Path profile guided partial dead code elimination using predication

Rajiv Gupta, David A. Berson, J. Fang
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引用次数: 72

Abstract

Presents a path-profile-guided partial dead code elimination algorithm that uses predication to enable sinking for the removal of deadness along frequently executed paths at the expense of adding additional instructions along infrequently executed paths. Our approach to optimization is particularly suitable for VLIW architectures since it directs the efforts of the optimizer towards aggressively enabling generation of fast schedules along frequently executed paths by reducing their critical path lengths. The paper presents a cost-benefit data flow analysis that uses path profiling information to determine the profitability of using predication-enabled sinking. The cost of predication-enabled sinking of a statement past a merge point is determined by identifying paths along which an additional statement is introduced. The benefit of predication-enabled sinking is determined by identifying paths along which additional dead code elimination is achieved due to predication. The results of this analysis are incorporated in a code sinking framework in which predication-enabled sinking is allowed past merge points only if its benefit is determined to be greater than the cost. It is also demonstrated that trade-off can be performed between the compile-time cost and the precision of cost-benefit analysis.
路径配置文件引导使用预测的部分死码消除
提出了一种路径配置文件引导的部分死代码消除算法,该算法使用预测来支持在频繁执行的路径上移除死代码,代价是在不频繁执行的路径上添加额外的指令。我们的优化方法特别适合于VLIW体系结构,因为它通过减少关键路径的长度,引导优化器积极地沿着频繁执行的路径生成快速调度。本文提出了一种成本效益数据流分析方法,该方法使用路径剖面信息来确定使用支持预测的下沉的盈利能力。启用了预测的语句经过合并点下沉的代价是通过标识引入附加语句的路径来确定的。支持预测的下沉的好处是通过确定路径来确定的,由于预测,沿着这些路径可以消除额外的死代码。该分析的结果被合并到一个代码下沉框架中,在这个框架中,只有当确定其收益大于成本时,才允许启用预测的下沉越过合并点。还证明了在编译时成本和成本效益分析的精度之间可以进行权衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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