Modern applications in artificial intelligence, scientific computing, computer vision, financial modeling, game development, and data analytics increasingly rely on GPU acceleration to process massive workloads efficiently. GPU Programming with Modern C++ and CUDA provides a practical, project-driven approach to mastering GPU programming, enabling you to build scalable, high-performance applications using NVIDIA's CUDA platform and modern C++.
Designed for developers who want to move beyond traditional CPU programming, this book introduces the principles of parallel computing before guiding you through CUDA fundamentals, GPU architecture, memory management, kernel programming, synchronization, optimization strategies, and multi-GPU development. Every concept is reinforced through real-world examples and hands-on projects that help you understand not only how CUDA works but also why specific design choices lead to better performance.
As your knowledge grows, you'll explore advanced topics including asynchronous execution with CUDA streams, shared memory optimization, occupancy tuning, performance profiling, debugging GPU applications, and integrating GPU-accelerated code into larger software systems. You'll also learn how to build reusable GPU libraries that can be accessed from Python and other programming environments, making your solutions portable across modern development ecosystems.
Whether you're accelerating machine learning pipelines, processing large scientific datasets, or building compute-intensive applications, this book equips you with the practical techniques and performance engineering skills needed to develop efficient GPU software with confidence.
What You'll Learn
Understand the foundations of parallel computing and GPU architecture.
Set up a complete CUDA development environment using modern C++.
Write efficient CUDA kernels for high-performance parallel execution.
Manage GPU memory effectively for maximum throughput.
Optimize applications using shared memory, streams, and asynchronous execution.
Improve performance through profiling, benchmarking, and optimization techniques.
Scale applications across multiple GPUs.
Build reusable GPU libraries and integrate them with Python applications.
Apply GPU programming techniques to AI, scientific computing, image processing, simulation, and data-intensive workloads.
Adopt industry best practices for writing maintainable, scalable, and production-ready CUDA applications.
Who This Book Is For
This book is intended for C++ developers, software engineers, AI and machine learning practitioners, game developers, data scientists, researchers, and high-performance computing professionals who want to leverage GPU acceleration to solve computationally intensive problems. A working knowledge of modern C++ is recommended, while prior CUDA experience is helpful but not required.
