Redundancy, retiming and data flow in compiling finite-difference applications for manycore architectures

为众核架构编译有限差分应用程序时的冗余、重定时和数据流

• 批准号: 2293810
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2293810
• 关键词: Redundancy; retiming; data; flow; compiling

The finite difference method is the most widely-used approach to solving partial differential equations in computational science and engineering - with important applications from subsurface image reconstruction to fluid dynamics to materials science and beyond. Finite difference solvers have characteristic loop and data-access patterns that benefit from optimisations that rely on application knowledge not available in general-purpose compilers. Devito, a software developed in the OPESCI project, provides a high-level language for the finite difference method that opens up the scope for such domain-specific compiler techniques. The goal of this project is to explore this frontier. The key idea we aim to exploit is symbolic manipulation of algebraic expressions to expose redundant computations, and to do this in concert with modelling of when to evaluate such expressions, where to store them, and when pre-computing them is better than recomputing them. We think there is a way to do this in a unified, perhaps even optimal, way. A key element of our research philosophy is the rigorous development of software tools that can be evaluated on diverse real-world problems of industrial and scientific importance. The work will be driven (at least initially) by applications in seismic inversion, which provide a rich spectrum of challenges. We will target the most sophisticated parallel hardware platforms available, including manycore, multithreaded and wide-vector architectures - such as Intel's Xeon Phi and Skylake processors.

有限差分方法是计算科学和工程中最广泛使用的求解偏微分方程的方法--从地下图像重建到流体力学，再到材料科学和其他领域，都有重要的应用。有限差分解算器具有独特的循环和数据访问模式，这些模式受益于依赖于通用编译器中不可用的应用程序知识的优化。DeVito是在OPESCI项目中开发的软件，它为有限差分法提供了一种高级语言，为这种特定于领域的编译器技术开辟了范围。这个项目的目标就是探索这一前沿。我们旨在利用的关键想法是对代数表达式进行符号操作，以暴露多余的计算，并与何时计算此类表达式、将它们存储在哪里以及何时预计算它们比重新计算它们更好的建模结合起来。我们认为有一种方法可以以一种统一的、甚至是最优的方式来实现这一点。我们研究理念的一个关键要素是严格开发软件工具，这些工具可以对各种具有工业和科学重要性的现实世界问题进行评估。这项工作将由地震反演中的应用推动(至少最初是这样)，这提供了丰富的挑战范围。我们将瞄准可用的最复杂的并行硬件平台，包括多核、多线程和宽矢量架构--例如英特尔的至强Phi和Skylake处理器。