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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

Summary

We started this chapter with a brief overview of the Python Ctypes library, which is used to interface directly with compiled binary code, and particularly dynamic libraries written in C/C++. We then looked at how to write a C-based wrapper with CUDA-C that launches a CUDA kernel, and then used this to indirectly launch our CUDA kernel from Python by writing an interface to this function with Ctypes. We then learned how to compile a CUDA kernel into a PTX module binary, which can be thought of as a DLL but with CUDA kernel functions, and saw how to load a PTX file and launch pre-compiled kernels with PyCUDA. Finally, we wrote a collection of Ctypes wrappers for the CUDA Driver API and saw how we can use these to perform basic GPU operations, including launching a pre-compiled kernel from a PTX file onto the GPU.

We will now proceed to what will arguably be the most technical...

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