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.

Monitoring, analysing and understanding component based enterprise software systems are challenging tasks. These tasks are essential in solving and preventing performance and quality problems. Obtaining component level interactions which show the relationships between different software entities is a necessary prerequisite for such efforts. This paper focuses on component based Java applications, currently widely used by industry. They pose specific challenges while raising interesting opportunities for component level interaction extraction tools. We present a range of representative approaches for dynamically obtaining and using component interactions. For each approach we detail the needs it addresses, and the technical requirements for building an implementation of the approach. We also take a critical look at the different available implementations of the various techniques presented. We give performance and functional considerations and contrast them against each other by outlining their relative advantages and disadvantages. Based on this data, developers and system integrators can better understand the current state-of-the-art and the implications of choosing or implementing different dynamic interaction extraction techniques.