# zwang4/awesome-machine-learning-in-compilers

Must read research papers and links to tools and datasets that are related to using machine learning for compilers and systems optimisation

Repository: https://github.com/zwang4/awesome-machine-learning-in-compilers
Canonical: https://ross.abutalabs.com/products/awesome-machine-learning-in-compilers
License: CC0-1.0
License Family: permissive
Topics: machine-learning, compiler, optimisation, parallel-computing, parallel-programming, parallelism, parallelisation, artificial-intelligence, operating-systems, auto-tuning, multi-cores
Last push: 2026-08-26T07:47:07+00:00

## Health v2 (maintenance only)
Score: 77/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 35, longevity 100
- inputs: {"age_days": 2268, "days_push": 7, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1689, forks 180 (observed 2026-08-28T04:05:22.466440+00:00)

## What it is
A curated awesome-list of research papers, books, talks, software tools, benchmarks, and datasets for applying machine learning to compilers and systems/program optimisation. It is maintained as a reference resource rather than a runnable software artifact.

## Use cases
- find research papers on machine learning for compiler optimisation
- discover datasets and benchmarks for ML-based compiler tuning
- learn about autotuning and compiler option tuning with ML
- find tools for ML-guided program optimisation
- survey the state of ML for compilers and systems research
- locate conferences and journals publishing ML-for-compilers work

## When to choose
- you are starting research at the intersection of ML and compilers
- you need a curated reading list of surveys and papers on compiler autotuning
- you want benchmarks or datasets for ML-based program optimisation experiments

## When to avoid
- you need runnable compiler or ML software rather than references
- you want a tutorial with hands-on code rather than a paper index
- you need production tooling for compiler optimisation

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, compiler, developer-tools, documentation
- domain: machine-learning, compilers, programming-languages, awesome-lists, tutorials, artificial-intelligence
- platform: cross-platform
- tags: awesome-list, research-papers, compilers, program-optimisation, auto-tuning, datasets, curated-list

## Member repositories
- zwang4/awesome-machine-learning-in-compilers (main) score 77

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:22.466440+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:38:42.402270+00:00, confidence not recorded.
  - readme: https://github.com/zwang4/awesome-machine-learning-in-compilers (fetched 2026-08-28T04:05:22.466440+00:00, sha 84f499ba2f6b)
- Data as of 2026-08-30T08:39:29.467469+00:00.