基于GPU优化的蒙特卡罗算法研究q态Potts模型亚稳性
q-State Potts model metastability study using optimized GPU-based Monte Carlo algorithms
摘要：
我们实现了基于GPU的并行代码来执行二维q态Potts模型的蒙特卡罗模拟。该算法基于棋盘式更新机制，并为每个线程分配独立的随机数生成器。该实现允许在测试的最快GPU卡上模拟高达约10^9次旋转的系统，每次旋转翻转的平均时间为0.147ns，与在高端CPU上运行的优化串行代码相比，加速高达155倍。在足够大的系统尺寸进行高速模拟的可能性，使我们能够提供一个积极的数值证据亚稳定性的存在非常大的系统的基础上宾德的标准，即，在存在或不特定的热奇点在旋节温度不同的过渡之一。
英文摘要：
We implemented a GPU based parallel code to perform Monte Carlo simulations of the two dimensional q-state Potts model. The algorithm is based on a checkerboard update scheme and assigns independent random numbers generators to each thread. The implementation allows to simulate systems up to ~10^9 spins with an average time per spin flip of 0.147ns on the fastest GPU card tested, representing a speedup up to 155x, compared with an optimized serial code running on a high-end CPU. The possibility of performing high speed simulations at large enough system sizes allowed us to provide a positive numerical evidence about the existence of metastability on very large systems based on Binder's criterion, namely, on the existence or not of specific heat singularities at spinodal temperatures different of the transition one.
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