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首页> 外文期刊>Journal of Computational Physics >SU (2) lattice gauge theory simulations on Fermi GPUs
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SU (2) lattice gauge theory simulations on Fermi GPUs

机译：Fermi GPU上的SU（2）晶格规理论模拟

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摘要

In this work we explore the performance of CUDA in quenched lattice SU (2) simulations. CUDA, NVIDIA Compute Unified Device Architecture, 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 in single and double precision. Analyses with multiple GPUs and two different architectures (G200 and Fermi architectures) 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.We produce codes for the Monte Carlo generation of SU (2) lattice gauge configurations, for the mean plaquette, for the Polyakov Loop at finite T and for the Wilson loop. We also present results for the potential using many configurations (50,000) without smearing and almost 2000 configurations with APE smearing. With two Fermi GPUs we have achieved an excellent performance of 200× the speed over one CPU, in single precision, around 110. Gflops/s. We also find that, using the Fermi architecture, double precision computations for the static quark-antiquark potential are not much slower (less than 2× slower) than single precision computations.
机译：在这项工作中，我们探索了CUDA在淬火晶格SU（2）模拟中的性能。 CUDA是NVIDIA计算统一设备架构，是NVIDIA开发的用于在GPU上进行计算的硬件和软件架构。我们提出了单精度和双精度GPU和CPU之间的分析和性能比较。还介绍了使用多个GPU和两种不同架构（G200和Fermi架构）的分析。为了获得高性能，必须针对GPU架构（即利用CUDA编程模型的内存层次结构的实现）对代码进行优化。平均球拍，有限T处的Polyakov环和Wilson环。我们还介绍了使用许多配置（50,000种）而不产生拖尾以及将近2000种配置进行APE涂抹的潜在结果。通过使用两个Fermi GPU，我们在一个CPU上的速度达到了200倍的出色性能，单精度约为110 Gflops / s。我们还发现，使用费米架构，静态夸克-反夸克势的双精度计算不会比单精度计算慢很多（小于2倍慢）。

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