First Experiences Accelerating Smith-Waterman on Intel’s Knights Landing Processor
The well-known Smith-Waterman (SW) algorithm is the most commonly used method for local sequence alignments. However, SW is very computationally demanding for large protein databases. There are several implementations that take advantage of parallel capacities on many-cores, FPGAs or GPUs, in order to increase the alignment throughtput. In this paper, we have explored SW acceleration on Intel KNL processor. The novelty of this architecture requires the revision of previous programming and optimization techniques on many-core architectures. To the best of authors knowledge, this is the first KNL architecture assessment for SW algorithm. Our evaluation, using the renowned Environmental NR database as benchmark, has shown that multi-threading and SIMD exploitation showed competitive performance (351 GCUPS) in comparison with other implementations.
KeywordsBioinformatics Smith-Waterman Xeon-Phi Intel-KNL SIMD
This work has been partially supported by Spanish government through research contract TIN2015-65277-R and CAPAP-H6 network (TIN2016-81840-REDT).
- 1.Asai, R.: MCDRAM as high-bandidth memory (HBM) in knights landing processors: developer’s guide (2016). https://goparallel.sourceforge.net/wp-content/uploads/2016/05/Colfax_KNL_MCDRAM_Guide.pdf
- 4.Isa, M., Benkrid, K., Clayton, T., Ling, C., Erdogan, A.: An FPGA-based parameterised and scalable optimal solutions for pairwise biological sequence analysis. In: 2011 NASA/ESA Conference on Adaptive Hardware and Systems (AHS), pp. 344–351, June 2011Google Scholar
- 5.Lan, H., Liu, W., Schmidt, B., Wang, B.: Accelerating large-scale biological database search on xeon phi-based neo-heterogeneous architectures. In: 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 503–510, November 2015Google Scholar
- 8.Liu, Y., Schmidt, B.: Swaphi: Smith-waterman protein database search on xeon phi coprocessors. In: 25th IEEE International Conference on Application-Specific Systems, Architectures and Processors (ASAP 2014) (2014)Google Scholar
- 9.Mount, D.W.: Bioinformatics: Sequence and Genome Analysis. Mount Bioinformatics. Cold Spring Harbor Laboratory Press, New York (2004)Google Scholar
- 10.Reinders, J., Jeffers, J., Sodani, A.: Intel Xeon Phi Processor High Performance Programming Knights, Landing edn. Morgan Kaufmann Publishers Inc., Boston (2016)Google Scholar
- 13.Rucci, E., Garcia, C., Botella, G., De Giusti, A., Naiouf, M., Prieto-Matas, M.: An energy-aware performance analysis of SWIMM: Smith Waterman implementation on Intel’s Multicore and Manycore architectures. Concurr. Comput. Pract. Exp. 27(18), 5517–5537 (2015). http://dx.doi.org/10.1002/cpe.3598 CrossRefGoogle Scholar
- 14.Rucci, E., Garcia, C., Botella, G., De Giusti, A., Naiouf, M., Prieto-Matas, M.: OSWALD: OpenCL Smith-Waterman algorithm on altera FPGA for large protein databases. Int. J. High Perform. Comput. Appl. (2016). http://dx.doi.org/10.1177/1094342016654215