CUDA C++ Programming for GPU Computing : Learn Parallel Processing, GPU Acceleration, AI Workloads, High-Performance Computing, and Modern Software Development

Lucas Kazao

ISBN 13: 9798173497956

Published by Independently Published Sep 2026, 2026

About this Item

Neuware.

Seller Inventory # 9798173497956

Bibliographic Details

• Title: CUDA C++ Programming for GPU Computing : Learn Parallel Processing, GPU Acceleration, AI Workloads, High-Performance Computing, and Modern Software Development
• Publisher: Independently Published Sep 2026
• Publication Date: 2026
• Language: English
• ISBN 13: 9798173497956
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 229W x 152H x 9D millimeters 229 millimeters width by 152 millimeters height by 9 millimeters depth
• Weight: 222 grams

About this title

Synopsis

Your CPU is the bottleneck. Your GPU capable of thousands of parallel operations is sitting there, mostly idle.
Modern AI training, scientific simulation, and high-performance computing all run on the same insight: some problems are far better solved by thousands of simple operations running in parallel than by a handful of powerful sequential ones. CUDA C++ is how you tap into that power directly, and this book takes you from writing your very first kernel to genuinely optimized, production-grade GPU code.
Inside, you'll learn to:
Understand GPU architecture at a level that actually changes how you write code
Write, launch, and debug CUDA kernels with real confidence, not trial and error
Master the thread, block, and grid hierarchy that underlies all CUDA programming
Navigate global, shared, and constant memory and know which to use when
Optimize for real, measurable performance gains, not just "it runs on the GPU now"
Apply CUDA C++ to AI training workloads, scientific computing, and large-scale data processing
Use profiling tools to find and fix bottlenecks in your own kernels
Scale from single-GPU experiments to more advanced parallel computing patterns
This book is written for C++ developers who are ready to move into high-performance and AI-adjacent engineering roles the kind of roles where understanding what's happening at the hardware level is what separates "it works" from "it's fast." Rather than treating GPU programming as an intimidating black box, this guide builds your understanding progressively, with a structured path from fundamentals through advanced optimization techniques used in real machine learning and HPC systems.
The AI infrastructure boom model training, inference optimization, large-scale simulation runs on GPU code written by people who understand CUDA. This book is how you become one of them.
Get your copy today and put your GPU to work instead of letting it idle.