Lattice SU(2) on GPU's

TL;DR

This paper demonstrates that GPU computing with CUDA significantly accelerates lattice SU(2) gauge simulations, enabling large-scale configuration generation and analysis with high efficiency and speedup over CPU implementations.

Contribution

The paper introduces an optimized CUDA-based GPU implementation for lattice SU(2) simulations, achieving high performance and enabling large-scale gauge configuration generation.

Findings

Achieved 200x speedup over CPU using 2 NVIDIA GTX cards.

Generated 1 million gauge configurations in one day on a 48^4 lattice.

Presented results for plaquette and Polyakov loop at various lattice sizes and temperatures.

Abstract

We discuss the CUDA approach to the simulation of pure gauge Lattice SU(2). CUDA is a hardware and software architecture developed by NVIDIA for computing on the GPU. We present an analysis and performance comparison between the GPU and CPU with single precision. Analysis with single and multiple GPU's, using CUDA and OPENMP, are also presented. In order to obtain a high performance, the code must be optimized for the GPU architecture, i.e., an implementation that exploits the memory hierarchy of the CUDA programming model. Using GPU texture memory and minimizing the data transfers between CPU and GPU, we achieve a speedup of $200\times$ using 2 NVIDIA 295 GTX cards relative to a serial CPU, which demonstrates that GPU's can serve as an efficient platform for scientific computing. With multi-GPU's we are able, in one day computation, to generate 1 000 000 gauge configurations in a $48^4$ lattice with $\beta=6.0$ and calculate the mean average plaquette. We present results for the mean average plaquette in several lattice sizes for different $\beta$. Finally we present results for the mean average Polyakov loop at finite temperature.