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GPU Programming with C++ and CUDA

GPU Programming with C++ and CUDA

By : Paulo Motta
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GPU Programming with C++ and CUDA

GPU Programming with C++ and CUDA

By: Paulo Motta

Overview of this book

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.
Table of Contents (17 chapters)
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Lock Free Chapter
1
Understanding Where We Are Heading
6
Bring It On!
10
Moving Forward
15
Other Books You May Enjoy
16
Index

Overlaying Multiple Operations

An interesting possibility with GPUs is to execute multiple operations at the same time. Remember that NVIDIA GPUs have special hardware dedicated to performing memory transfers separate from the execution cores. By using CUDA streams, it is possible to execute memory transfers to and from the GPU at the same time as we execute a kernel, thereby overlaying the operations. This means we can have three things happening in an interleaved way.

Another possibility is to have more than one GPU on the same machine, although its higher cost means this is a less common setup. We will touch on this interesting alternative in a brief overview; it requires careful thinking about the algorithm to ensure we make the most of our devices.

But before we jump into those interesting topics, we will start this chapter by exploring how to use VS Code to debug our code. This is a useful skill, especially when we have multiple operations happening at the same time...

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GPU Programming with C++ and CUDA
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