Abstract
Molecular dynamics (MD) is an important computational tool used to simulate chemical and physical processes at the molecular level. MD simulations focus on the motion of the interaction of numerous molecules or atoms. Most scholars focus on accelerating MD on multicore central processing units (CPUs) or other coprocessors, such as graphics processing unit (GPU) or many integrated cores [1]. However, most researchers disregard CPU resources and merely perceive a CPU as a controller when using coprocessors. Thus, hybrid computing cannot be achieved, thereby resulting in the waste of CPU computing resources. In this study, we propose three strategies to accelerate MD simulation. The first strategy uses Compute Unified Device Architecture [2] to rewrite the MD code and to run applications on a single-core CPU-GPU platform. This strategy can achieve satisfactory performance but does not make use of CPU resources to compute for most research activities. In the second strategy, the CPU is set to compute the pair force of a small part of molecules along with the GPU after accomplishing the task of starting the GPU computation. The third strategy is applicable under the condition that the GPU is shared by numerous MPI processes, each of which uses the GPU separately. In this situation, the performance can be improved.
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References
Intel Xeon Phi Coprocessor. https://software.intel.com/zh-cn/mic-developer. Accessed March 2014
CUDATM 4.0 Programming Guide, NVIDIAR Corporation. http://www.nvidia.com (2011). Accessed November 2013
Salomon-Ferrer, R., Goetz, A.W., Poole, D., Le Grand, S., Walker, R.C.: Routine microsecond molecular dynamics simulations with AMBER - Part II: Particle Mesh Ewald (PME). J. Chem. Theory Comput. 9, 3878–3888 (2013)
Brooks, B.R., Brooks III, C.L., Mackerell, A.D., Nilsson, L., Petrella, R.J., Roux, B., Won, Y., Archontis, G., Bartels, C., Boresch, S., Caflisch, A., Caves, L., Cui, Q., Dinner, A.R., Feig, M., Fischer, S., Gao, J., Hodoscek, M., Im, W., Kuczera, K., Lazaridis, T., Ma, J., Ovchinnikov, V., Paci, E., Pastor, R.W., Post, C.B., Pu, J.Z., Schaefer, M., Tidor, B., Venable, R.M., Woodcock, H.L., Wu, X., Yang, W., York, D.M., Karplus, M.: CHARMM: the biomolecular simulation program. J. Comp. Chem. 30, 1545–1615 (2009)
Lindahl, E., van der Spoel, D., Hess, B., et. al.: http://www.gromacs.org/GPU_acceleration
LAMMPS Molecular Dynamics Simulator. http://lammps.sandia.gov/
Rodrigues, C.I., Hardy, D.J., Stone, J.E.: GPU acceleration of cutoff pair potentials for molecular modeling applications. In: CF 2008 Proceedings of the 5th Conference on Computing Frontiers, vol. 32, no. 4, pp. 273–282. ACM, New York, NY, USA (2008)
Brown, W.M., Wang, P., Plimpton, S.J., Tharrington, A.N.: Implementing molecular dynamics on hybrid high performance computers – short range forces. Comput. Phys. Commun. 182(4), 898–911 (2011)
Xu, J., Ren, Y., Ge, W., Yu, X., Yang, X., Li, J.: Molecular dynamics simulation of macromolecules using graphics processing unit. Comput. Phys. Commun. 182(4), 921–942 (2011)
Friedrichs, M.S., Eastman, P., Vaidyanathan, V., Houston, M.: Accelerating Molecular Dynamic Simulation on Graphics Processing Units, Wiley Inter Science. www.interscience.wiley.com (2009)
Kylasa, S.B., Aktulga, H.M., Grama, A.Y.: PuReMD-GPU: a reactive molecular dynamic simulation package for GPUs. J. Comput. Phys. 272(1), 343–359 (2014)
Wu, Q., Yang, C., Tang, T., Xiao, L.: Exploiting hierarchy parallelism for molecular dynamics on a petascale heterogeneous system. J. Parallel Distrib. Comput. 73(12), 1592–1604 (2013)
Yang, C., Wu, Q., Tang, T., Wang, F., Xue, J.: Programming for scientific computing on peta-scale heterogeneous parallel systems. J. Cent. South Univ. Technol. 20, 1189–1203 (2013)
Zhang, X., Guo, W., Qin, X., Zhao, X.: A highly extensible frame- work for molecule dynamic simulation on GPUs. In: The 2013 International Conference on Parallel and Distributed, Processing Techniques and Applications (PDPTA 2013) (2013)
Hwu, W.W., Stratton, J.A., Stone, S.S.: MCUDA: an efficient implementation of CUDA kernels for multi-core CPUs. In: Amaral, J.N. (ed.) LCPC 2008. LNCS, vol. 5335, pp. 16–30. Springer, Heidelberg (2008)
Linderman, M.D., Collins, J.D., Wang, H., Meng, T.H.: Merge: a programming model for heterogeneous multi-core systems. ACM SIGARCH Comput. Archit. News- ASPLOS 36, 287–296 (2008)
Pennycook, S.J., Hammond, S.D., Jarvis, S.A., Mudalige, G.R.: Performance analysis of a hybrid MPI/CUDA implementation of the NAS-LU benchmark. In: ACM SIG- METRICS Performance Evaluation Review Special Issue on the 1st International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computing Systems (PMBS 2010), vol. 38, no. 4, pp. 23–29. ACM, New York, NY, USA (2011)
Jacobsen, D.A., Thibault, J.C., Senocak, I.: An MPI-CUDA implementation for massively parallel incompressible flow computations on multi-GPU clusters. In: 48th AIAA Aerospace Sciences Meeting and Exhibit, pp. 1–16, Orlando, Florida (2010)
Buck, I., Foley, T., Horn, D., Sugerman, J., Fatahalian, K., Houston, M., Hanrahan, P.: Brook for GPUs: stream computing on graphics hardware. ACM Trans. Graph. 23, 777–786 (2004)
Technical Overview ATI Stream Computing. http://developer.amd.com/gpuassets/StreamComputingOverview.pdf (2009). Accessed November 2011
Cool, M.M., Toit, S.D.: Metaprogramming GPUs with Sh (2004)
Khronos Group. OpenCL. http://www.khronos.org/opencl/ (2008)
Openfoam: The Open Source CFD Toolbox, userguide. http://www.openfoam.org. Accessed August 2010
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This work is partially supported by NSFC (61125201).
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Wang, Y., Dou, Y., Guo, S., Lei, Y., Li, B., Wang, Q. (2016). Accelerating Molecular Dynamics Simulations on Heterogeneous Architecture. In: Xu, W., Xiao, L., Li, J., Zhang, C. (eds) Computer Engineering and Technology. NCCET 2015. Communications in Computer and Information Science, vol 592. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49283-3_12
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DOI: https://doi.org/10.1007/978-3-662-49283-3_12
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