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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

Comparing CPUs and GPUs

Now that we know a little more about how GPUs are organized, we can compare them with CPUs to understand the impact of using these devices.Modern CPUs are typically composed of many cores, so they're capable of executing parallel applications by using threads. But while they’re capable of handling tens of threads, a simple GPU can handle thousands of threads.As mentioned previously, the fact that GPU cores execute the same instruction on many pieces of data is an interesting difference from CPU cores. On a CPU, each core is a complete processor that can execute either different applications or different threads of the same parallel application. This means that branch execution on a CPU core doesn't affect the performance of other CPU core executions.Another important distinction is that CPUs can switch between tasks quickly, while GPU cores are controlled by their stream multiprocessor.Regarding memory management, most of the time...

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