• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

J4 ›› 2013, Vol. 35 ›› Issue (11): 105-110.

• 论文 • 上一篇    下一篇

ANSYS和Abaqus软件GPU加速性能典型算例测试与分析

王惠,郭培卿,陈小龙   

  1. (上海超级计算中心,上海 201203)
  • 收稿日期:2013-04-28 修回日期:2013-07-03 出版日期:2013-11-25 发布日期:2013-11-25
  • 基金资助:

    国家863计划资助项目(2012AA01A308)

Testing and analysis of typical examples for
ANSYS and Abaqus software GPU-accelerated performance 

WANG Hui,GUO Pei-qing,CHEN Xiao-long   

  1. (Shanghai Supercomputing Center,Shanghai 201203,China)
  • Received:2013-04-28 Revised:2013-07-03 Online:2013-11-25 Published:2013-11-25

摘要:

在高性能计算领域,CPU/GPU异构协同处理技术已经成为快速获得计算结果的有效手段之一。典型结构力学计算软件ANSYS和Abaqus最新版本中加入了CPU/GPU协同处理技术,以进一步提高问题的求解效率。利用NVIDIA公司Tesla 系列M2090 GPU和上海超级计算中心“蜂鸟”超级计算平台,通过求解典型结构问题,对ANSYS和Abaqus软件在开启GPU加速功能前后对求解效率的影响进行了对比和分析。结果表明,当并行规模低于16核时,GPU加速能够不同程度地减少各类结构问题的求解时间,但加速效果随着并行规模的增加逐渐减弱,多GPU协同求解对加速性能的提高并不明显,在实际应用中,需要结合问题类型以及当前硬件架构选择合适的并行方式和协同处理模式。

关键词: CPU/GPU, 协同处理, 加速性能, 高性能计算

Abstract:

In the field of HPC, CPU/GPU co-processing technology has become one of the effective approaches for obtaining quick computing results. In the latest version of typical structural mechanics calculation software: ANSYS and Abaqus, CPU/GPU co-processing technology is adopted for improving the efficiency of problem solving. In the paper, a research is conducted by using typical structural problems, and NVIDIA Tesla M2090 GPU and "Hummingbird" supercomputing platforms from Shanghai Supercomputing Center, to compare and analyze problem solving efficiency of ANSYS and Abaqus before and after the acceleration of GPU. Results indicate that in the situation of parallel scale less than 16 cores, GPU acceleration can reduce solution time at different levels. However, the performance shows a decreasing trend with increasing parallel scale. In addition, the effect of Multi-GPU collaborative solution on enhancing acceleration performance is not obvious. Therefore, in practical application, the selection of appropriate parallel approach and co-processing model should be associated with problem type and current hardware architecture.

Key words: CPU/GPU;co-processing;acceleration performance;HPC