Scalability study of molecular dynamics simulation on Godson-T many-core architecture
L Peng and GM Tan and RK Kalia and A Nakano and P Vashishta and DR Fan and H Zhang and FL Song, JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING, 73, 1469-1482 (2013).
Molecular dynamics (MD) simulation has broad applications, and an increasing amount of computing power is needed to satisfy the large scale of the real world simulation. The advent of the many-core paradigm brings unprecedented computing power, but it remains a great challenge to harvest the computing power due to MD's irregular memory-access pattern. To address this challenge, this paper presents a joint application/architecture study to enhance the scalability of MD on Godson-T-like many-core architecture. First, a preprocessing approach leveraging an adaptive divide-and-conquer framework is designed to exploit locality through memory hierarchy with software controlled memory. Then three incremental optimization strategies - a novel data- layout to improve data locality, an on-chip locality-aware parallel algorithm to enhance data reuse, and a pipelining algorithm to hide latency to shared memory - are proposed to enhance on-chip parallelism for Godson-T many-core processor. Experiments on Godson-T simulator exhibit strong-scaling parallel efficiency of 0.99 on 64 cores, which is confirmed by a field-programmable gate array emulator. Also the performance per watt of MD on Godson-T is much higher than MD on a 16-cores Intel core i7 symmetric multiprocessor (SMP) and 26 times higher than MD on an 8-core 64-thread Sun T2 processor. Detailed analysis shows that optimizations utilizing architectural features to maximize data locality and to enhance data reuse benefit scalability most. Furthermore, a hierarchical parallelization scheme is designed to map the MD algorithm to Godson-T many-core cluster and a simple performance model is derived, which 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. Published by Elsevier Inc.
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