Understanding Optimization Phase Interactions to Reduce the Phase Order Search Space

Abstract

Compiler optimization phase ordering is a longstanding problem, and is of particular relevance to the performance-oriented and cost-constrained domain of embedded systems applications. Optimization phases are known to interact with each other, enabling and disabling opportunities for successive phases. Therefore, varying the order of applying these phases often generates distinct output codes, with different speed, code-size and power consumption characteristics. Most cur- rent approaches to address this issue focus on developing innovative methods to selectively evaluate the vast phase order search space to produce a good (but, potentially suboptimal) representation for each program. In contrast, the goal of this thesis is to study and reduce the phase order search space by: (1) identifying common causes of optimization phase interactions across all phases, and then devising techniques to eliminate them, and (2) exploiting natural phase independence to prune the phase order search space. We observe that several phase interactions are caused by false register dependence during many optimization phases. We explore the potential of cleanup phases, such as register remapping and copy propagation, at reducing false dependences. We show that innovative implementation and application of these phases not only reduces the size of the phase order search space substantially, but can also improve the quality of code generated by optimizing compilers. We examine the effect of removing cleanup phases, such as dead assignment elimination, which should not interact with other compiler phases, from the phase order search space. Finally, we show that reorganization of the phase order search into a multi-staged approach employing sets of mutually independent optimizations can reduce the search space to a fraction of its original size without sacrificing performance.