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A Fast Parallel Implementation of Molecular Dynamics with the Morse Potential on a Heterogeneous Petascale Supercomputer

机译:在异构Petascale超级计算机上具有莫尔斯电势的分子动力学的快速并行实现

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Molecular Dynamics (MD) simulations have been widely used in the study of macromolecules. To ensure an acceptable level of statistical accuracy relatively large number of particles are needed, which calls for high performance implementations of MD. These days heterogeneous systems, with their high performance potential, low power consumption, and high price-performance ratio, offer a viable alternative for running MD simulations. In this paper we introduce a fast parallel implementation of MD simulation with the Morse potential on Tianhe-1A, a petascale heterogeneous supercomputer. Our code achieves a speedup of 3.6$times$ on one NVIDIA Tesla M2050 GPU (containing 14 Streaming Multiprocessors) compared to a 2.93GHz six-core Intel Xeon X5670 CPU. In addition, our code runs faster on 1024 compute nodes (with two CPUs and one GPU inside a node) than on 4096 GPU-excluded nodes, effectively rendering one GPU more efficient than six six-core CPUs. Our work shows that large-scale MD simulations can benefit enormously from GPU acceleration in petascale supercomputing platforms. Our performance results are achieved by using (1) a patch-cell design to exploit parallelism across the simulation domain, (2) a new GPU kernel developed by taking advantage of Newton's Third Law to reduce redundant force computation on GPUs, (3) two optimization methods including a dynamic load balancing strategy that adjusts the workload, and a communication overlapping method to overlap the communications between CPUs and GPUs.
机译:分子动力学(MD)模拟已广泛用于大分子的研究。为了确保可接受的统计精度水平,需要相对大量的粒子,这需要MD的高性能实现。如今,异构系统具有高性能,低功耗和高性价比的特性,为运行MD仿真提供了可行的替代方案。在本文中,我们介绍了在Petascale异构超级计算机Tianhe-1A上具有Morse势的MD仿真的快速并行实现。与2.93GHz的六核Intel Xeon X5670 CPU相比,我们的代码在一个NVIDIA Tesla M2050 GPU(包含14个流式多处理器)上的速度提高了3.6倍。此外,我们的代码在1024个计算节点上运行(一个节点内有两个CPU和一个GPU)比在4096个GPU除外的节点上运行更快,从而使一个GPU的效率比六个六核CPU高。我们的工作表明,大规模MD仿真可以从petascale超级计算平台中的GPU加速中受益匪浅。我们的性能结果是通过以下方式获得的:(1)修补程序单元设计在整个仿真域中利用并行性;(2)利用牛顿第三定律开发的新GPU内核,以减少GPU上的冗余力计算;(3)两个优化方法包括可调整工作负载的动态负载平衡策略,以及用于使CPU和GPU之间的通信重叠的通信重叠方法。

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