CAREER: Compilation Processes to Enhance Dataflow Optimizations

超级计算推动了许多科学和工程领域的进步。从理解气候变化，到发现太阳能电池的新材料，再到治愈癌症，各种挑战都依赖于领先级别的超级计算资源的力量。这类资源的建立和运行花费了数千万美元，因此使用它们的科学应用程序应该在最短的时间内，使用最少的能量，产生尽可能大的科学进步。然而，科学应用程序的运行效率很低，因为它们通常以低于潜在最大速率的30%的速度执行操作，而且往往更低。通过重写代码来手动优化科学应用程序可以提高运行率，但也会引入代码复杂性，使代码难以理解和维护，从而阻碍科学进步。自动代码优化保留了代码的可维护性，并有可能显著提高运行率。然而，也存在一些障碍，例如使用共享数学库阻碍了自动优化。该项目旨在克服用于科学应用的自动化代码优化的关键障碍。一项综合教育计划将吸引爱达荷州当地高中的高危青年学习计算机科学，该计划建立在一个成功的试点项目的基础上，该项目将怀孕和育儿的青少年与计算机科学专业的学生聚集在一起。该项目的研究目标是解除依赖于编译器执行的代码转换的内存优化，该代码转换被称为函数内联，这对后续的代码转换至关重要。与内存的交互是昂贵的，应用程序设计人员依赖它们来支持软件设计模式。应用程序设计人员经常使用库调用或函数指针将软件分解为可重复使用的小型模块化组件。但是，这样做会防止内联，并限制有助于内存优化的转换的效率。为了应对这个问题，该项目将更早地提供功能实体，并以一种易于转换的形式提供。为了实现这一点，该项目将使用高级编译器内部表示法开发一个新的早期链接阶段，从而产生以适合转换的结构表示的大片应用程序代码。这种方法允许内联和后续的循环转换，从而消除了使用预编译库产生的障碍，以及函数指针使用的一些障碍。项目目标是：(1)实现预编译库的内联，(2)实现函数指针的内联，以及(3)提供定制的优化计划。该项目由CCF部门软件和硬件基础计划和既定的激励竞争研究计划(EPSCoR)共同资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Supercomputing drives advances in many areas of science and engineering. Challenges ranging from understanding climate change, to discovering new materials for solar cells, to curing cancer, depend on the power of leadership-class supercomputing resources. Resources of this class cost tens of millions to set up and operate, so the scientific applications using them should produce the largest scientific advancement possible, in the shortest amount of time, using the least amount of energy. However, scientific applications operate inefficiently in that they typically perform operations at less than 30 percent of the potential maximum rate, and often further less than that. Hand-optimizing scientific applications by rewriting the code can increase the operation rate, but also introduces code complexities that make code difficult to understand and maintain, impeding scientific progress. Automated code optimization preserves code maintainability and has the potential to significantly increase operation rates. However, there are obstacles, such as the use of shared mathematical libraries that block automated optimization. This project seeks to overcome key obstacles to automated code optimization for scientific applications. An integrated education plan will engage at-risk youth from local Idaho high schools in computer science, building on a successful pilot program that brought pregnant and parenting teens together with computer science students.The research goal of this project is to unblock memory optimizations that depend on a code transformation performed by compilers called function inlining that is crucial to subsequent code transformation. Interactions with memory are expensive and application designers depend on them to support software design patterns. Application designers often use library calls or function pointers to break software into small, reusable modular components. However, doing so prevents inlining and limits the efficacy of transformations contributing to memory optimizations. In response to this problem, the project will make function bodies available earlier and in a form that is amenable to transformations. To accomplish this, the project will develop a new early linking stage using a high-level compiler internal representation, resulting in large regions of application code expressed in a structure ideal for transformation. This approach allows for inlining and subsequent loop transformations, thus removing the barrier resulting from using precompiled libraries, and some of the barriers from function pointer use. Project objectives are to: (1) enable inlining of precompiled libraries, (2) enable inlining over function pointers, and (3) provide customized optimization planning.This project is jointly funded by CCF Division Software and Hardware Foundations Program and the Established Program to Stimulate Competitive Research (EPSCoR).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.