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Parallelizing Biochemical Stochastic Simulations: A Comparison of GPUs and Intel Xeon Phi Processors

  • P. Cazzaniga
  • F. Ferrara
  • M. S. Nobile
  • D. BesozziEmail author
  • G. Mauri
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9251)

Abstract

Stochastic simulations of biochemical reaction networks can be computationally expensive on Central Processing Units (CPUs), especially when a large number of simulations is required to compute the system states distribution or to carry out advanced model analysis. Anyway, since all simulations are independent, parallel architectures can be exploited to reduce the overall running time. The purpose of this work is to compare the computational performance of CPUs, general-purpose Graphics Processing Units (GPUs) and Intel Xeon Phi coprocessors based on the Many Integrated Core (MIC) architecture, for the execution of Gillespie’s Stochastic Simulation Algorithm (SSA). To this aim, we consider an ad hoc implementation of SSA on GPUs, while exploiting the peculiar capability of MICs of reusing existing CPUs source code. We measure the running time needed to execute several batches of simulations, for various biochemical models of increasing size. Our results show that in all tested cases GPUs outperform the other architectures, and that reusing available code with the MICs does not represent a clever strategy to fully leverage Xeon Phi horsepower.

References

  1. 1.
    Aldridge, B.B., Burke, J.M., Lauffenburger, D.A., Sorger, P.K.: Physicochemical modelling of cell signalling pathways. Nat. Cell Biol. 8, 1195–1203 (2006)CrossRefGoogle Scholar
  2. 2.
    Wilkinson, D.: Stochastic modelling for quantitative description of heterogeneous biological systems. Nat. Rev. Genet. 10, 122–133 (2009)CrossRefGoogle Scholar
  3. 3.
    Gillespie, D.T.: Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem. 81, 2340–2361 (1977)CrossRefGoogle Scholar
  4. 4.
    Gillespie, D.T.: A rigorous derivation of the chemical master equation. Physica A 188, 404–425 (1992)CrossRefGoogle Scholar
  5. 5.
    Cao, Y., Gillespie, D.T., Petzold, L.R.: Efficient step size selection for the tau-leaping simulation method. J. Chem. Phys. 124, 044109 (2006)CrossRefGoogle Scholar
  6. 6.
    Nobile, M.S., Cazzaniga, P., Besozzi, D., Pescini, D., Mauri, G.: Reverse engineering of kinetic reaction networks by means of Cartesian Genetic Programming and Particle Swarm Optimization. In: IEEE Congress of Evolutionary Computation, pp. 1594–1601 (2013)Google Scholar
  7. 7.
    Tian, T., Burrage, K.: Parallel implementation of stochastic simulation of large-scale cellular processes. In: 8th International Conference on High-Performance Computing in Asia-Pacific Region, pp. 621–626 (2005)Google Scholar
  8. 8.
    Kent, E., Hoops, S., Mendes, P.: Condor-COPASI: high-throughput computing for biochemical networks. BMC Syst. Biol. 6, 91 (2012)CrossRefGoogle Scholar
  9. 9.
    Macchiarulo, L.: A massively parallel implementation of Gillespie algorithm on FPGAs. In: International Conference of the IEEE on Engineering in Medicine and Biology Society, pp. 1343–1346 (2008)Google Scholar
  10. 10.
    Nobile, M.S., Cazzaniga, P., Besozzi, D., Pescini, D., Mauri, G.: cuTauLeaping: A GPU-powered tau-leaping stochastic simulator for massive parallel analyses of biological systems. PLoS ONE 9, e91963 (2014)CrossRefGoogle Scholar
  11. 11.
    Nobile, M.S., Besozzi, D., Cazzaniga, P., Mauri, G., Pescini, D.: cupSODA: A CUDA-powered simulator of mass-action kinetics. In: Malyshkin, V. (ed.) PaCT 2013. LNCS, vol. 7979, pp. 344–357. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  12. 12.
    Nobile, M.S., Cazzaniga, P., Besozzi, D., Mauri, G.: GPU-accelerated simulations of mass-action kinetics models with cupSODA. J. Supercomput. 69, 17–24 (2014)CrossRefGoogle Scholar
  13. 13.
    Bernaschi, M., Bisson, M., Salvadore, F.: Multi-Kepler GPU vs. multi-Intel MIC for spin systems simulations. Comput. Phys. Commun. 185, 2495–2503 (2014)CrossRefGoogle Scholar
  14. 14.
    Fang, J., Varbanescu, A.L., Imbernon, B., Cecilia, J.M., Perez-Sanchez, H.: Parallel computation of non-bonded interactions in drug discovery: NVidia GPUs vs. Intel Xeon Phi. In: Proceedings of the 2nd International Work-Conference on Bioinformatics and Biomedical Engineering. pp. 579–588 (2014)Google Scholar
  15. 15.
    Halyo, V., LeGresley, P., Lujan, P., Karpusenko, V., Vladimirov, A.: First evaluation of the CPU, GPGPU and MIC architectures for real time particle tracking based on Hough transform at the LHC. J. Instrum. 9, P04005 (2014)CrossRefGoogle Scholar
  16. 16.
    Lyakh, D.I.: An efficient tensor transpose algorithm for multicore CPU, Intel Xeon Phi, and NVidia Tesla GPU. Comput. Phys. Commun. 189, 84–91 (2015)CrossRefGoogle Scholar
  17. 17.
    Shimoda, T., Suzuki, S., Ohue, M., Ishida, T., Akiyama, Y.: Protein-protein docking on hardware accelerators: comparison of GPU and MIC architectures. BMC Syst. Biol. 9, S6 (2015)CrossRefGoogle Scholar
  18. 18.
    Nobile, M.S., Besozzi, D., Cazzaniga, P., Mauri, G., Pescini, D.: A GPU-based multi-swarm PSO method for parameter estimation in stochastic biological systems exploiting discrete-time target series. In: Giacobini, M., Vanneschi, L., Bush, W.S. (eds.) EvoBIO 2012. LNCS, vol. 7246, pp. 74–85. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  19. 19.
    Butcher, J.C.: Numerical Methods for Ordinary Differential Equations. John Wiley & Sons, New York (2003)CrossRefzbMATHGoogle Scholar
  20. 20.
    Nickolls, J., Dally, W.J.: The GPU computing era. Micro IEEE 30, 56–69 (2010)CrossRefGoogle Scholar
  21. 21.
    Farber, R.M.: Topical perspective on massive threading and parallelism. J. Mol. Graph. Model. 30, 82–89 (2011)CrossRefGoogle Scholar
  22. 22.
    Harvey, M.J., Fabritiis, G.D.: A survey of computational molecular science using graphics processing units. WIREs Comput. Mol. Sci. 2, 734–742 (2012)CrossRefGoogle Scholar
  23. 23.
    Cavazzoni, C.: EURORA: a European architecture toward exascale. In: Proceedings of the Future HPC Systems: The Challenges of Power-Constrained Performance, 1, ACM (2012)Google Scholar
  24. 24.
    Komarov, I., D’Souza, R.M., Tapia, J.J.: Accelerating the Gillespie \(\tau \)-leaping method using graphics processing units. PLoS ONE 7, e37370 (2012)CrossRefGoogle Scholar
  25. 25.
    Fang, J., Varbanescu, A.L., Sips, H., Zhang, L., Che, Y., Xu, C.: Benchmarking Intel Xeon Phi to guide kernel design. Technical report, Delft University of Technology, Netherlands (2013)Google Scholar
  26. 26.
    Kraus, J., Pivanti, M., Schifano, S.F., Tripiccione, R., Zanella, M.: Benchmarking GPUswith a parallel Lattice-Boltzmann code. In: IEEE 25th International Symposium on ComputerArchitecture and High Performance Computing, pp. 160–167 (2013)Google Scholar
  27. 27.
    Besozzi, D., Cazzaniga, P., Pescini, D., Mauri, G., Colombo, S., Martegani, E.: The role of feedback control mechanisms on the establishment of oscillatory regimes in the Ras/cAMP/PKA pathway in S. cerevisiae. EURASIP J. Bioinform. Syst. Biol. 2012 (2012)Google Scholar
  28. 28.
    Gunawan, R., Cao, Y., Petzold, L.R., Doyle, F.J.: Sensitivity analysis of discrete stochastic systems. Biophys. J. 88, 2530–2540 (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • P. Cazzaniga
    • 1
  • F. Ferrara
    • 2
  • M. S. Nobile
    • 2
  • D. Besozzi
    • 3
    Email author
  • G. Mauri
    • 2
  1. 1.Dipartimento di Scienze Umane e SocialiUniversità degli Studi di BergamoBergamoItaly
  2. 2.Dipartimento di Informatica, Sistemistica e ComunicazioneUniversità degli Studi di Milano-BicoccaMilanoItaly
  3. 3.Dipartimento di InformaticaUniversità degli Studi di MilanoMilanoItaly

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