Shypd.aiThe largest open index of AI toolsawesome-machine-learning-in-compilers is a research & education tool that curates research papers, datasets, and tools for applying machine learning to compilers and program optimization. It provides resources for improving compiler performance using ML techniques.
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awesome-machine-learning-in-compilers is a comprehensive, curated list of research papers, datasets, and tools dedicated to the application of machine learning in compilers and program optimization. This GitHub repository serves as an invaluable resource for researchers, academics, and practitioners looking to explore and advance the field. It categorizes papers into key areas such as Survey, Iterative Compilation and Compiler Option Tuning, Instruction-level Optimisation, Parallelism Mapping and Task Scheduling, Languages and Compilation, Auto-tuning and Design Space Exploration, Code Size Reduction, Cost and Performance Models, Domain-specific Optimisation, Learning Program Representation, ML for Compilers and Systems Optimisation, and Memory/Cache Modelling/Analysis. Additionally, it provides links to relevant books, talks, tutorials, software, benchmarks, and datasets, making it a central hub for anyone interested in the synergy between machine learning and compiler technology.
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