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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? FREE CHAPTER 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) with the PyCUDA module is also required.

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

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