Godson-T多核处理器分子动力学仿真性能分析与优化

Liu Peng, A. Nakano, Guangming Tan, P. Vashishta, Dongrui Fan, Hao Zhang, R. Kalia, Fenglong Song
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引用次数: 1

摘要

分子动力学(MD)模拟具有广泛的应用,但其不规则的内存访问模式给性能优化带来了挑战。本文提出了一种应用/架构联合研究方法,以提高MD在类Godson-T多核架构上的片上并行性。首先,设计了利用自适应分治框架的预处理,通过软件控制内存的内存层次利用局部性。在此基础上,提出了三种增量优化策略:(1)采用一种新的数据布局,重新组织链表单元数据结构,提高数据局部性;(2)芯片上位置感知并行算法,增强数据重用;(3)采用流水线算法来隐藏共享内存的延迟。在goson - t模拟器上的实验表明,该算法在64核上的并行效率为0.99,并通过FPGA仿真得到了验证。详细的分析表明,利用体系结构特性最大化数据局部性和增强数据重用的优化最有利于可伸缩性。此外,一个简单的性能模型表明,优化方案可能很好地扩展到百亿亿次。某些架构特性对于这些优化至关重要,它们可以指导未来的硬件开发。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance analysis and optimization of molecular dynamics simulation on Godson-T many-core processor
Molecular dynamics (MD) simulation has broad applications, but its irregular memory-access pattern makes performance optimization a challenge. This paper presents a joint application/architecture study to enhance on-chip parallelism of MD on Godson-T -like many-core architecture. First, a preprocessing leveraging an adaptive divide-and-conquer framework is designed to exploit locality through memory hierarchy with software controlled memory. Then we propose three incremental optimization strategies: (1) a novel data-layout to re-organize linked-list cell data structures to improve data locality; (2) an on-chip locality-aware parallel algorithm to enhance data reuse; and (3) a pipelining algorithm to hide latency to shared memory. Experiments on Godson-T simulator exhibit strong-scaling parallel efficiency 0.99 on 64 cores, which is confirmed by an FPGA emulator. Detailed analysis shows that optimizations utilizing architectural features to maximize data locality and to enhance data reuse benefit scalability most. Furthermore, a simple performance model suggests that the optimization scheme is likely to scale well toward exascale. Certain architectural features are found essential for these optimizations, which could guide future hardware developments.
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