Architecture News
            ACM SIGARCH Computer Architecture News  Volume 40, Issue 5
            ACM SIGARCH Computer Architecture News/HEART '12
            December 2012
            110 pages
            ISSN:0163-5964
            DOI:10.1145/2460216
            Issue’s Table of Contents

            Copyright © 2012 Authors

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            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 25 March 2012

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