Abstract
Sliding window sums are widely used for string indexing, hashing, time series analysis and machine learning. New vector algorithms which utilize the advanced vector extension (AVX) instructions available on modern processors, or the parallel compute units on GPUs and FPGAs, would provide a significant performance boost.
We develop a generic vectorized sliding sum algorithm with speedup for window size w and number of processors P is O(P/w) for a generic sliding sum. For a sum with commutative operator the speedup is improved to O(P/log(w)). Implementing the algorithm for the bioinformatics application of minimizer based k-mer table generation using AVX instructions, we obtain a speedup of over 5\(\times \).
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References
Altschul, S.F., Gish, W., Miller, W., Myers, E.W., Lipman, D.J.: Basic local alignment search tool. J. Mol. Biol. 215(3), 403–410 (1990)
Basak, A., Venkataraman, K., Murphy, R., Singh, M.: Stream Analytics with Microsoft Azure: Real-Time Data Processing for Quick Insights Using Azure Stream Analytics. Packt Publishing Ltd., Birmingham (2017)
Blelloch, G.E.: Prefix sums and their applications. In: Synthesis of Parallel Algorithms. Morgan Kaufmann (1993)
Harris, R.S.: Improved pairwise alignment of genomic DNA. ProQuest (2007)
Kotu, V., Deshpande, B.: Data Science: Concepts and Practice. Morgan Kaufmann, Burlington (2018)
Li, H.: Minimap and miniasm: fast mapping and de novo assembly for noisy long sequences. Bioinformatics 32(14), 2103–2110 (2016)
Li, H.: Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 1, 7 (2018)
Roberts, M., Hayes, W., Hunt, B.R., Mount, S.M., Yorke, J.A.: Reducing storage requirements for biological sequence comparison. Bioinformatics 20(18), 3363–3369 (2004)
Smith, T.F., Waterman, M.S.: Identification of common molecular subsequences. J. Mol. Biol. 147(1), 195–197 (1981)
Sović, I., Šikić, M., Wilm, A., Fenlon, S.N., Chen, S., Nagarajan, N.: Fast and sensitive mapping of nanopore sequencing reads with graphmap. Nat. Commun. 7, 11307 (2016)
Tangwongsan, K., Hirzel, M., Schneider, S.: Constant-time sliding window aggregation. IBM, IBM Research Report RC25574 (WAT1511-030) (2015)
Turakhia, Y., Bejerano, G., Dally, W.J.: Darwin: a genomics co-processor provides up to 15,000 x acceleration on long read assembly. In: Proceedings of the Twenty-Third International Conference on Architectural Support for Programming Languages and Operating Systems, pp. 199–213. ACM (2018)
Wood, D.E., Salzberg, S.L.: Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol. 15(3), R46 (2014)
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Snytsar, R., Turakhia, Y. (2020). Parallel Approach to Sliding Window Sums. In: Wen, S., Zomaya, A., Yang, L.T. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2019. Lecture Notes in Computer Science(), vol 11945. Springer, Cham. https://doi.org/10.1007/978-3-030-38961-1_3
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DOI: https://doi.org/10.1007/978-3-030-38961-1_3
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