计算机应用 ›› 2011, Vol. 31 ›› Issue (03): 843-847.DOI: 10.3724/SP.J.1087.2011.00843

• 典型应用 • 上一篇    下一篇

多核CPU和GPU加速分子动力学模拟

林江宏1,林锦贤2,吕暾3   

  1. 1. 福州大学 数学与计算机科学学院,福州350108
    2. 福州大学 数学与计算机科学学院,福州350108; 福州大学 福建省超级计算中心,福州350108
    3. 福州大学 福建省超级计算中心,福州350108;福州大学 生物科学与工程学院,福州350108
  • 收稿日期:2010-08-16 修回日期:2010-11-04 发布日期:2011-03-03 出版日期:2011-03-01
  • 通讯作者: 林江宏
  • 作者简介:林江宏(1986-),男,福建宁德人,硕士研究生,主要研究方向:分子动力学并行算法;林锦贤(1957-),男,福建福州人,副教授,主要研究方向:高性能计算;吕暾(1973-),男,福建厦门人,研究员,主要研究方向:计算生物学。
  • 基金资助:
    福建省高校科研专项重点项目(JK2009002);福建省科技厅青年人才基金资助项目(2008F306010107)

Accelerated molecular dynamics simulation using multi-core CPU and GPU

LIN Jiang-hong1,LIN Jin-xian2,LV Tun3   

  1. 1. College of Mathematics and Computer Science, Fuzhou University, Fuzhou Fujian 350108, China
    2. College of Mathematics and Computer Science, Fuzhou University, Fuzhou Fujian 350108, China; Fujian Supercomputing Center, Fuzhou University, Fuzhou Fujian 350108, China
    3. Fujian Supercomputing Center, Fuzhou University, Fuzhou Fujian 350108, China; College of Biological Science and Technology, Fuzhou University, Fuzhou Fujian 350108, China
  • Received:2010-08-16 Revised:2010-11-04 Online:2011-03-03 Published:2011-03-01
  • Contact: LIN Jiang-hong

摘要: 在多核中央处理器(CPU)—图形处理器(GPU)异构并行体系结构上,采用OpenMP和计算统一设备架构(CUDA)编程实现了基于AMBER力场的蛋白质分子动力学模拟程序。通过合理地将程序划分为CPU单线程、CPU多线程和GPU多线程执行部分,高效地利用了计算机的处理能力。性能测试结果表明,相对于优化后的CPU串行计算,多核CPU-GPU异构并行计算模型有强大的性能优势,特别是将占整个程序执行时间90%的作用力的计算移植到GPU上执行,获得了最高可达12倍的计算加速比。

关键词: 分子动力学, 图形处理器, 多核中央处理器, AMBER力场, 计算统一设备架构, OpenMP

Abstract: On the heterogeneous architecture of multi-core Central Processing Unit (CPU) and Graphic Processing Unit (GPU), the Open Multi-Processing (OpenMP) and the programming interfaces of Compute Unified Device Architecture (CUDA) were used to implement a molecular dynamics simulation program based on AMBER force field. In order to efficiently use computer processing power, the program was divided into different parts which were processed by CPU single-thread, CPU multi-thread and GPU multi-thread respectively. The experimental results show that compared with the optimized CPU-based implementations, the heterogeneous parallel computing model based on multi-core CPU-GPU gets powerful performance advantage. Especially, the calculations of forces, which account for more than 90% of processing time, get at most 12 times faster than CPU-based implementations while being implemented on GPU.

Key words: Molecular Dynamics (MD), Graphic Processing Unit (GPU), multi-core Central Processing Unit (CPU), AMBER force field, Compute Unified Device Architecture (CUDA), Open Multi-Processing (OpenMP)

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