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首页> 外文期刊>Computer physics communications >Parallel tempering simulation of the three-dimensional Edwards-Anderson model with compact asynchronous multispin coding on GPU
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Parallel tempering simulation of the three-dimensional Edwards-Anderson model with compact asynchronous multispin coding on GPU

机译：在GPU上使用紧凑的异步多轴编码对三维Edwards-Anderson模型进行并行回火仿真

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摘要

Monte Carlo simulations of the Ising model play an important role in the field of computational statistical physics, and they have revealed many properties of the model over the past few decades. However, the effect of frustration due to random disorder, in particular the possible spin glass phase, remains a crucial but poorly understood problem. One of the obstacles in the Monte Carlo simulation of random frustrated systems is their long relaxation time making an efficient parallel implementation on state-ofthe- art computation platforms highly desirable. The Graphics Processing Unit (GPU) is such a platform that provides an opportunity to significantly enhance the computational performance and thus gain new insight into this problem. In this paper, we present optimization and tuning approaches for the CUDA implementation of the spin glass simulation on GPUs. We discuss the integration of various design alternatives, such as GPU kernel construction with minimal communication, memory tiling, and lookup tables. We present a binary data format, Compact Asynchronous Multispin Coding (CAMSC), which provides an additional 28.4% speedup compared with the traditionally used Asynchronous Multispin Coding (AMSC). Our overall design sustains a performance of 33.5 ps per spin flip attempt for simulating the three-dimensional Edwards-Anderson model with parallel tempering, which significantly improves the performance over existing GPU implementations.
机译：Ising模型的蒙特卡洛模拟在计算统计物理学领域中发挥着重要作用，并且在过去的几十年中它们揭示了该模型的许多特性。然而，由于随机无序而产生的挫折影响，尤其是可能的旋转玻璃相，仍然是一个关键但尚未广为人知的问题。随机受挫系统的蒙特卡罗模拟中的障碍之一是其漫长的松弛时间，因此非常需要在最新的计算平台上进行有效的并行实现。图形处理单元（GPU）就是这样的平台，它提供了一个机会来显着提高计算性能，从而获得对该问题的新见解。在本文中，我们为在GPU上旋转玻璃仿真的CUDA实现提供了优化和调整方法。我们讨论了各种设计替代方案的集成，例如以最少的通信，内存切片和查找表构建GPU内核。我们提出了一种二进制数据格式，紧凑型异步多旋转编码（CAMSC），与传统使用的异步多旋转编码（AMSC）相比，其速度提高了28.4％。我们的总体设计在每次旋转翻转尝试时可维持33.5 ps的性能，以模拟具有平行回火的三维Edwards-Anderson模型，与现有的GPU实施相比，该性能大大提高了。

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