基于GPU的碳纳米管分子动力学并行仿真  

Parallel Simulation of Carbon Nano Tube Molecular Dynamics Based on GPU

作　　者：孟小华[1] 覃大胜[1] 郑冬琴[2] 周玉宇[1] 

机构地区：[1]暨南大学计算机科学系,广州510632 [2]暨南大学物理系,广州510632

出　　处：《计算机工程》2015年第4期288-293,共6页Computer Engineering

基　　金：国家"863"计划基金资助项目(2013AA040404);广东省重点实验室建设基金资助项目(2011A060901026)

摘　　要：基于计算机的分子动力学仿真具有理论分析方法和实验方法无法比拟的优点,但分子动力学仿真算法计算量非常大,特别是在对碳纳米管的大规模粒子数进行仿真处理时,普通的基于CPU的串行算法执行效率低且耗时多。为此,提出基于统一计算设备架构的碳纳米管分子动力学的图形处理单元(GPU)并行算法,设计并实现仿真算法中适合GPU并行运算的分裂算法,将具有竞争资源的运算以非竞争方式运行。实验结果表明,与CPU串行仿真算法相比,分裂算法的运算速度较快,且在只有16个GPU流处理器显卡上可获得十多倍的加速比。Molecular dynamics simulation has advantages dramatically superior to both theoretical analysis and experiments. How ever,due to the extremely high cost of computation resources during the simulation of a large number of Carbon Nano Tube（ CNT） particles,typical CPU serial algorithm implementation is non-efficient and slow. A Compute Unified Device Architecture（ CUDA） based parallel algorithm of CNT molecular dynamics is proposed in this paper to take advantage of the data parallelism of Graphic Processing Unit（ GPU）. A CNT is divided to several blocks and processed parallel in the GPU. Experimental results show that the algorithm can obtain a speed-up more than 10 times to the CPU serial algorithm in a low-configured graphics card that has only 16 GPU stream processors.

关 键 词：碳纳米管 分子动力学 统一计算设备架构 并行计算 时间效率 加速比 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]