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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

Summary

Setting up your Python environment for GPU programming can be a very delicate process. The Anaconda Python 2.7 distribution is suggested for both Windows and Linux users for the purposes of this text. First, we should ensure that we have the correct hardware for GPU programming; generally speaking, a 64-bit Windows or Linux PC with 4 gigabytes of RAM and any entry-level NVIDIA GPU from 2016 or later will be sufficient for our ends. Windows users should be careful in using a version of Visual Studio that works well with both the CUDA Toolkit and Anaconda (such as VS 2015), while Linux users should be particularly careful in the installation of their GPU drivers, and set up the appropriate environment variables in their .bashrc file. Furthermore, Windows users should create an appropriate launch script that will set up their environment for GPU programming and should use...

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