The simulation of the two-dimensional Ising model is used as a benchmark to show the computational capabilities of Graphic Processing Units (GPUs). The rich programming environment now available on GPUs and flexible hardware capabilities allowed us to quickly experiment with several implementation ideas: a simple stencil-based algorithm, recasting the stencil operations into matrix multiplies to take advantage of Tensor Cores available on NVIDIA GPUs, and a highly optimized multi-spin coding approach. Using the managed memory API available in CUDA allows for simple and efficient distribution of these implementations across a multi-GPU NVIDIA DGX-2 server. We show that even a basic GPU implementation can outperform current results published on TPUs and that the optimized multi-GPU implementation can simulate very large lattices faster than custom FPGA solutions.

中文翻译：

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2D Ising 模型在 GPU 上的高性能实现

二维 Ising 模型的模拟被用作基准来展示图形处理单元 (GPU) 的计算能力。现在 GPU 上可用的丰富编程环境和灵活的硬件功能使我们能够快速尝试几种实现思路：基于模板的简单算法，将模板运算重铸为矩阵乘法以利用 NVIDIA GPU 上可用的 Tensor Core，以及高度优化的多旋转编码方法。使用 CUDA 中可用的托管内存 API 可以在多 GPU NVIDIA DGX-2 服务器上简单高效地分发这些实现。