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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

Querying your GPU

Before we begin to program our GPU, we should really know something about its technical capacities and limits. We can determine this by doing what is known as a GPU query. A GPU query is a very basic operation that will tell us the specific technical details of our GPU, such as available GPU memory and core count. NVIDIA includes a command-line example written in pure CUDA-C called deviceQuery in the samples directory (for both Windows and Linux) that we can run to perform this operation. Let's take a look at the output that is produced on the author's Windows 10 laptop (which is a Microsoft Surface Book 2 with a GTX 1050 GPU):

Let's look at some of the essentials of all of the technical information displayed here. First, we see that there is only one GPU installed, Device 0—it is possible that a host computer has multiple GPUs and makes...

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