Abstract
Dynamic programming techniques are well-established and employed by various practical algorithms which are used as similarity measures, for instance the edit-distance algorithm or the dynamic time warping algorithm. These algorithms usually operate in iteration-based fashion where new values are computed from values of the previous iteration, thus they cannot be processed by simple data-parallel approaches. In this paper, we propose a way how to utilize computational power of massively parallel GPUs to compute dynamic programming algorithms effectively and efficiently. We address both the problem of computing one distance on large inputs concurrently and the problem of computing large number of distances simultaneously (e.g., when a similarity query is being resolved).
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References
Chacón, A., Marco-Sola, S., Espinosa, A., Ribeca, P., Moure, J.C.: Thread-cooperative, bit-parallel computation of Levenshtein distance on GPU. In: Proceedings of the 28th ACM International Conference on Supercomputing, pp. 103–112. ACM (2014)
Delgado, G., Aporntewan, C.: Data dependency reduction in dynamic programming matrix. In: 2011 Eighth International Joint Conference on Computer Science and Software Engineering (JCSSE), pp. 234–236. IEEE (2011)
Khajeh-Saeed, A., Poole, S., Perot, B.J.: Acceleration of the Smith-Waterman algorithm using single and multiple graphics processors. Journal of Computational Physics 229(11), 4247–4258 (2010)
Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions and reversals. Soviet Physics doklady 10, 707 (1966)
Ligowski, L., Rudnicki, W.: An efficient implementation of Smith Waterman algorithm on GPU using CUDA, for massively parallel scanning of sequence databases. In: IEEE International Symposium on Parallel & Distributed Processing, 2009, IPDPS 2009, pp. 1–8. IEEE (2009)
Liu, Y., Huang, W., Johnson, J., Vaidya, S.: GPU accelerated Smith-Waterman. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2006. LNCS, vol. 3994, pp. 188–195. Springer, Heidelberg (2006)
Liu, Y., Wirawan, A., Schmidt, B.: Cudasw++ 3.0: accelerating Smith-Waterman protein database search by coupling CPU and GPU SIMD instructions. BMC Bioinformatics 14(1), 117 (2013)
Manavski, S.A., Valle, G.: CUDA compatible GPU cards as efficient hardware accelerators for Smith-Waterman sequence alignment. BMC Bioinformatics 9(Suppl 2), S10 (2008)
Müller, M.: Dynamic time warping. In: Information Retrieval for Music and Motion, pp. 69–84 (2007)
Myers, G.: A fast bit-vector algorithm for approximate string matching based on dynamic programming. Journal of the ACM (JACM) 46(3), 395–415 (1999)
NVIDIA: Kepler GPU Architecture. http://www.nvidia.com/object/nvidia-kepler.html. Accessed 10 July 2015
Owens, J.D., Luebke, D., Govindaraju, N., Harris, M., Krüger, J., Lefohn, A.E., Purcell, T.J.: A survey of general-purpose computation on graphics hardware. In: Computer Graphics Forum, vol. 26, pp. 80–113. Wiley Online Library (2007)
Sart, D., Mueen, A., Najjar, W., Keogh, E., Niennattrakul, V.: Accelerating dynamic time warping subsequence search with GPUs and FPGAs. In: 2010 IEEE 10th International Conference on Data Mining (ICDM), pp. 1001–1006. IEEE (2010)
Smith, T.F., Waterman, M.S.: Identification of common molecular subsequences. Journal of Molecular Biology 147(1), 195–197 (1981)
Tomiyama, A., Suda, R.: Automatic parameter optimization for edit distance algorithm on GPU. In: Daydé, M., Marques, O., Nakajima, K. (eds.) VECPAR 2012. LNCS, vol. 7851, pp. 420–434. Springer, Heidelberg (2013)
Ukkonen, E.: Finding approximate patterns in strings. Journal of Algorithms 6(1), 132–137 (1985)
Wagner, R.A., Fischer, M.J.: The string-to-string correction problem. Journal of the ACM (JACM) 21(1), 168–173 (1974)
Xu, K., Cui, W., Hu, Y., Guo, L.: Bit-parallel multiple approximate string matching based on GPU. Procedia Computer Science 17, 523–529 (2013)
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Kruliš, M., Bednárek, D., Brabec, M. (2015). Improving Parallel Processing of Matrix-Based Similarity Measures on Modern GPUs. In: Amato, G., Connor, R., Falchi, F., Gennaro, C. (eds) Similarity Search and Applications. SISAP 2015. Lecture Notes in Computer Science(), vol 9371. Springer, Cham. https://doi.org/10.1007/978-3-319-25087-8_27
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DOI: https://doi.org/10.1007/978-3-319-25087-8_27
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