Experimental Software Systems: Experimental Evaluation of Scalable Optimization Techniques

[806525] soffa, Mary LouGupta， rajiv匹兹堡大学实验软件系统：可扩展优化技术的实验评估虽然在开发编译器优化技术上花费了大量的研究工作，但很少关注这些技术在大型程序中的可扩展性和有效性。尽管许多优化已经被证明对于大大提高中等规模程序的性能是有效的，但是不断增加的复杂性和规模，以及应用程序的模块化设计使得优化技术过于昂贵或无效。本研究项目的目标是开发并实验验证一种可扩展的编译器优化方法。与当前技术同时考虑整个程序的优化机会不同，程序路径被用来限制优化的范围。用于分析和代码转换的需求驱动框架构成了技术开发的基础。来自政府、行业和标准基准套件的基准程序的目标是测试所开发技术的可伸缩性。可扩展技术将在工业中用于提高大型程序的性能，而开发的优化器将作为一个平台，使研究人员能够通过实验评估优化的可扩展性。需求驱动的分析算法不仅对可伸缩优化有用，而且对软件工程工具也有用。

9806525Soffa, Mary LouGupta, RajivUniversity of PittsburghExperimental Software Systems: Experimental Evaluation of Scalable Optimization TechniquesWhile extensive research efforts have been expended on developing compiler optimization techniques, little attention has been paid to the scalability and effectiveness of these techniques for large programs. Although many optimizations have been shown to be effective for greatly improving the performance of moderately size programs, the increasing complexity and size, as well as the modular design of applications make the optimization techniques too costly or not effective.The goal of this research project is to develop and experimentally verify a scalable approach to compiler optimizations. Instead of simultaneously considering optimization opportunities across the whole program, as current techniques do, program paths are used to limit the scope of optimizations. A demand driven framework for analyses and code transformations forms the foundation for the development of the techniques. Benchmark programs from government, industry, and standard benchmark suites are targeted to test the scalability of techniques developed.Scalable techniques will be useful in industry to improve the performance of large programs, and the optimizer developed will serve as a platform to enable researchers to experimentally evaluate the scalability of optimizations. The demand driven analysis algorithms will not only be useful for scalable optimizations, but also for software engineering tools.