CAREER: Compilation Processes to Enhance Dataflow Optimizations

职业：增强数据流优化的编译过程

• 批准号: 1943319
• 负责人: Catherine Olschanowsky
• 金额: $ 54.42万
• 依托单位: Boise State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-15 至 2023-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1943319&HistoricalAwards=false
• 关键词: CAREER; Compilation; Processes; Enhance; Dataflow

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.

超级计算推动了许多科学和工程领域的进步。从了解气候变化到发现太阳能电池的新材料，再到治疗癌症，这些挑战都取决于领导级超级计算资源的能力。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应该在最短的时间内产生最大的科学进步，使用最少的能源。然而，科学应用程序的运行效率低下，因为它们通常以低于潜在最大速率的30%执行操作，并且通常进一步低于该速率。通过重写代码来手动优化科学应用程序可以提高运算速度，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。自动化代码优化保留了代码的可维护性，并有可能显著提高操作率。然而，也存在一些障碍，例如使用共享的数学库来阻止自动优化。该项目旨在克服科学应用程序自动化代码优化的关键障碍。一个综合教育计划将吸引来自当地爱达荷州高中的高危青少年学习计算机科学，该计划建立在一个成功的试点计划的基础上，该计划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锁内存优化，该优化依赖于编译器执行的代码转换，称为函数内联，这对随后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小的、可重用的模块化组件。但是，这样做会阻止内联并限制有助于内存优化的转换的功效。针对这一问题，该项目将使功能机构更早地可用，并以一种易于转换的形式提供。为了实现这一目标，该项目将使用高级编译器内部表示开发一个新的早期链接阶段，从而导致大面积的应用程序代码以适合转换的结构表示。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造成的障碍，以及使用函数指针造成的一些障碍。项目目标是：（1）启用预编译库的内联，（2）启用函数指针上的内联，和（3）提供定制的优化规划。该项目由CCF部门软件和硬件基础计划以及刺激竞争研究的既定计划（EPSCoR）联合资助该奖项反映了NSF的法定使命，并通过使用基金会的智力价值进行评估，更广泛的影响审查标准。

项目成果

Techniques for Managing Polyhedral Dataflow Graphs

管理多面体数据流图的技术

• DOI: 10.1007/978-3-030-99372-6_9
• 发表时间: 2021
• 期刊: LCPC 2021
• 作者: Shankar, Ravi;Orenstein, Aaron;Rift, Anna;Popoola, Tobi;MacDonald;Yang, Shuai;Mikesell, T. Dylan;Olschanowsky, Catherine
• 通讯作者: Olschanowsky, Catherine

Code Synthesis for Sparse Tensor Format Conversion and Optimization

稀疏张量格式转换和优化的代码综合

• 发表时间: 2023
• 期刊: International Symposium on Code Generation and Optimization
• 作者: Popoola, Tobi;Zhao, Tuowen;St. George, Aaron;Bhetwal, Kalyan;Strout, Michelle;Hall, Mary;Olschanowsky, Catherine
• 通讯作者: Olschanowsky, Catherine

An Object-Oriented Interface to The Sparse Polyhedral Library

稀疏多面体库的面向对象接口

• DOI: 10.1109/compsac51774.2021.00275
• 发表时间: 2021
• 期刊: and Applications Conference (COMPSAC
• 作者: Popoola, Tobi;Shankar, Ravi;Rift, Anna;Singh, Shivani;Davis, Eddie C.;Strout, Michelle Mills;Olschanowsky, Catherine
• 通讯作者: Olschanowsky, Catherine