基于多GPU的Swendsen-Wang多簇算法模拟二维q态Potts模型
Multi-GPU-based Swendsen-Wang multi-cluster algorithm for the simulation of two-dimensional q-state Potts model
摘要：
针对二维q态Potts模型的Swendsen-Wang多簇算法，提出了基于通用统一设备架构（CUDA）的多GPU计算。扩展我们的单GPU计算算法[Comp.Phys.Comm.183（2012）1155]，我们实现了多GPU的Swendsen-Wang多簇算法的GPU计算。我们实现了我们的代码在大型开放科学超级计算机TSUBAME 2.0，并测试的性能和可扩展性的模拟的二维波茨模型。在使用256个GPU的Tesla M2050上，对于q=2的Potts模型（伊辛模型），在临界温度下获得的性能为每纳秒37.3次自旋翻转，线性系统大小L=65536。
英文摘要：
We present the multiple GPU computing with the common unified device architecture (CUDA) for the Swendsen-Wang multi-cluster algorithm of two-dimensional (2D) q-state Potts model. Extending our algorithm for single GPU computing [Comp. Phys. Comm. 183 (2012) 1155], we realize the GPU computation of the Swendsen-Wang multi-cluster algorithm for multiple GPUs. We implement our code on the large-scale open science supercomputer TSUBAME 2.0, and test the performance and the scalability of the simulation of the 2D Potts model. The performance on Tesla M2050 using 256 GPUs is obtained as 37.3 spin flips per a nano second for the q=2 Potts model (Ising model) at the critical temperature with the linear system size L=65536.
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