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耗散粒子动力学 GPU并行计算研究?
Accelerating dissipative particle dynamics with graphic pro cessing unit
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摘要: 研究了耗散粒子动力学基于计算统一设备架构的图形处理器(GPU)并行计算的实施。对其中涉及的算法映射模型、Cell-List法数组的并行化更新、随机数生成、存储器访问优化、负载平衡等进行了详细的讨论。进一步模拟了Poiseuille流动和突扩突缩流动，从而验证了GPU计算结果的正确性。计算结果表明，相对于基于中央处理器的串行计算，在耗散粒子动力学中实施GPU并行计算可以获得约20倍的加速比。Abstract: In this paper, the graphic processing unit (GPU) parallel computing of dissipative particle dynamics (DPD) based on compute unified device architecture is carried out. Some issues involved, such as thread mapping, parallel cell-list array updating, generating pseudo-random number on GPU, memory access optimization and loading balancing are discussed in detail. Furthermore, Poiseuille flow and suddenly contracting and expanding flow are simulated to verify the correctness of GPU parallel computing. The results of GPU parallel computing of DPD show that the speedup ratio is about 20 times compared with central processing unit serial computing.
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