Exploiting Partially Context-Sensitive Profiles to Improve Performance of Hot Code

Abstract

Availability of profiling information is a major advantage of just-in-time (JIT) compilation. Profiles guide the compilation order and optimizations, thus substantially improving program performance. Ahead-of-time (AOT) compilation can also utilize profiles, obtained during separate profiling runs of the programs. Profiles can be context-sensitive, i.e., each profile entry is associated with a call-stack. To ease profile collection and reduce overheads, many systems collect partially context-sensitive profiles, which record only a call-stack suffix. Despite prior related work, partially context-sensitive profiles have the potential to further improve compiler optimizations. In this paper, we describe a novel technique that exploits partially context-sensitive profiles to determine which portions of code are hot, and compile them with additional compilation budget. This technique is applicable to most AOT compilers that can access partially context-sensitive profiles, and its goal is to improve program performance without significantly increasing code size. The technique relies on a new hot-code-detection algorithm to reconstruct hot regions based on the partial profiles. The compilation ordering and the inlining of the compiler are modified to exploit the information about the hot code. We formally describe the proposed algorithm and its heuristics, and then describe our implementation inside GraalVM Native Image, a state-of-the-art AOT compiler for Java. Evaluation of the proposed technique on 16 benchmarks from DaCapo, Scalabench and Renaissance suites shows a performance improvement between \(22\% \) and \(40\% \) on 4 benchmarks, and between \(2.5\% \) and \(10\% \) on 5 benchmarks. Code-size increase ranges from \(0.8-9\% \) , where 10 benchmarks exhibit an increase of less than \(2.5\% \) .