Diagnosing Compiler Performance by Comparing Optimization Decisions

Modern compilers apply a set of optimization passes aiming to speed up the generated code. The combined effect of individual optimizations is hard to predict. Thus, changes to a compiler’s code may hinder the performance of generated code as an unintended consequence. Performance regressions are often related to misapplied opti- mizations. The regressions are hard to investigate, considering the vast number of compilation units and applied optimizations. Ad- ditionally, a method may be part of several compilation units and optimized differently in each. Moreover, compiled methods and in- lining decisions are not invariant across runs of the virtual machine (VM). We propose to solve the problem of diagnosing performance regressions by capturing the compiler’s optimization decisions. We do so by representing the applied optimization phases, optimization decisions, and inlining decisions in the form of trees. This paper introduces an approach utilizing tree edit distance (TED) to detect optimization differences in a semi-automated way. We present an approach to compare optimization decisions in differently-inlined methods. We employ these techniques to pinpoint the causes of performance problems in various benchmarks of the Graal compiler.