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MLIR Foundations of Next Generation Compilers: Concepts, Dialects, and Progressive Lowering with the LLVM Multi Level IR

by Theo McKenzie
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₹2,561.00
Original price ₹2,561.00
Original price ₹2,561.00
₹2,561.00
Current price ₹2,561.00

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Book cover type: Paperback
  • ISBN13: 9798267960953
  • Binding: Paperback
  • Subject: N/A
  • Publisher: Independently Published
  • Publisher Imprint: Independently Published
  • Publication Date:
  • Pages: 250
  • Original Price: USD 24.5
  • Language: English
  • Edition: N/A
  • Item Weight: 440 grams
  • BISAC Subject(s): Programming / Compilers

Modern computing needs compilers that can handle multiple abstraction levels and target diverse hardware efficiently. MLIR (Multi-Level Intermediate Representation), developed as part of the LLVM ecosystem, is at the heart of this evolution. It enables compiler engineers to design modular, extensible, and scalable compilers that bridge high-level frameworks and low-level hardware backends.

MLIR Foundations of Next Generation Compilers is Volume One in the MLIR Foundations and Applications: Next Generation Compiler Engineering series. This volume introduces the core building blocks of MLIR, moving beyond documentation to provide step-by-step guidance, annotated code, and diagrams that make complex ideas approachable.

In Volume One, you will learn:

  • The evolution of compiler IRs and why MLIR is different

  • MLIR concepts: operations, attributes, regions, types, and blocks

  • How to install, configure, and use MLIR tools

  • Writing and verifying MLIR programs from scratch

  • Creating custom dialects, operations, and validation rules

  • Writing transformation passes and debugging pipelines

  • Progressive lowering from high-level dialects to LLVM IR

Preparing for Volume Two
This first volume gives you a strong foundation in MLIR concepts and workflows. Volume Two, MLIR Applied Development for Compilers and Accelerators, builds on these foundations and takes you into advanced territory, including:

  • Optimizing kernels with MLIR transformations

  • Integrating MLIR with TensorFlow, PyTorch, and XLA

  • Targeting GPUs, TPUs, and custom accelerators

  • End-to-end workflows from high-level frameworks to machine code

  • Real-world compiler case studies and lessons from industry adoption

Together, the two volumes form a complete learning path: from grasping the fundamentals of MLIR to mastering advanced applications in modern compiler engineering.

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