Large-Scale Neo-Heterogeneous Programming and Optimization of SNP Detection on Tianhe-2

  • Yingbo Cui
  • Xiangke Liao
  • Shaoliang PengEmail author
  • Yutong Lu
  • Canqun Yang
  • Bingqiang Wang
  • Chengkun Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9137)


SNP detection is a fundamental procedure in genome analysis. A popular SNP detection tool SOAPsnp can take more than one week to analyze one human genome with a 20-fold coverage. To improve the efficiency, we developed mSNP, a parallel version of SOAPsnp. mSNP utilizes CPU cooperated with Intel® Xeon PhiTM for large-scale SNP detection. Firstly, we redesigned the key data structure of SOAPsnp, which significantly reduces the overhead of memory operations. Secondly, we devised a coordinated parallel framework, in which CPU collaborates with Xeon Phi for higher hardware utilization. Thirdly, we proposed a read-based window division strategy to improve throughput and parallel scale on multiple nodes. To the best of our knowledge, mSNP is the first SNP detection tool empowered by Xeon Phi. We achieved a 45x speedup on a single node of Tianhe-2, without any loss in precision. Moreover, mSNP showed promising scalability on 4,096 nodes on Tianhe-2.


SNP detection SOAPsnp Parallelized algorithm Xeon Phi Many Integrated Core (MIC) Coprocessor Tianhe-2 



We would like to thank Mr. Yingrui Li from BGI for providing the source code of SOAPsnp and Dr. Jun Wang from BGI for providing related test data. We would also like to thank Prof. Hans V. Westerhoff from University of Manchester for discussions of the human genome re-sequencing analysis problem and thus improving our own understanding. This work is supported by NSFC Grant 61272056, U1435222, 61133005, 61120106005, 91430218 and 61303191.


  1. 1.
    National Center for Biotechnology Information.
  2. 2.
    Li, R., Li, Y., Fang, X.: SNP detection for massively parallel whole-genome resequencing. Genome Res. 19(6), 1124–1132 (2009)CrossRefGoogle Scholar
  3. 3.
    Short Oligonucleotide Analysis Package Sites.
  4. 4.
    James, J., Reinders, J.: Intel Xeon Phi Coprocessor High Performance Programming. Morgan Kaufmann, Newnes (2013)Google Scholar
  5. 5.
    Liu, X., Smelyanskiy, M., Chow, E., Dubey, P.: Efficient sparse matrix-vector multiplication on x86-based many-core processors. In: Proceedings of the 27th International ACM Conference on International Conference on Supercomputing, pp. 273–282. ACM (2013)Google Scholar
  6. 6.
    Park, J., Bikshandi, G., Vaidyanathan, K., Tang, P.T.P., Dubey, P., Kim, D.: Tera-scale 1D FFT with low-communication algorithm and Intel\(^{\textregistered }\) Xeon Phi\({}^{\rm TM}\) coprocessors. In: Proceedings of SC13: International Conference for High Performance Computing, Networking, Storage and Analysis, p. 34. ACM (2013)Google Scholar
  7. 7.
    Heinecke, A., Vaidyanathan, K., Smelyanskiy, M., Kobotov, A., Dubtsov, R. et al.: Design and implementation of the linpack benchmark for single and multi-node systems based on intel\({}^{\textregistered }\) Xeon Phi coprocessor. In: 2013 IEEE 27th International Symposium on Parallel & Distributed Processing (IPDPS), pp. 126–137. IEEE (2013)Google Scholar
  8. 8.
    Pennycook, S.J., Hughes, C.J., Smelyanskiy, M., Jarvis, S.A.: Exploring SIMD for molecular dynamics, using intel\({}^{\textregistered }\) Xeon\({}^{\textregistered }\) processors and intel\({}^{\textregistered }\) Xeon Phi coprocessors. In: 2013 IEEE 27th International Symposium on Parallel & Distributed Processing (IPDPS), pp. 1085–1097. IEEE (2013)Google Scholar
  9. 9.
    Misra, S., Pamnany, K., Aluru, S.: Parallel mutual information based construction of whole-genome networks on the intel\({}^{\textregistered }\) Xeon PhiTM coprocessor. In: 2014 IEEE 28th International Parallel and Distributed Processing Symposium, pp. 241–250. IEEE (2014)Google Scholar
  10. 10.
    Graham, S.L., Kessler, P.B., McKusick, M.K.: Gprof: a call graph execution profiler. ACM SIGPLAN Not. 39(4), 49–57 (2004)CrossRefGoogle Scholar
  11. 11.
    Wikipedia Sites of VTune.
  12. 12.
    TOP500 Supercomputer Sites.
  13. 13.
    Tang, J., Leunissen, J.A.M., Voorrips, R.E.: HaploSNPer: a web-based allele and SNP de-tection tool. BMC Genet. 9(1), 23 (2008)CrossRefGoogle Scholar
  14. 14.
    Dereeper, A., Nicolas, S., Le Cunff, L.: SNiPlay: a web-based tool for detection, management and analysis of SNPs. Application to grapevine diversity projects. BMC Bioinform. 12(1), 134 (2011)CrossRefGoogle Scholar
  15. 15.
    Tang, J., Vosman, B., Voorrips, R.E.: QualitySNP: a pipeline for detecting single nucleotide polymorphisms and insertions/deletions in EST data from diploid and polyploid species. BMC Bioinform. 7(1), 438 (2006)CrossRefGoogle Scholar
  16. 16.
    Li, H., Handsaker, B., Wysoker, A.: The sequence alignment/map format and SAMtools. Bioinformatics 25(16), 2078–2079 (2009)CrossRefGoogle Scholar
  17. 17.
    DePristo, M.A., Banks, E., Poplin, R.: A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nature Genet. 43(5), 491–498 (2011)CrossRefGoogle Scholar
  18. 18.
    Raczy, C., Petrovski, R., Saunders, C.T.: Isaac: ultra-fast whole-genome secondary analysis on Illumina sequencing platforms. Bioinformatics 29, 2041–2043 (2013). btt314CrossRefGoogle Scholar
  19. 19.
    Langmead, B., Schatz, M.C., Lin, J.: Searching for SNPs with cloud computing. Genome Biol. 10(11), R134 (2009)CrossRefGoogle Scholar
  20. 20.
    Shvachko, K., Kuang, H., Radia, S., The hadoop distributed file system. IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST), pp. 1–10. IEEE (2010)Google Scholar
  21. 21.
    Zhao, S., Prenger, K., Smith, L.: Rainbow: a tool for large-scale whole-genome sequencing data analysis using cloud computing. BMC Genomics 14(1), 425 (2013)CrossRefGoogle Scholar
  22. 22.
    Lu, M., Zhao, J., Luo, Q.: GSNP: a DNA single-nucleotide polymorphism detection system with GPU acceleration. In: 2011 International Conference on Parallel Processing (ICPP), pp. 592–601. IEEE (2011)Google Scholar
  23. 23.
    Kutlu, M., Agrawal, G.: Cluster-based SNP calling on large-scale genome sequencing data. In: 2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid computing (CCGrid), pp. 455–464. IEEE (2013)Google Scholar
  24. 24.
    Cui, Y., Liao, X., Zhu, X.: mBWA: a massively parallel sequence reads aligner. In: Saez-Rodriguez, J., Rocha, M.P., Fdez-Riverola, F., De Paz Santana, J.F. (eds.) PACBB 2014. AISC, vol. 294, pp. 113–120. Springer, Heidelberg (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Yingbo Cui
    • 1
  • Xiangke Liao
    • 1
  • Shaoliang Peng
    • 1
    Email author
  • Yutong Lu
    • 1
  • Canqun Yang
    • 1
  • Bingqiang Wang
    • 2
  • Chengkun Wu
    • 1
  1. 1.School of Computer ScienceNational University of Defense TechnologyChangshaChina
  2. 2.National Supercomputing Center in ShenzhenShenzhenChina

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