神威太湖之光硅晶应用的OpenACC +线程协同优化

IF 2 4区计算机科学 Q2 COMPUTER SCIENCE, THEORY & METHODS

Parallel Computing Pub Date : 2022-07-01 DOI:10.1016/j.parco.2022.102893

Jianguo Liang , Rong Hua , Wenqiang Zhu , Yuxi Ye , You Fu , Hao Zhang

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引用次数: 1

摘要

利用基于分子动力学(MD)的硅晶体应用程序模拟晶体的导热性，采用Tersoff势来模拟硅晶体的运动轨迹。基于OpenACC版本，为了更好地解决离散内存读写依赖问题，提出了任务流水线优化和区间图着色调度方法。同时，通过SIMD命令对cpe上的部分代码进行矢量化，进一步提高了计算性能。经过OpenACC+Athread的协同开发，性能比OpenACC版本提升了16.68倍，实现了2.34倍的提速。此外，应用程序扩展到66,560个核，可以模拟268,435,456个硅原子的反应。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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OpenACC + Athread collaborative optimization of Silicon-Crystal application on Sunway TaihuLight

The Silicon-Crystal application based on molecular dynamics (MD) is used to simulate the thermal conductivity of the crystal, which adopts the Tersoff potential to simulate the trajectory of the silicon crystal. Based on the OpenACC version, to better solve the problem of discrete memory access and write dependency, task pipeline optimization and the interval graph coloring scheduling method are proposed. Also, the part of codes on CPEs is vectorized by the SIMD command to further improve the computational performance. After the collaborative development of OpenACC+Athread, the performance has been improved by 16.68 times and achieves 2.34X speedup compared with the OpenACC version. Moreover, the application is expanded to 66,560 cores and can simulate reactions of 268,435,456 silicon atoms.

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来源期刊

Parallel Computing 工程技术-计算机：理论方法

CiteScore

3.50

自引率

7.10%

发文量

审稿时长

4.5 months

期刊介绍： Parallel Computing is an international journal presenting the practical use of parallel computer systems, including high performance architecture, system software, programming systems and tools, and applications. Within this context the journal covers all aspects of high-end parallel computing from single homogeneous or heterogenous computing nodes to large-scale multi-node systems. Parallel Computing features original research work and review articles as well as novel or illustrative accounts of application experience with (and techniques for) the use of parallel computers. We also welcome studies reproducing prior publications that either confirm or disprove prior published results. Particular technical areas of interest include, but are not limited to: -System software for parallel computer systems including programming languages (new languages as well as compilation techniques), operating systems (including middleware), and resource management (scheduling and load-balancing). -Enabling software including debuggers, performance tools, and system and numeric libraries. -General hardware (architecture) concepts, new technologies enabling the realization of such new concepts, and details of commercially available systems -Software engineering and productivity as it relates to parallel computing -Applications (including scientific computing, deep learning, machine learning) or tool case studies demonstrating novel ways to achieve parallelism -Performance measurement results on state-of-the-art systems -Approaches to effectively utilize large-scale parallel computing including new algorithms or algorithm analysis with demonstrated relevance to real applications using existing or next generation parallel computer architectures. -Parallel I/O systems both hardware and software -Networking technology for support of high-speed computing demonstrating the impact of high-speed computation on parallel applications