CATBench: A Compiler Autotuning Benchmarking Suite for Black-box Optimization

TL;DR

CATBench is a comprehensive benchmarking suite designed to evaluate Bayesian optimization methods on complex compiler autotuning tasks, addressing structural challenges and promoting reproducibility in the domain.

Contribution

It introduces a standardized, versatile benchmarking suite capturing real-world autotuning complexities, facilitating fair comparison and advancement of Bayesian optimization algorithms.

Findings

Validated on state-of-the-art algorithms, revealing their strengths and weaknesses.

Demonstrated the suite's potential to advance research in Bayesian optimization and compiler autotuning.

Promoted reproducibility through an easy-to-use, containerized setup.

Abstract

Bayesian optimization is a powerful method for automating tuning of compilers. The complex landscape of autotuning provides a myriad of rarely considered structural challenges for black-box optimizers, and the lack of standardized benchmarks has limited the study of Bayesian optimization within the domain. To address this, we present CATBench, a comprehensive benchmarking suite that captures the complexities of compiler autotuning, ranging from discrete, conditional, and permutation parameter types to known and unknown binary constraints, as well as both multi-fidelity and multi-objective evaluations. The benchmarks in CATBench span a range of machine learning-oriented computations, from tensor algebra to image processing and clustering, and uses state-of-the-art compilers, such as TACO and RISE/ELEVATE. CATBench offers a unified interface for evaluating Bayesian optimization algorithms, promoting reproducibility and innovation through an easy-to-use, fully containerized setup of both surrogate and real-world compiler optimization tasks. We validate CATBench on several state-of-the-art algorithms, revealing their strengths and weaknesses and demonstrating the suite's potential for advancing both Bayesian optimization and compiler autotuning research.