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You're reading from Hands-On GPU Programming with Python and CUDA Explore high-performance parallel computing with CUDA

Product type Paperback

Published in Nov 2018

Publisher Packt

ISBN-13 9781788993913

Length 310 pages

Edition 1st Edition

Languages

Python

Tools

CUDA

Concepts

Graphics Programming

Author (1):

Dr. Brian Tuomanen

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Table of Contents (15) Chapters

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

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Questions

Can we run CUDA on our main processor's built-in Intel HD GPU? What about on a discrete AMD Radeon GPU?
Does this book use Python 2.7 or Python 3.7 for examples?
What program do we use in Windows to see what GPU hardware we have installed?
What command-line program do we use in Linux to see what GPU hardware we have installed?
What is the command we use in Linux to determine how much memory our system has?
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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Authors (1)

Tuomanen

Dr. Brian Tuomanen has been working with CUDA and General-Purpose GPU Programming since 2014. He received his Bachelor of Science in Electrical Engineering from the University of Washington in Seattle, and briefly worked as a Software Engineer before switching to Mathematics for Graduate School. He completed his Ph.D. in Mathematics at the University of Missouri in Columbia, where he first encountered GPU programming as a means for studying scientific problems. Dr. Tuomanen has spoken at the US Army Research Lab about General Purpose GPU programming, and has recently lead GPU integration and development at a Maryland based start-up company. He currently lives and works in the Seattle area.

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