Advertisement

Accelerating Turbo Similarity Searching on Multi-cores and Many-cores Platforms

  • Marwah Haitham Al-laila
  • Mohd Norhadri Hilmi
  • Nurul Hashimah Ahamed Hassain Malim
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 362)

Abstract

Turbo Similarity Searching (TSS) is a two phases searching procedure that has been proven by previous works as one of the best searching method on chemical databases. TSS however consumes lots of computation time due to the number of searches and fusion carried out in the second phase of its procedure. Hence, TSS would not be able to cater the increase in the chemical database size due to this limitation. With the emergence of the parallel technology, this research looks into accelerating TSS on the widely-used many-cores i.e. the Graphics Processing Unit (GPU) and multi-cores platform. This would not only solve the computational time issue but also the cost as GPUs can be obtained at a lower cost. Hence, the implementation of TSS will help the medicinal chemist to execute the virtual screening in an accurate and fast manner. This study investigates the best possible method to parallelize TSS via experimentation of three parallel designs; two designs were implemented on GPU platform using the Compute Unified Device Architecture (CUDA) API namely CUDA 1 and CUDA 2 whilst one design was implemented on multi-core platform using OpenMP API. The CUDA 1 design had shown tremendous speedup and low GPU-memory utilization as compared to CUDA 2 design. In general observation, the parallel CUDA 1 was 131 times faster than sequential and 51 times faster than parallel OpenMP. This leads to the conclusion that CUDA 1 design as the best parallel design for TSS.

Keywords

Graphical processing unit (GPU) Compute unified device architecture platform Turbo similarity searching Virtual screening Open multiprocessing 

Notes

Acknowledgments

We would like to thank Dr. Shereena M Arif for providing us the MDDR database.

References

  1. 1.
    Gasteiger, J. (ed.): Handbook of Chemoinformatics (2003)Google Scholar
  2. 2.
    Johnson, M. A., Maggiora, G. M.: Concepts and Applications of Molecular Similarity (1990)Google Scholar
  3. 3.
    Zainal, A., Yusri, N., Malim, N., Arif, S. M.: The influence of similarity measures and fusion rules toward turbo similarity searching. In: International Conference on Electrical Engineering and Informatics (2013)Google Scholar
  4. 4.
    Whittle, M., et al.: Enhancing the effectiveness of virtual screening by fusing nearest neighbor lists: a comparison of similarity coefficients. J. Chem. Inf. Comput. Sci. 44(5), 1840–1848 (2004)CrossRefGoogle Scholar
  5. 5.
    Malim, N., Pei-Chia, Y., Arif, S. M.: New strategy for turbo similarity searching: implementation and testing. In: IEEE 2013 International Conference on Advanced Computer Science and Information Systems (2013)Google Scholar
  6. 6.
    Hert, J., Willett, P., Wilton, D.J.: Comparison of fingerprint-based methods for virtual screening using multiple bioactive reference structures. J. Chem. Inf. Comput. Sci. 44, 1177–1185 (2004)CrossRefGoogle Scholar
  7. 7.
    Whittle, M., Gillett, V.J., Willett, P., Loesel, J.: Analysis of data fusion methods in virtual screening: theoretical model. J. Chem. Inf. Model. 46, 2193–2205 (2006)CrossRefGoogle Scholar
  8. 8.
    Hert, J., et al.: Enhancing the effectiveness of similarity-based virtual screening using nearest-neighbor information. J. Med. Chem. 48, 7049–7054 (2005)CrossRefGoogle Scholar
  9. 9.
    Whittle, M., Gillett, V.J., Willett, P., Loesel, J.: Analysis of data fusion methods in virtual screening: similarity and group fusion. J. Chem. Inf. Model. 46, 2206–2219 (2006)CrossRefGoogle Scholar
  10. 10.
    Dagum, L., Menon, R.: OpenMP: an industry standard API for shared-memory programming. IEEE Comput. Sci. Eng. 5(1), 46–55 (1998)CrossRefGoogle Scholar
  11. 11.
    NVIDIA. Nvidia’s next generation cuda compute architecture: FermiGoogle Scholar
  12. 12.
    Sachdeva, V., Freimuth, D., Mueller, C.: Evaluating the jaccard-tanimoto index on multi-core architectures. Comput. Sci. ICCS 2009, pp 944–953 (2009)Google Scholar
  13. 13.
    Ma, C., Wang, L., Xie, X.-Q.: GPU accelerated chemical similarity calculation for compound library comparison. J. Chem. Inf. Model. 51(7), 1521–1527 (1998)CrossRefGoogle Scholar
  14. 14.
    Yan, X., Gu, Q., Lu, F., Li, J., Xu, J.: GSA: a GPU-accelerated structure similarity algorithm and its application in progressive virtual screening. Mol. Divers. 16(4), 759–769 (2012)CrossRefGoogle Scholar
  15. 15.
    Maggioni, M., Santambrogio, M.D., Liang, J.: GPU-accelerated chemical similarity assessment for large scale databases. Procedia Comput. Sci. 4, 2007–2016 (2011)CrossRefGoogle Scholar
  16. 16.
    Southan, C., Várkonyi, P., Muresan, S.: Quantitative assessment of the expanding complementarity between public and commercial databases of bioactive compounds. J. Cheminform. 1, 10 (2009)CrossRefGoogle Scholar
  17. 17.
    Otoo, E. J., Rotem, D.: Parallel access of out-of-core dense extendible arrays. In: IEEE International Conference on Cluster Computing, pp. 31–40 (2007)Google Scholar
  18. 18.
    Huang, Q., Huang, Z., Werstein, P., Purvis, M.: GPU as a general purpose computing resource. Paper presented at the ninth international conference on parallel and distributed computing, applications and technologies, 2008Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Marwah Haitham Al-laila
    • 1
    • 2
  • Mohd Norhadri Hilmi
    • 2
  • Nurul Hashimah Ahamed Hassain Malim
    • 2
  1. 1.University of MosulMosulIraq
  2. 2.School of Computer SciencesUniversiti Sains MalaysiaPenangMalaysia

Personalised recommendations