Textbook in PDF format

Key benefits
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
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.
Who is this book 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.
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