基于gpu的点阵QCD计算框架

F. Winter, M. Clark, R. Edwards, B. Joó
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引用次数: 34

摘要

配备了像gpu这样的加速器的计算平台已经被证明可以提供强大的计算能力。然而,为现有的科学应用开发这样的平台并不是一项微不足道的任务。当前的GPU编程框架(如CUDA C/ c++)需要开发人员进行低级编程才能实现高性能代码。因此,将应用程序移植到gpu通常仅限于时间主导算法和例程,而其余部分没有加速,这可能会引发严重的阿姆达尔定律问题。Lattice QCD应用程序Chroma允许我们探索一种不同的移植策略。软件体系结构的分层结构在逻辑上将数据并行层与应用层分开。QCD数据并行软件层提供了适合晶格场理论的数据类型和具有类似模板操作的表达式。Chroma根据这个高级接口实现算法。因此,通过移植低级层,可以一次有效地移植整个应用程序层。QDP-JIT/PTX库,我们对底层的重新实现,为CUDA架构的Lattice QCD计算提供了一个框架。支持完整的软件接口,因此应用程序可以在基于gpu的并行计算机上不加更改地运行。这种重新实现是可能的,因为JIT编译器可以将汇编语言(PTX)转换为GPU代码。现有的表达式模板使我们能够使用编译时计算来构建代码生成器并自动化CUDA的内存管理。我们的实现使我们能够在大型基于gpu的机器(如Titan和Blue Waters)上部署完整的色度计生成程序,并将计算速度提高一个数量级以上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Framework for Lattice QCD Calculations on GPUs
Computing platforms equipped with accelerators like GPUs have proven to provide great computational power. However, exploiting such platforms for existing scientific applications is not a trivial task. Current GPU programming frameworks such as CUDA C/C++ require low-level programming from the developer in order to achieve high performance code. As a result porting of applications to GPUs is typically limited to time-dominant algorithms and routines, leaving the remainder not accelerated which can open a serious Amdahl's law issue. The Lattice QCD application Chroma allows us to explore a different porting strategy. The layered structure of the software architecture logically separates the data-parallel from the application layer. The QCD Data-Parallel software layer provides data types and expressions with stencil-like operations suitable for lattice field theory. Chroma implements algorithms in terms of this high-level interface. Thus by porting the low-level layer one effectively ports the whole application layer in one swing. The QDP-JIT/PTX library, our reimplementation of the low-level layer, provides a framework for Lattice QCD calculations for the CUDA architecture. The complete software interface is supported and thus applications can be run unaltered on GPU-based parallel computers. This reimplementation was possible due to the availability of a JIT compiler which translates an assembly language (PTX) to GPU code. The existing expression templates enabled us to employ compile-time computations in order to build code generators and to automate the memory management for CUDA. Our implementation has allowed us to deploy the full Chroma gauge-generation program on large scale GPU-based machines such as Titan and Blue Waters and accelerate the calculation by more than an order of magnitude.
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