GraalSP: Polyglot, efficient, and robust machine learning-based static profiler

Abstract

Compilers use profiles to apply profile-guided optimizations and produce efficient programs. Dynamic profilers collect high-quality profiles but require identifying suitable profile collection workloads, introduce additional complexity to the application build pipeline, and cause significant time and memory overheads. Modern static profilers use machine learning (ML) models to predict profiles and mitigate these issues. However, state-of-the-art ML-based static profilers handcraft features, which are platform-specific and challenging to adapt to other architectures and programming languages. They use computationally expensive deep neural network models, thus increasing application compile time. Furthermore, they can introduce performance degradation in the compiled programs due to inaccurate profile predictions. We present GraalSP, a portable, polyglot, efficient, and robust ML-based static profiler. GraalSP is portable as it defines features on a high-level, graph-based intermediate representation and semi-automates the definition of features. For the same reason, it is also polyglot and can operate on any language that compiles to Java bytecode (such as Java, Scala, and Kotlin). GraalSP is efficient as it uses an XGBoost model based on lightweight decision tree models and robust as it uses branch probability prediction heuristics to ensure the high performance of compiled programs. We integrated GraalSP into the Graal compiler and achieved an execution time speedup of 7.46% geometric mean compared to a default configuration of the Graal compiler.