Swendson-Wang动力学的GPU加速度
GPU Acceleration of Swendson-Wang Dynamics
摘要：
当模拟接近其临界温度的晶格系统时，用于对系统的演化进行建模的局部算法可以将非常大的自相关时间引入到采样数据中。这种临界减速限制了对临界点周围系统行为的及时分析。因为人们常常希望围绕这一点来研究这类系统，所以必须引入新的算法。因此，我们转向聚类算法，如Swendsen-Wang算法和Wolff聚类算法。它们结合了全局更新，生成新的晶格配置，与以前的状态几乎没有相关性，甚至在临界点附近。我们希望通过实施和基准测试旨在在NVIDIA CUDA框架下的GPU上运行的每个算法的并行实现来加快这些算法的运行速度。一个17和90倍的计算速度分别增加时，经历了对串行代码中实现的等效算法进行测量。
英文摘要：
When simulating a lattice system near its critical temperature, local algorithms for modeling the system's evolution can introduce very large autocorrelation times into sampled data. This critical slowing down places restrictions on the analysis that can be completed in a timely manner of the behavior of systems around the critical point. Because it is often desirable to study such systems around this point, a new algorithm must be introduced. Therefore, we turn to cluster algorithms, such as the Swendsen-Wang algorithm and the Wolff clustering algorithm. They incorporate global updates which generate new lattice configurations with little correlation to previous states, even near the critical point. We look to accelerate the rate at which these algorithm are capable of running by implementing and benchmarking a parallel implementation of each algorithm designed to run on GPUs under NVIDIA's CUDA framework. A 17 and 90 fold increase in the computational rate was respectively experienced when measured against the equivalent algorithm implemented in serial code.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
