GPU Programming with C++ and CUDA: Uncover effective techniques for writing efficient GPU-parallel C++ applications

Learn to solve parallel problems with GPU-accelerated C++ code and create reusable libraries that can be accessed from other programming languages Key Features Harness the power of GPU parallelism to accelerate real-world tasks Utilize CUDA streams and scale performance with custom C++ solutions Create reusable GPU libraries and expose them to Python seamlessly Book Description Written by Paulo Motta, a senior researcher with decades of experience, this comprehensive GPU programming book is an essential guide for leveraging the power of parallelism to accelerate your computations. The first section introduces the concept of parallelism and provides practical advice on how to think about and utilize it effectively. Starting with a basic GPU program, you then gain hands-on experience in managing the device. This foundational knowledge is then expanded by parallelizing the program to illustrate how GPUs enhance performance. The second section explores GPU architecture and implementation strategies for parallel algorithms, and offers practical insights into optimizing resource usage for efficient execution. In the final section, you will explore advanced topics such as utilizing CUDA streams. You will also learn how to package and distribute GPU-accelerated libraries for the Python ecosystem, extending the reach and impact of your work. Combining expert insight with real-world problem solving, this book is a valuable resource for developers and researchers aiming to harness the full potential of GPU computing. The blend of theoretical foundations, practical programming techniques, and advanced optimization strategies it offers is sure to help you succeed in the fast-evolving field of GPU programming. What you will learn Manage GPU devices and accelerate your applications Apply parallelism effectively using CUDA and C++ Choose between existing libraries and custom GPU solutions Package GPU code into libraries for use with Python Explore advanced topics such as CUDA streams Implement optimization strategies for resource-efficient execution Who this book is for C++ developers and programmers interested in accelerating applications using GPU programming will benefit from this book. It is suitable for those with solid C++ experience who want to explore high-performance computing techniques. Familiarity with operating system fundamentals will help when dealing with device memory and communication in advanced chapters. Table of Contents Introduction to Parallel Programming Getting Started Hello CUDA Hello again, but in parallel A closer look into the GPU world Data Management and Persistence Performance strategies Using multiple GPUs Exposing your code as a Python Library Exploring the existing GPU models