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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

Testing your code with GTest and Pytest

Creating our code is the first part, but we cannot deliver it until we’ve made sure that everything is working properly. To guarantee this it is a very good idea to have automated, repeatable tests in place that will execute again and again to make sure that any new changes do not introduce regressions into our code.

TDD starts with the test code

When using test-driven development, we first create a test that calls our code, let’s say a function, and then we create a version of the function that simply returns false or null. With that version, we run the test and it will fail. Then we implement the minimum amount of code necessary to make the test pass. We then iterate these steps, creating multiple tests, until we have fully functional and tested code that we can rely on. With many different tests for each piece of code we can cover different error scenarios, corner cases and boundaries, drastically increasing...

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