Abstract
GROMACS is a widely used package for biomolecular simulation, and over the last two decades it has evolved from small-scale efficiency to advanced heterogeneous acceleration and multi-level parallelism targeting some of the largest supercomputers in the world. Here, we describe some of the ways we have been able to realize this through the use of parallelization on all levels, combined with a constant focus on absolute performance. Release 4.6 of GROMACS uses SIMD acceleration on a wide range of architectures, GPU offloading acceleration, and both OpenMP and MPI parallelism within and between nodes, respectively. The recent work on acceleration made it necessary to revisit the fundamental algorithms of molecular simulation, including the concept of neighborsearching, and we discuss the present and future challenges we see for exascale simulation - in particular a very fine-grained task parallelism. We also discuss the software management, code peer review and continuous integration testing required for a project of this complexity.
The authors ‘S. Páll and M.J. Abraham’ contributed equally.
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Notes
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Amdahl’s law gives a model for the expected (and maximum) speedup of a program when parallelized over multiple processors with respect to the serial version. It states that the achievable speedup is limited by the sequential part of the program.
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At the time of that decision, sharing a GPU among multiple MPI ranks was inefficient, so the only efficient way to use multiple cores in a node was with OpenMP within a rank. This constraint has since been relaxed.
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References
Intel Thread Building Blocks. https://www.threadingbuildingblocks.org
Abraham, M.J., Gready, J.E.: Optimization of parameters for molecular dynamics simulation using smooth particle-mesh Ewald in GROMACS 4.5. J. Comput. Chem. 32(9), 2031–2040 (2011)
Amdahl, G.M.: Validity of the single processor approach to achieving large scale computing capabilities. In: Proceedings of the Spring Joint Computer Conference, AFIPS 1967 (Spring), pp. 483–485. ACM, New York, NY, USA (1967). http://doi.acm.org/10.1145/1465482.1465560
Anderson, J.A., Lorenz, C.D., Travesset, A.: General purpose molecular dynamics simulations fully implemented on graphics processing units. J. Comput. Phys. 227, 5324–5329 (2008)
Andoh, Y., Yoshii, N., Fujimoto, K., Mizutani, K., Kojima, H., Yamada, A., Okazaki, S., Kawaguchi, K., Nagao, H., Iwahashi, K., Mizutani, F., Minami, K., Ichikawa, S.I., Komatsu, H., Ishizuki, S., Takeda, Y., Fukushima, M.: MODYLAS: a highly parallelized general-purpose molecular dynamics simulation program for large-scale systems with long-range forces calculated by Fast Multipole Method (FMM) and highly scalable fine-grained new parallel processing algorithms. J. Chem. Theory Comput. 9(7), 3201–3209 (2013). http://pubs.acs.org/doi/abs/10.1021/ct400203a
Arnold, A., Fahrenberger, F., Holm, C., Lenz, O., Bolten, M., Dachsel, H., Halver, R., Kabadshow, I., Gähler, F., Heber, F., Iseringhausen, J., Hofmann, M., Pippig, M., Potts, D., Sutmann, G.: Comparison of scalable fast methods for long-range interactions. Phys. Rev. E 88, 063308 (2013). http://link.aps.org/doi/10.1103/PhysRevE.88.063308
Bowers, K.J., Dror, R.O., Shaw, D.E.: Overview of neutral territory methods for the parallel evaluation of pairwise particle interactions. J. Phys. Conf. Ser. 16(1), 300 (2005). http://stacks.iop.org/1742-6596/16/i=1/a=041
Bowers, K.J., Dror, R.O., Shaw, D.E.: Zonal methods for the parallel execution of range-limited n-body simulations. J. Comput. Phys. 221(1), 303–329 (2007). http://dx.doi.org/10.1016/j.jcp.2006.06.014
Brown, W.M., Wang, P., Plimpton, S.J., Tharrington, A.N.: Implementing molecular dynamics on hybrid high performance computers - short range forces. Comp. Phys. Comm. 182, 898–911 (2011)
Eastman, P., Pande, V.S.: Efficient nonbonded interactions for molecular dynamics on a graphics processing unit. J. Comput. Chem. 31, 1268–1272 (2010)
Eleftheriou, M., Moreira, J.E., Fitch, B.G., Germain, R.S.: A volumetric FFT for BlueGene/L. In: Pinkston, T.M., Prasanna, V.K. (eds.) HiPC 2003. LNCS (LNAI), vol. 2913, pp. 194–203. Springer, Heidelberg (2003)
Essmann, U., Perera, L., Berkowitz, M.L., Darden, T., Lee, H., Pedersen, L.G.: A smooth particle mesh Ewald method. J. Chem. Phys. 103(19), 8577–8593 (1995)
Faradjian, A., Elber, R.: Computing time scales from reaction coordinates by milestoning. J. Chem. Phys. 120, 10880–10889 (2004)
Hess, B., Kutzner, C., van der Spoel, D., Lindahl, E.: GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. J. Chem. Theor. Comput. 4(3), 435–447 (2008)
Humphrey, W., Dalke, A., Schulten, K.: VMD: visual molecular dynamics. J. Mol. Graph. 14(1), 33–38 (1996)
Jagode, H.: Fourier transforms for the BlueGene/L communication network. Ph.D. thesis, The University of Edinburgh, Edinburgh, UK (2005)
Páll, S., Hess, B.: A flexible algorithm for calculating pair interactions on SIMD architectures. Comput. Phys. Commun. 184(12), 2641–2650 (2013). http://www.sciencedirect.com/science/article/pii/S0010465513001975
Phillips, J.C., Braun, R., Wang, W., Gumbart, J., Tajkhorshid, E., Villa, E., Chipot, C., Skeel, R.D., Kale, L., Schulten, K.: Scalable molecular dynamics with NAMD. J. Comput. Chem. 26, 1781–1802 (2005)
Pronk, S., Larsson, P., Pouya, I., Bowman, G.R., Haque, I.S., Beauchamp, K., Hess, B., Pande, V.S., Kasson, P.M., Lindahl, E.: Copernicus: A new paradigm for parallel adaptive molecular dynamics. In: Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2011, pp. 60:1–60:10. ACM, New York, NY, USA (2011) http://doi.acm.org/10.1145/2063384.2063465
Pronk, S., Páll, S., Schulz, R., Larsson, P., Bjelkmar, P., Apostolov, R., Shirts, M.R., Smith, J.C., Kasson, P.M., van der Spoel, D., Hess, B., Lindahl, E.: GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics 29(7), 845–854 (2013). http://bioinformatics.oxfordjournals.org/content/29/7/845.abstract
Reyes, R., Turner, A., Hess, B.: Introducing SHMEM into the GROMACS molecular dynamics application: experience and results. In: Weiland, M., Jackson, A., Johnson, N. (eds.) Proceedings of the 7th International Conference on PGAS Programming Models. The University of Edinburgh, October 2013. http://www.pgas2013.org.uk/sites/default/files/pgas2013proceedings.pdf
Schütte, C., Winkelmann, S., Hartmann, C.: Optimal control of molecular dynamics using Markov state models. Math. Program. (Series B) 134, 259–282 (2012)
Shirts, M., Pande, V.S.: Screen savers of the world unite!. Science 290(5498), 1903–1904 (2000). http://www.sciencemag.org/content/290/5498/1903.short
Sugita, Y., Okamoto, Y.: Replica-exchange molecular dynamics method for protein folding. Chem. Phys. Lett. 314, 141–151 (1999)
Verlet, L.: Computer “Experiments” on classical fluids. I. Thermodynamical properties of Lennard-Jones molecules. Phys. Rev. 159, 98–103 (1967). http://link.aps.org/doi/10.1103/PhysRev.159.98
Wilson, G., Aruliah, D.A., Brown, C.T., Chue Hong, N.P., Davis, M., Guy, R.T., Haddock, S.H.D., Huff, K.D., Mitchell, I.M., Plumbley, M.D., Waugh, B., White, E.P., Wilson, P.: Best practices for scientific computing. PLoS Biol 12(1), e1001745 (2014). http://dx.doi.org/10.1371/journal.pbio.1001745
Yokota, R., Barba, L.A.: A tuned and scalable fast multipole method as a preeminent algorithm for exascale systems. Int. J. High Perform. Comput. Appl. 26(4), 337–346 (2012). http://hpc.sagepub.com/content/26/4/337.abstract
Acknowledgments
This work was supported by the European research Council (258980, BH), the Swedish e-Science research center, and the EU FP7 CRESTA project (287703). Computational resources were provided by the Swedish National Infrastructure for computing (grants SNIC 025/12-32 & 2013-26/24) and the Leibniz Supercomputing Center.
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Páll, S., Abraham, M.J., Kutzner, C., Hess, B., Lindahl, E. (2015). Tackling Exascale Software Challenges in Molecular Dynamics Simulations with GROMACS. In: Markidis, S., Laure, E. (eds) Solving Software Challenges for Exascale. EASC 2014. Lecture Notes in Computer Science(), vol 8759. Springer, Cham. https://doi.org/10.1007/978-3-319-15976-8_1
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