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GPU Programming with C++ and CUDA

You're reading from   GPU Programming with C++ and CUDA Uncover effective techniques for writing efficient GPU-parallel C++ applications

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Product type Paperback
Published in Aug 2025
Last Updated in Aug 2025
Publisher Packt
ISBN-13 9781805124542
Length 270 pages
Edition 1st Edition
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Author (1):
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Paulo Motta Paulo Motta
Author Profile Icon Paulo Motta
Paulo Motta
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Table of Contents (17) Chapters Close

Preface 1. Understanding Where We Are Heading FREE CHAPTER
2. Introduction to Parallel Programming 3. Setting Up Your Development Environment 4. Hello CUDA 5. Hello Again, but in Parallel 6. Bring It On!
7. A Closer Look into the World of GPUs 8. Parallel Algorithms with CUDA 9. Performance Strategies 10. Moving Forward
11. Overlaying Multiple Operations 12. Exposing Your Code to Python 13. Exploring Existing GPU Models 14. Unlock Your Book’s Exclusive Benefits 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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