10.17632/XRB9XTKBCP.1
Romero, Joshua
Joshua
Romero
High performance implementations of the 2D Ising model on GPUs
Mendeley
2020
Dataset
Statistical Physics
Thermodynamics
Computational Physics
Bisson, Mauro
Mauro
Bisson
Fatica, Massimiliano
Massimiliano
Fatica
Bernaschi, Massimo
Massimo
Bernaschi
2020-07-16
10.1016/j.cpc.2020.107473
10.17632/xrb9xtkbcp
MIT License
We present and make available novel implementations of the two-dimensional Ising model that is used as a benchmark to show the computational capabilities of modern 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 (Yang et al., 2019) and that the optimized multi-GPU implementation can simulate very large lattices faster than custom FPGA solutions (Ortega-Zamorano et al., 2016).