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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
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
2. Introduction to Parallel Programming FREE CHAPTER 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

Optimizing to speed up our code

Now that we’ve profiled our kernel we have our first report, which provides the information we need to optimize our code. In Figure 7.14 we see the basic information resulting from the execution we specified. We see the time it took to execute the kernel of the current session, and we can also see the launch configuration that was used: (125,125,1) blocks of (16,16,1) threads.

Figure 7.14: The summary of profiling the kernel

We also find, on the Summary page, a list of recommendations based on what the tool found out about our code that could be improved, as shown in Figure 7.15. However, we need to look at the details page to understand the recommendations in depth.

Figure 7.15: The recommendations for the kernel

We do this by selecting the Details page on the top left corner of the report, as shown in Figure 7.16.

Figure 7.16: Selecting the Details page

We then reach the Details page, where we have sections...

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