The Next 700 ML-Enabled Compiler Optimizations
This addresses modularity and performance issues for researchers and practitioners in ML-enhanced compiler optimizations, though it appears incremental as a bridge tool rather than a new optimization method.
The paper tackles the challenge of integrating ML models with compilers by proposing ML-Compiler-Bridge, which enables ML development in Python while allowing efficient end-to-end integration with optimizing compilers, evaluated across multiple use cases, compilers, and optimization problems.
There is a growing interest in enhancing compiler optimizations with ML models, yet interactions between compilers and ML frameworks remain challenging. Some optimizations require tightly coupled models and compiler internals,raising issues with modularity, performance and framework independence. Practical deployment and transparency for the end-user are also important concerns. We propose ML-Compiler-Bridge to enable ML model development within a traditional Python framework while making end-to-end integration with an optimizing compiler possible and efficient. We evaluate it on both research and production use cases, for training and inference, over several optimization problems, multiple compilers and its versions, and gym infrastructures.