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Hands-On GPU Programming with Python and CUDA

You're reading from   Hands-On GPU Programming with Python and CUDA Explore high-performance parallel computing with CUDA

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Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781788993913
Length 310 pages
Edition 1st Edition
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Author (1):
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Dr. Brian Tuomanen Dr. Brian Tuomanen
Author Profile Icon Dr. Brian Tuomanen
Dr. Brian Tuomanen
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Table of Contents (15) Chapters Close

Preface 1. Why GPU Programming? 2. Setting Up Your GPU Programming Environment FREE CHAPTER 3. Getting Started with PyCUDA 4. Kernels, Threads, Blocks, and Grids 5. Streams, Events, Contexts, and Concurrency 6. Debugging and Profiling Your CUDA Code 7. Using the CUDA Libraries with Scikit-CUDA 8. The CUDA Device Function Libraries and Thrust 9. Implementation of a Deep Neural Network 10. Working with Compiled GPU Code 11. Performance Optimization in CUDA 12. Where to Go from Here 13. Assessment 14. Other Books You May Enjoy

Working with Compiled GPU Code

Throughout the course of this book, we have generally been reliant on the PyCUDA library to interface our inline CUDA-C code for us automatically, using just-in-time compilation and linking with our Python code. We might recall, however, that sometimes the compilation process can take a while. In Chapter 3, Getting Started With PyCUDA, we even saw in detail how the compilation process can contribute to slowdown, and how it can even be somewhat arbitrary as to when inline code will be compiled and retained. In some cases, this may be inconvenient and cumbersome given the application, or even unacceptable in the case of a real-time system.

To this end, we will finally see how to use pre-compiled GPU code from Python. In particular, we will look at three distinct ways to do this. First, we will look at how we can do this by writing a host-side CUDA...

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