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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

Ensuring that we have the right hardware

For this book, we recommend that you have the following hardware as a minimum:

  • 64-bit Intel/AMD-based PC
  • 4 gigabytes (GB) of RAM
  • NVIDIA GeForce GTX 1050 GPU (or higher)

This configuration will ensure that you can comfortably learn GPU programming, run all of the examples in this book, and also run some of the other newer and interesting GPU-based software, such as Google's TensorFlow (a machine learning framework) or the Vulkan SDK (a cutting-edge graphics API).

Note that you must have an NVIDIA brand GPU to make use of this book! The CUDA Toolkit is proprietary for NVIDIA cards, so it won't work for programming Intel HD or Radeon GPUs.

As stated, we will be assuming that you are using either the Windows 10 or Ubuntu LTS (long-term support) release.

Ubuntu LTS releases generally have version numbers of the form 14.04, 16.04...
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