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ASPLOS2022顶会

Finding missed optimizations through the lens of dead code elimination

Theodoros Theodoridis, Manuel Rigger, Zhendong Su

2022年份
48被引次数
21顶会引用

摘要

Compilers are foundational software development tools and incorporate increasingly sophisticated optimizations. Due to their complexity, it is difficult to systematically identify opportunities for improving them. Indeed, the automatic discovery of missed optimizations has been an important and significant challenge. The few existing approaches either cannot accurately pinpoint missed optimizations or target only specific analyses. This paper tackles this challenge by introducing a novel, effective approach that --- in a simple and general manner --- automatically identifies a wide range of missed optimizations. Our core insight is to leverage dead code elimination (DCE) to both analyze how well compilers optimize code and identify missed optimizations: (1) insert "optimization markers" in the basic blocks of a given program, (2) compute the program's live/dead basic blocks using the "optimization markers", and (3) identify missed optimizations from how well compilers eliminate dead blocks. We essentially exploit that, since DCE heavily depends on the rest of the optimization pipeline, through the lens of DCE, one can systematically quantify how well compilers optimize code. We conduct an extensive analysis of GCC and LLVM using our approach, which (1) provides quantitative and qualitative insights regarding their optimization capabilities, and (2) uncovers a diverse set of missed optimizations. Our results also lead to 84 bug reports for GCC and LLVM, of which 62 have already been confirmed or fixed, demonstrating our work's strong practical utility. We expect that the simplicity and generality of our approach will make it widely applicable for understanding compiler performance and finding missed optimizations. This work opens and initiates this promising direction.

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