awesome-machine-learning-in-compilers
github.com/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
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Papers
- Absinthe: Learning an Analytical Performance Model to Fuse and Tile Stencil Codes in One Shot
Tobias Gysi, Tobias Grosser, and Torsten Hoefler. PACT 2019.
- Accelerated Auto-Tuning of GPU Kernels for Tensor Computations
Chendi Li and Yufan Xu and Sina Mahdipour Saravani and P. Sadayappan. ICS 2024.
- Accurate static estimators for program optimization
Tim A. Wagner, Vance Maverick, Susan L. Graham, and Michael A. Harrison. PLDI 1994.
- Achieving High-performance the Functional Way: a Functional Pearl on Expressing High-performance Optimizations as Rewrite Strategies
Bastian Hagedorn, Johannes Lenfers, Thomas K{\oe}hler, Xueying Qin, Sergei Gorlatch, and Michel Steuwer. Proceedings of the ACM on Programming Languages 2020.
- A Collaborative Filtering Approach for the Automatic Tuning of Compiler Optimisations
Stefano Cereda, Gianluca Palermo, Paolo Cremonesi, and Stefano Doni, LCTES 2020.
- A Convolutional Attention Network
for Extreme Summarization of Source Code
Miltos Allamanis, Hao Peng, and Charles Sutton. ICML 2016.
Conferences
- ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI
- ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP
- Architectural Support for Programming Languages and Operating Systems, ASPLOS
- Conference on Machine Learning and Systems, MLSys
- International Conference for High Performance Computing, Networking, Storage, and Analysis, SC
- International Conference on Compiler Construction, CC
Benchmarks and Datasets
- ANGHABENCH
A suite with One Million Compilable C Benchmarks (
- BHive
A Benchmark Suite and Measurement Framework for Validating x86-64 Basic Block Performance Models (
- cBench
32 C benchmarks with datasets and driver scripts.
- CodeXGLUE
A Machine Learning Benchmark Dataset for Code Understanding and Generation (
- DeepDataFlow
469k LLVM-IR files and 8.6B data-flow analysis labels for classification (
- devmap
650 OpenCL benchmark features and CPU/GPU classification labels (
Books
- Automatic Tuning of Compilers Using Machine Learning
Amir H. Ashouri, Gianluca Palermo, John Cavazos, and Cristina Silvano. Springer 2018.
Software
- clgen
Benchmark generator using LSTMs (
- COBAYN
Compiler Autotuning using BNs (
- CodeBert
pre-trained DNN models for programming languages (
- CompilerGym
Reinforcement learning environments for compiler optimizations (
- IR2Vec
LLVM IR based program embeddings for machine learning (
- IREE
A retargetable MLIR-based machine learning compiler and runtime toolkit.
Showing a sample of 235 resources. View the full list on GitHub →