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GPU-Accelerated Computing with Python 3 and CUDA

You're reading from   GPU-Accelerated Computing with Python 3 and CUDA From low-level kernels to real-world applications in scientific computing and machine learning

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Product type Paperback
Published in Mar 2026
Publisher Packt
ISBN-13 9781803245423
Length 534 pages
Edition 1st Edition
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Authors (2):
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Niels Cautaerts Niels Cautaerts
Author Profile Icon Niels Cautaerts
Niels Cautaerts
Hossein Ghorbanfekr Hossein Ghorbanfekr
Author Profile Icon Hossein Ghorbanfekr
Hossein Ghorbanfekr
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Toc

Table of Contents (24) Chapters Close

Preface 1. Part 1: Fundamentals of GPU programming with CUDA in Python 3
2. Chapter 1: Why GPU Programming with CUDA in Python 3? FREE CHAPTER 3. Chapter 2: Setting Up a GPU Programming Environment Locally and in the Cloud 4. Chapter 3: Writing and Executing CUDA Kernels with Numba-CUDA 5. Chapter 4: Profiling and Debugging CUDA Code 6. Part 2: Performance Optimization and Advanced CUDA Topics
7. Chapter 5: Optimizing the Performance of CUDA Code 8. Chapter 6: Enabling Concurrency Using CUDA Streams 9. Chapter 7: Scaling to Multiple GPUs 10. Part 3: Using High-Level Python Libraries for GPU Computation
11. Chapter 8: Bringing NumPy and SciPy to the GPU with CuPy 12. Chapter 9: Bringing pandas and scikit-learn to the GPU with Rapids 13. Chapter 10: Solving Optimization Problems on the GPU with JAX 14. Part 4: Real-World Example Applications
15. Chapter 11: Solving the Heat Equation on the GPU 16. Chapter 12: Image Processing and Computer Vision on the GPU 17. Chapter 13: Simulating Atomic Interactions on the GPU 18. Chapter 14: Implementing Your Own Transformer-Based Language Model 19. Part 5: Beyond This Book
20. Chapter 15: Expanding and Deepening Your GPU Programming Knowledge 21. Chapter 16: Unlock Your Exclusive Benefits 22. Other Books You May Enjoy 23. Index

Technical requirements

To run the code in this chapter, you will need an installation of Python 3 with the numpy, matplotlib, numba, and scalene packages installed. The recommendation is to create a pixi environment based on the pixi.lock and pyproject.toml files in the Github repository associated with this book: https://github.com/PacktPublishing/GPU-Accelerated-Computing-with-Python-3-and-CUDA.

Installation instructions can be found in the README.md file.

If you've worked with Python for a while, you may not yet be familiar with the Pixi package manager. The reasons we chose Pixi for this book are explained in Chapter 2.

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GPU-Accelerated Computing with Python 3 and CUDA
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GPU-Accelerated Computing with Python 3 and CUDA
Published in: Mar 2026
Publisher: Packt
ISBN-13: 9781803245423
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