Hardware-Agnostic Mojo : Writing Portable Kernels from CPU to GPU
Language: English
Published by Independently Published Mai 2026, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 34.21
Quantity: 2 available
Add to basketItem description from seller
Neuware - Hardware-Agnostic Mojo: Writing Portable Kernels from CPU to GPUFor decades, software engineers and AI practitioners have been trapped by a brutal development compromise: write highly portable code that runs slowly everywhere, or write hyper-optimized parallel code that locks your infrastructure to a single hardware vendor. If you build performance-critical applications or deep learning pipelines using proprietary frameworks like CUDA, your software is restricted exclusively to a single ecosystem. Changing your cloud infrastructure means facing a multi-million-dollar rewriting penalty.Hardware-Agnostic Mojo shatters this monopoly. This comprehensive manual introduces the paradigm-shifting capabilities of the Mojo programming language and the Multi-Level Intermediate Representation (MLIR) compiler framework. This book provides a production-ready blueprint for designing mathematical algorithms, spatial convolution filters, and high-performance tensor workloads that decouple execution logic from physical silicon constraints.Moving systematically from core systems hardware architecture to low-level compilation mechanics, you will learn how to: - Harness Compile-Time Metaprogramming: Replace fragile C++ preprocessor macros with type-safe parameters to freeze loop boundaries and memory strides directly into compiled binaries.- Optimize Matrix and Vector Primitives: Transition traditional mental models into Mojo's safety-first syntax, using register tiling, stride tuning, first-class SIMD vectors, and lane masks to maximize single-core processing capabilities.- Architect Universal Thread Grids: Map multidimensional data topologies and coordinate indices smoothly across diverse processing systems whether routing to safe scalar CPU worker pools or fine-grained parallel GPU streaming multiprocessors.- Deploy Portably to Production: Package highly optimized kernels into unmanaged C-ABI shared libraries, implement dynamic runtime CPU dispatching safety layers, and insulate your codebase from vendor lock-in forever.Complete with complete, official Mojo standard library code examples, benchmarks, and technical unit exercises, this manual eliminates the abstract 'lowest common denominator penalty' of legacy wrappers. You will write hardware-agnostic code once, and let the compiler scale your architectural intent to the absolute performance ceiling of whatever chip it encounters next.Stop wasting valuable engineering cycles maintaining fractured, vendor-locked codebases. Click the button below to buy your copy of Hardware-Agnostic Mojo today, master the future of high-performance parallel computing, and build runtime-resilient code paths that conquer any silicon runway from CPU to GPU.…
Seller Inventory # 9798198417014
- Title
- Hardware-Agnostic Mojo : Writing Portable Kernels from CPU to GPU
- Author
- Ronald Cioffi
- Publisher
- Independently Published Mai 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798198417014
- Item weight
- 409 grams
- Dimensions
- 244x170x13 mm
Hardware-Agnostic Mojo: Writing Portable Kernels from CPU to GPU
For decades, software engineers and AI practitioners have been trapped by a brutal development compromise: write highly portable code that runs slowly everywhere, or write hyper-optimized parallel code that locks your infrastructure to a single hardware vendor. If you build performance-critical applications or deep learning pipelines using proprietary frameworks like CUDA, your software is restricted exclusively to a single ecosystem. Changing your cloud infrastructure means facing a multi-million-dollar rewriting penalty.
Hardware-Agnostic Mojo shatters this monopoly. This comprehensive manual introduces the paradigm-shifting capabilities of the Mojo programming language and the Multi-Level Intermediate Representation (MLIR) compiler framework. This book provides a production-ready blueprint for designing mathematical algorithms, spatial convolution filters, and high-performance tensor workloads that decouple execution logic from physical silicon constraints.
Moving systematically from core systems hardware architecture to low-level compilation mechanics, you will learn how to:
- Harness Compile-Time Metaprogramming: Replace fragile C++ preprocessor macros with type-safe parameters to freeze loop boundaries and memory strides directly into compiled binaries.
- Optimize Matrix and Vector Primitives: Transition traditional mental models into Mojo's safety-first syntax, using register tiling, stride tuning, first-class SIMD vectors, and lane masks to maximize single-core processing capabilities.
- Architect Universal Thread Grids: Map multidimensional data topologies and coordinate indices smoothly across diverse processing systems whether routing to safe scalar CPU worker pools or fine-grained parallel GPU streaming multiprocessors.
- Deploy Portably to Production: Package highly optimized kernels into unmanaged C-ABI shared libraries, implement dynamic runtime CPU dispatching safety layers, and insulate your codebase from vendor lock-in forever.
Complete with complete, official Mojo standard library code examples, benchmarks, and technical unit exercises, this manual eliminates the abstract "lowest common denominator penalty" of legacy wrappers. You will write hardware-agnostic code once, and let the compiler scale your architectural intent to the absolute performance ceiling of whatever chip it encounters next.
Stop wasting valuable engineering cycles maintaining fractured, vendor-locked codebases. Click the button below to buy your copy of Hardware-Agnostic Mojo today, master the future of high-performance parallel computing, and build runtime-resilient code paths that conquer any silicon runway from CPU to GPU"Synopsis" may belong to another edition of this title.
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