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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

Questions

  1. Can we run CUDA on our main processor's built-in Intel HD GPU? What about on a discrete AMD Radeon GPU?
  2. Does this book use Python 2.7 or Python 3.7 for examples?
  3. What program do we use in Windows to see what GPU hardware we have installed?
  4. What command-line program do we use in Linux to see what GPU hardware we have installed?
  5. What is the command we use in Linux to determine how much memory our system has?
  6. If we don't want to alter our Linux system's APT repository, should we use the run or deb installer for CUDA?
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