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

Product type Paperback

Published in Mar 2026

Publisher Packt

ISBN-13 9781803245423

Length 534 pages

Edition 1st Edition

Languages

Python

Tools

CUDA

Concepts

Programming Language

Authors (2):

Niels Cautaerts

Hossein Ghorbanfekr

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Table of Contents (24) Chapters

Preface

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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

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22. Other Books You May Enjoy

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23. Index

Part 3

Using High-Level Python Libraries for GPU Computation

This part pivots to GPU computing with high-level libraries that mimic the API of familiar Python libraries. These libraries let us focus on quickly prototyping solutions rather than implementing every low-level detail from scratch. We'll use CuPy to replace NumPy and SciPy workflows, RAPIDS (cuDF/cuML) for pandas and scikit-learn tasks, and JAX for optimization and machine learning. Each chapter bridges high-level APIs with GPU performance and explores interoperability with custom low-level CUDA kernels.

This part of the book includes the following chapters: