High Performance Biological Pairwise Sequence Alignment: FPGA versus GPU versus Cell BE versus GPP
This paper explores the pros and cons of reconfigurable computing in the form of FPGAs for high performance efficient computing. In particular, the paper presents the results of a comparative study between three different acceleration technologies, namely, Field Programmable Gate Arrays (FPGAs), Gr...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | International Journal of Reconfigurable Computing |
| Online Access: | http://dx.doi.org/10.1155/2012/752910 |
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| _version_ | 1849685138242273280 |
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| author | Khaled Benkrid Ali Akoglu Cheng Ling Yang Song Ying Liu Xiang Tian |
| author_facet | Khaled Benkrid Ali Akoglu Cheng Ling Yang Song Ying Liu Xiang Tian |
| author_sort | Khaled Benkrid |
| collection | DOAJ |
| description | This paper explores the pros and cons of reconfigurable computing in the form of FPGAs for high performance efficient computing. In particular, the paper presents the results of a comparative study between three different acceleration technologies, namely, Field Programmable Gate Arrays (FPGAs), Graphics Processor Units (GPUs), and IBM’s Cell Broadband Engine (Cell BE), in the design and implementation of the widely-used Smith-Waterman pairwise sequence alignment algorithm, with general purpose processors as a base reference implementation. Comparison criteria include speed, energy consumption, and purchase and development costs. The study shows that FPGAs largely outperform all other implementation platforms on performance per watt criterion and perform better than all other platforms on performance per dollar criterion, although by a much smaller margin. Cell BE and GPU come second and third, respectively, on both performance per watt and performance per dollar criteria. In general, in order to outperform other technologies on performance per dollar criterion (using currently available hardware and development tools), FPGAs need to achieve at least two orders of magnitude speed-up compared to general-purpose processors and one order of magnitude speed-up compared to domain-specific technologies such as GPUs. |
| format | Article |
| id | doaj-art-c30b6b5189ee40ba98547bba085819d5 |
| institution | DOAJ |
| issn | 1687-7195 1687-7209 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Reconfigurable Computing |
| spelling | doaj-art-c30b6b5189ee40ba98547bba085819d52025-08-20T03:23:15ZengWileyInternational Journal of Reconfigurable Computing1687-71951687-72092012-01-01201210.1155/2012/752910752910High Performance Biological Pairwise Sequence Alignment: FPGA versus GPU versus Cell BE versus GPPKhaled Benkrid0Ali Akoglu1Cheng Ling2Yang Song3Ying Liu4Xiang Tian5Institute of Integrated Systems, School of Engineering, The University of Edinburgh, Kings Buildings, Mayfield Road, Edinburgh EH9 3JL, UKElectrical and Computer Engineering Department, The University of Arizona, Tucson, AZ 85721-0104, USAInstitute of Integrated Systems, School of Engineering, The University of Edinburgh, Kings Buildings, Mayfield Road, Edinburgh EH9 3JL, UKElectrical and Computer Engineering Department, The University of Arizona, Tucson, AZ 85721-0104, USAInstitute of Integrated Systems, School of Engineering, The University of Edinburgh, Kings Buildings, Mayfield Road, Edinburgh EH9 3JL, UKInstitute of Integrated Systems, School of Engineering, The University of Edinburgh, Kings Buildings, Mayfield Road, Edinburgh EH9 3JL, UKThis paper explores the pros and cons of reconfigurable computing in the form of FPGAs for high performance efficient computing. In particular, the paper presents the results of a comparative study between three different acceleration technologies, namely, Field Programmable Gate Arrays (FPGAs), Graphics Processor Units (GPUs), and IBM’s Cell Broadband Engine (Cell BE), in the design and implementation of the widely-used Smith-Waterman pairwise sequence alignment algorithm, with general purpose processors as a base reference implementation. Comparison criteria include speed, energy consumption, and purchase and development costs. The study shows that FPGAs largely outperform all other implementation platforms on performance per watt criterion and perform better than all other platforms on performance per dollar criterion, although by a much smaller margin. Cell BE and GPU come second and third, respectively, on both performance per watt and performance per dollar criteria. In general, in order to outperform other technologies on performance per dollar criterion (using currently available hardware and development tools), FPGAs need to achieve at least two orders of magnitude speed-up compared to general-purpose processors and one order of magnitude speed-up compared to domain-specific technologies such as GPUs.http://dx.doi.org/10.1155/2012/752910 |
| spellingShingle | Khaled Benkrid Ali Akoglu Cheng Ling Yang Song Ying Liu Xiang Tian High Performance Biological Pairwise Sequence Alignment: FPGA versus GPU versus Cell BE versus GPP International Journal of Reconfigurable Computing |
| title | High Performance Biological Pairwise Sequence Alignment: FPGA versus GPU versus Cell BE versus GPP |
| title_full | High Performance Biological Pairwise Sequence Alignment: FPGA versus GPU versus Cell BE versus GPP |
| title_fullStr | High Performance Biological Pairwise Sequence Alignment: FPGA versus GPU versus Cell BE versus GPP |
| title_full_unstemmed | High Performance Biological Pairwise Sequence Alignment: FPGA versus GPU versus Cell BE versus GPP |
| title_short | High Performance Biological Pairwise Sequence Alignment: FPGA versus GPU versus Cell BE versus GPP |
| title_sort | high performance biological pairwise sequence alignment fpga versus gpu versus cell be versus gpp |
| url | http://dx.doi.org/10.1155/2012/752910 |
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