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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

Basic GPU profiling tools

Before discussing NVIDIA profilers, we make use of lightweight tools that can help us to get a quick sense of GPU utilization, execution time, and resource usage. We have already seen Python's built-in timing modules and the Scalene package in Chapter 1. These tools are useful during early development or smaller-scale experiments, where diving into the hundreds of metrics provided by a full GPU profiler is an overkill.

Next, we'll discuss a few categories of basic GPU profiling tools available to Python developers and Linux users.

Time profiling using the time and timeit modules

At the most fundamental level, timing our code is the simplest form of profiling. Python's built-in time and timeit modules are widely used for this purpose. The time module provides a straightforward way to measure elapsed wall-clock time between two points in the code. This is extremely useful for quick benchmarks and comparisons between different kernel implementations...

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