The Community for Technology Leaders
RSS Icon
Issue No.04 - April (2012 vol.23)
pp: 751-759
Fumihiko Ino , Osaka University, Osaka
Yuma Munekawa , Bukkyo University, Kyoto
Kenichi Hagihara , Osaka University, Osaka
In this paper, we propose a Fine Grained Cycle Sharing (FGCS) system capable of exploiting idle Graphics Processing Units (GPUs) for accelerating sequence homology search in local area network environments. Our system exploits short idle periods on GPUs by running small parts of guest programs such that each part can be completed within hundreds of milliseconds. To detect such short idle periods from the pool of registered resources, our system continuously monitors keyboard and mouse activities via event handlers rather than waiting for a screensaver, as is typically deployed in existing systems. Our system also divides guest tasks into small parts according to a performance model that estimates execution times of the parts. This task division strategy minimizes any disruption to the owners of the GPU resources. Experimental results show that our FGCS system running on two nondedicated GPUs achieves 111-116 percent of the throughput achieved by a single dedicated GPU. Furthermore, our system provides over two times the throughput of a screensaver-based system. We also show that the idle periods detected by our system constitute half of the system uptime. We believe that the GPUs hidden and often unused in office environments provide a powerful solution to sequence homology search.
Distributed systems, performance of systems, fine grained cycle sharing, homology search, Smith-Waterman algorithm, GPGPU, CUDA.
Fumihiko Ino, Yuma Munekawa, Kenichi Hagihara, "Sequence Homology Search Using Fine Grained Cycle Sharing of Idle GPUs", IEEE Transactions on Parallel & Distributed Systems, vol.23, no. 4, pp. 751-759, April 2012, doi:10.1109/TPDS.2011.239
[1] T.F. Smith and M.S. Waterman, "Identification of Common Molecular Subsequences," J. Molecular Biology, vol. 147, pp. 195-197, 1981.
[2] S.F. Altschul, W. Gish, W. Miller, E.W. Myers, and D.J. Lipman, "Basic Local Alignment Search Tool," J. Molecular Biology, vol. 215, no. 3, pp. 403-410, Oct. 1990.
[3] W.R. Pearson, "Searching Protein Sequence Libraries: Comparison of the Sensitivity and Selectivity of the Smith-Waterman and FASTA Algorithms," Genomics, vol. 11, no. 3, pp. 635-650, Nov. 1991.
[4] R. Bellman, Dynamic Programming. Princeton Univ. Press, 1957.
[5] T. Rognes and E. Seeberg, "Six-Fold Speed-Up of Smith-Waterman Sequence Database Searches Using Parallel Processing on Common Microprocessors," Bioinformatics, vol. 16, no. 8, pp. 699-706, Aug. 2000.
[6] A. Bairoch and R. Apweiler, "The SWISS-PROT Protein Sequence Data Bank and Its Supplement TrEMBL," Nucleic Acids Research, vol. 25, no. 1, pp. 31-36, Jan. 1997.
[7] Y. Liu, D.L. Maskell, and B. Schmidt, "CUDASW++: Optimizing Smith-Waterman Sequence Database Searches for CUDA-Enabled Graphics Processing Units," BMC Research Notes, vol. 2, no. 1, p. 73, May 2009.
[8] Y. Munekawa, F. Ino, and K. Hagihara, "Accelerating Smith-Waterman Algorithm for Biological Database Search on CUDA-Compatible GPUs," IEICE Trans. Information and Systems, vol. E93-D, no. 6, pp. 1479-1488, June 2010.
[9] A. Szalkowski, C. Ledergerber, P. Krähenbühl, and C. Dessimoz, "SWPS3—Fast Multi-Threaded Vectorized Smith-Waterman for IBM Cell/B.E. and x86/SSE2," BMC Research Notes, vol. 1, no. 1, p. 107, Oct. 2008.
[10] M. Farrar, "Optimizing Smith-Waterman for the Cell Broadband Engine," p. 5, SW-CellBE.pdf, 2008.
[11] NVIDIA Corporation, "CUDA Programming Guide Version 2.3," http://developer.nvidia.comcuda/, July 2009.
[12] E. Lindholm, J. Nickolls, S. Oberman, and J. Montrym, "NVIDIA Tesla: A Unified Graphics and Computing Architecture," IEEE, Micro, vol. 28, no. 2, pp. 39-55, Mar. 2008.
[13] A. Singh, C. Chen, W. Liu, W. Mitchell, and B. Schmidt, "A Hybrid Computational Grid Architecture for Comparative Genomics," IEEE Trans. Information Technology in Biomedicine, vol. 12, no. 2, pp. 218-225, Mar. 2008.
[14] D.P. Anderson, "BOINC: A System for Public-Resource Computing and Storage," Proc. IEEE/ACM Fifth Int'l Workshop Grid Computing (GRID '04), pp. 4-10, Nov. 2004.
[15] Y. Kotani, F. Ino, and K. Hagihara, "A Resource Selection System for Cycle Stealing in GPU Grids," J. Grid Computing, vol. 6, no. 4, pp. 399-416, Dec. 2008.
[16] F. Ino, Y. Kotani, Y. Munekawa, and K. Hagihara, "Harnessing the Power of Idle GPUs for Acceleration of Biological Sequence Alignment," Parallel Processing Letters, vol. 19, no. 4, pp. 513-533, Dec. 2009.
[17] Tokyo Inst. of Tech nology, "TSUBAME2," http://www.gsic. /, 2010.
[18] W. chun Feng and K. Cameron, "The Green500 List: Encouraging Sustainable Supercomputing," Computer, vol. 40, no. 12, pp. 50-55, http:/, Dec. 2007.
[19] H. Meuer, E. Strohmaier, H.D. Simon, and J. Dongarra, "TOP500 Supercomputing Sites," http:/, Nov. 2010.
[20] W. Liu, B. Schmidt, G. Voss, and W. Müller-Wittig, "Streaming Algorithms for Biological Sequence Alignment on GPUs," IEEE Trans. Parallel and Distributed Systems, vol. 18, no. 9, pp. 1270-1281, Sept. 2007.
[21] D. Shreiner, M. Woo, J. Neider, and T. Davis, OpenGL Programming Guide, fifth ed. Addison-Wesley, Aug. 2005.
[22] R. Raman, M. Livny, and M. Solomon, "Policy Driven Heterogeneous Resource Co-Allocation with Gangmatching," Proc. IEEE 12th Int'l Symp. High Performance Distributed Computing (HPDC '03), pp. 80-89, June 2003.
[23] J.R. Dabrowski and E.V. Munson, "Is 100 Milliseconds Too Fast?" Proc. CHI '01 Extended Abstracts on Human Factors in Computing Systems, pp. 317-318, Mar. 2001.
[24] Beepa Pty Ltd., "Fraps: Real-Time Video Capture and Benchmarking," http:/, 2011.
32 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool