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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

Installing the GPU drivers

If you already have drivers for your GPU installed, you may possibly skip this step; moreover, some versions of CUDA are pre-packaged with the latest drivers. Quite often, CUDA is very particular about which driver you have installed and may not even work with the CUDA Toolkit driver, so you may have to experiment with several different drivers before you find one that works.

Generally speaking, Windows has better CUDA driver compatibility and a more user-friendly installation than Linux. Windows users may consider skipping this step and just use the driver that is packaged with the CUDA Toolkit, which we will install a little later in this chapter. We would strongly suggest that Linux users (particularly Linux laptop users), however, closely follow all the steps in this section before proceeding.

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