Machine Learning in Compilers: A Collection of Research Papers and Tools

2023/06/21

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Machine learning is a rapidly growing field in computer science, and it has many applications in the world of compilers and systems optimization. The use of machine learning in compilers can lead to more efficient code generation, better program analysis, and improved performance. If you're interested in learning more about this exciting area of research, you should check out the "awesome-machine-learning-in-compilers" repository on GitHub.

The "awesome-machine-learning-in-compilers" repository is a collection of research papers, tools, and datasets related to using machine learning for compilers and systems optimization. It was created by zwang4, and it has over 900 stars and 100 forks on GitHub. The repository is a great resource for developers who want to keep up with the latest developments in this field.

The repository contains links to research papers that cover a wide range of topics related to machine learning in compilers. Some of these papers focus on using machine learning to optimize code generation, while others explore the use of machine learning for program analysis and debugging. There are also papers that discuss the use of machine learning for performance tuning and profiling.

In addition to research papers, the repository also contains links to tools and datasets that can be used for machine learning in compilers. Some of these tools are designed to help developers analyze and optimize their code, while others are designed to help researchers experiment with different machine learning algorithms and techniques.

One of the tools that is featured in the repository is the LLVM-MCA (Machine Code Analyzer) tool. LLVM-MCA is a performance analysis tool that uses machine learning to predict the performance of machine code. It can be used to identify performance bottlenecks in code and to optimize code generation for specific microarchitectures.

Another tool that is featured in the repository is the MLIR (Multi-Level Intermediate Representation) framework. MLIR is a new intermediate representation that is designed to support a wide range of machine learning applications. It can be used to represent both high-level and low-level code, and it can be used to optimize code generation for specific hardware targets.

If you're interested in learning more about machine learning in compilers, the "awesome-machine-learning-in-compilers" repository is a great resource to explore. It contains a wealth of information on research papers, tools, and datasets that can help you stay up-to-date with the latest developments in this exciting field.