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Hands-On GPU Programming with Python and CUDA

You're reading from  Hands-On GPU Programming with Python and CUDA

Product type Book
Published in Nov 2018
Publisher Packt
ISBN-13 9781788993913
Pages 310 pages
Edition 1st Edition
Languages
Author (1):
Dr. Brian Tuomanen Dr. Brian Tuomanen
Profile icon Dr. Brian Tuomanen

Table of Contents (15) Chapters

Preface 1. Why GPU Programming? 2. Setting Up Your GPU Programming Environment 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

Technical requirements

A Linux or Windows 10 PC with a modern NVIDIA GPU (2016—onward) is required for this chapter, with all of the necessary GPU drivers and the CUDA Toolkit (9.0–onward) installed. A suitable Python 2.7 installation (such as Anaconda Python 2.7) that includes the PyCUDA module is also required.

This chapter's code is also available on GitHub, and can be found at https://github.com/PacktPublishing/Hands-On-GPU-Programming-with-Python-and-CUDA.

For more information about the prerequisites, check out the preface of this book. For more information about the software and hardware requirements, check out the README file at https://github.com/PacktPublishing/Hands-On-GPU-Programming-with-Python-and-CUDA.
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