MLIR for Compiler and Machine Learning Infrastructure (eBook, ePUB)
The Complete Guide for Developers and Engineers
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"MLIR for Compiler and Machine Learning Infrastructure" "MLIR for Compiler and Machine Learning Infrastructure" is a comprehensive guide to the revolutionary Multi-Level Intermediate Representation (MLIR) technology at the forefront of modern compiler and machine learning developments. This book begins by elucidating the motivations behind MLIR, assessing the limitations of traditional compiler infrastructures, and unveiling the modular, hierarchical design principles that empower MLIR to serve varied domains. Through detailed discussions of extensibility and customization via dialects, the te...
"MLIR for Compiler and Machine Learning Infrastructure" "MLIR for Compiler and Machine Learning Infrastructure" is a comprehensive guide to the revolutionary Multi-Level Intermediate Representation (MLIR) technology at the forefront of modern compiler and machine learning developments. This book begins by elucidating the motivations behind MLIR, assessing the limitations of traditional compiler infrastructures, and unveiling the modular, hierarchical design principles that empower MLIR to serve varied domains. Through detailed discussions of extensibility and customization via dialects, the text methodically addresses the needs of compiler developers, systems engineers, and machine learning practitioners seeking to harness flexible and scalable IR frameworks. Spanning foundational concepts to advanced optimizations and deployment, the book explores the full breadth of MLIR's capabilities. Readers are introduced to the theory and practice of constructing custom dialects, implementing transformations using pass management infrastructure, and orchestrating efficient lowering strategies capable of targeting heterogeneous hardware environments. Real-world case studies and deep dives into topics such as SSA, symbol management, graph-level optimizations, and integration with leading frameworks like TensorFlow and PyTorch, offer invaluable insights into building robust, high-performance compiler pipelines for AI and beyond. The book also ventures beyond core compilation mechanics, investigating serialization strategies, cross-IR interoperability, ecosystem tools, and practical deployment in distributed and secure contexts. Special attention is given to emerging trends-AI-driven optimizations, domain-specific code generation paths, hardware/IR co-design, and quantum computing applications-making this volume a vital reference for engineers, architects, researchers, and anyone aiming to lead or innovate in the evolving landscape of compiler and machine learning infrastructure.
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