Learn GPU Programming with C++ and CUDA: A Hands-On Guide to Accelerating Your Code With NVIDIA GPUs

In today’s world of data-heavy applications, artificial intelligence, and high-performance computing, GPU acceleration has become essential. Learn GPU Programming with C++ and CUDA is your complete, hands-on guide to harnessing the raw computational power of NVIDIA GPUs — from the fundamentals of CUDA programming to real-world, high-performance application design.

Written for developers, engineers, and researchers who want to push their applications beyond CPU limits, this book takes you step by step through the core principles, coding practices, and performance techniques that define modern GPU programming. You’ll gain a solid understanding of how GPUs differ from CPUs, how to design efficient CUDA kernels, and how to optimize your code for speed, scalability, and portability across architectures.

With crystal-clear explanations and practical examples, you’ll explore topics such as thread hierarchies, memory models, kernel synchronization, CUDA libraries, concurrency, and multi-GPU scaling. Each chapter is built around real-world scenarios and actionable exercises, guiding you from basic concepts to advanced optimization and debugging strategies.

Beyond technical mastery, this book focuses on writing maintainable, error-tolerant, and future-proof GPU applications. You’ll learn how to measure performance correctly, avoid common pitfalls, and apply professional-level debugging and profiling techniques using NVIDIA’s tools. The later chapters explore GPU-powered AI, scientific computing, financial modeling, and cross-platform development, ensuring you can adapt your skills across diverse industries.

Whether you’re a C++ programmer diving into parallel computing for the first time or an experienced developer aiming to refine your GPU expertise, Learn GPU Programming with C++ and CUDA will give you the confidence, understanding, and skills to build efficient, scalable, and cutting-edge GPU applications.

By the end, you won’t just understand GPU programming — you’ll be ready to design, optimize, and deploy real-world GPU-accelerated systems that deliver breakthrough performance in any computational domain.

Key Takeaways

Master the fundamentals of CUDA programming with C++
Learn how to map data efficiently to threads, blocks, and grids
Understand GPU memory hierarchies and optimization strategies
Build scalable applications using multiple GPUs and CUDA libraries
Profile, debug, and tune your applications for peak performance
Explore real-world implementations in AI, finance, and scientific computing
Develop portable, maintainable, and future-ready GPU code