Effective Source-to-Source Outlining to Support Whole Program Empirical Optimization
Effective Source-to-Source Outlining to Support Whole Program Empirical Optimization
复制标题
有效的源到源大纲支持整个程序的经验优化
DOI：
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
T. Panas
中科院分区：
文献类型：
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作者：
C. Liao;D. Quinlan;R. Vuduc;T. Panas
虽然自动化的经验性能优化和调优是很好的研究内核和特定领域的图书馆，目前的研究面临的巨大挑战是如何将这些方法和工具扩展到显着更大的顺序和并行应用程序。在这种情况下，我们提出了ROSE源到源大纲，它解决了从整个程序中提取可调内核的问题，从而有助于将具有挑战性的整个程序调优问题转换为一组更易于管理的内核调优任务。我们的大纲旨在处理大规模的C/C++，Fortran和OpenMP应用程序。利用一组程序分析和转换技术来增强源到源大纲的可移植性、可伸缩性和互操作性。更重要的是，生成的内核保留了调优目标的性能特征，并且可以很容易地由其他工具处理。初步评估表明，ROSE outliner是端到端经验优化系统中的关键组件，可以实现广泛的顺序和并行优化机会。
Although automated empirical performance optimization and tuning is well-studied for kernels and domain-specific libraries, a current research grand challenge is how to extend these methodologies and tools to significantly larger sequential and parallel applications. In this context, we present the ROSE source-to-source outliner, which addresses the problem of extracting tunable kernels out of whole programs, thereby helping to convert the challenging whole-program tuning problem into a set of more manageable kernel tuning tasks. Our outliner aims to handle large scale C/C++, Fortran and OpenMP applications. A set of program analysis and transformation techniques are utilized to enhance the portability, scalability, and interoperability of source-to-source outlining. More importantly, the generated kernels preserve performance characteristics of tuning targets and can be easily handled by other tools. Preliminary evaluations have shown that the ROSE outliner serves as a key component within an end-to-end empirical optimization system and enables a wide range of sequential and parallel optimization opportunities.