Awesome Tensor Compilers
In the rapidly evolving field of machine learning and tensor computation, keeping up with the latest innovations in compiler technology is crucial. The "Awesome Tensor Compilers" project is a comprehensive resource designed to guide learners, developers, and researchers through the vast landscape of compiler projects and academic papers dedicated to tensor computation and deep learning.
Contents Overview
The project offers a well-organized list of valuable resources categorized into several sections to facilitate easier exploration:
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Open Source Projects: Featuring a robust selection of compiler frameworks and languages designed for tensor computation. These projects range from end-to-end machine learning compiler frameworks like TVM, to hardware-specific compilers such as Glow for neural network accelerators. There are tools for both broad use and specific needs, such as high-performance abstractions and platforms for neural network computations.
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Papers: This section is a goldmine of academic insights, neatly divided into subcategories such as surveys, design innovations in compilers, auto-tuning techniques, cost modeling, and optimizations for CPUs, GPUs, NPUs, and more. Each subcategory showcases cutting-edge research that drives the development of more efficient and robust compiler technologies.
Highlights of Open Source Projects
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TVM (Tensor Virtual Machine): An end-to-end compiler stack for machine learning that is key in optimizing deep learning models across a variety of hardware devices. TVM automatically generates optimized code, making it easier for users to deploy models efficiently.
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MLIR (Multi-Level Intermediate Representation): Developed as a part of the LLVM project to enhance compiler technology. It provides a flexible, multi-layered approach to optimizing and compiling domain-specific computations.
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Halide: Originally created for image processing, Halide has evolved into a potent language and compiler for various tensor computations, known for its speed and portability across different platforms.
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Glow: This compiler optimizes neural network execution specifically for hardware accelerators, offering significant performance improvements in inference tasks.
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PlaidML: Focused on democratizing access to deep learning, this platform aims to run deep learning computations efficiently on a wide range of hardware, promoting inclusivity in AI development.
Noteworthy Papers
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Survey Papers: Provide a thorough analysis of the current landscape in deep learning compilers, alongside comparisons and evaluations of different compiler solutions available.
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Compiler and IR Design: Explores the intricate design patterns of compilers, focusing on intermediate representations critical for optimizing machine learning workloads.
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Auto-tuning and Auto-scheduling: Discuss new methodologies for automatically enhancing performance of tensor computations, crucial for achieving optimum efficiency in deep learning models.
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Optimization Studies: Cover a wide array of strategies aimed at enhancing computational performance on various processor architectures, including CPUs, GPUs, and NPUs.
Tutorials and Contribution
The project also curates tutorials for learners at various stages of their journey in understanding tensor compilers, offering guidance on how to leverage the technologies for their specific needs.
Furthermore, "Awesome Tensor Compilers" is an open community, encouraging contributions from interested individuals who wish to expand and update the resource with new and emerging content in the field of tensor compilers.
This repository serves as a critical resource for anyone involved in or interested in the intersection of machine learning and compiler design, making it an essential stop for researchers, practitioners, and enthusiasts eager to understand the latest developments and strategies in optimizing deep learning computations.