awesome-machine-learning-in-compilers

Why we included this project

Machine learning has become a regular part of compiler research, and the work is spread across programming languages, systems, and ML venues. This list gathers the papers, datasets, and tools that matter into one place, grouped by topic: iterative compilation, cost and performance models, parallelism mapping, memory and cache analysis, and program representation. That organization makes it a practical starting point for graduate students and researchers surveying the field, while the software and benchmark sections give engineers building auto-tuning or optimisation tooling concrete places to begin. The list is actively maintained and now includes recent work on LLMs and compilers, so it tracks the field rather than sitting as a static bibliography.