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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

Getting Started with PyCUDA

In the last chapter, we set up our programming environment. Now, with our drivers and compilers firmly in place, we will begin the actual GPU programming! We will start by learning how to use PyCUDA for some basic and fundamental operations. We will first see how to query our GPU—that is, we will start by writing a small Python program that will tell us what the characteristics of our GPU are, such as the core count, architecture, and memory. We will then spend some time getting acquainted with how to transfer memory between Python and the GPU with PyCUDA's gpuarray class and how to use this class for basic computations. The remainder of this chapter will be spent showing how to write some basic functions (which we will refer to as CUDA Kernels) that we can directly launch onto the GPU.

The learning outcomes for this chapter are as follows...

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