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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

In this chapter, we went over some of the options and paths for those that are interested in furthering their background in GPU programming, which is beyond the scope of this book. The first path we covered was expanding your background in pure CUDA and GPGPU programming—some of the things you can learn about that weren't covered in this book include programming systems with multiple GPUs and networked clusters. We also looked at some of the parallel programming languages/APIs besides CUDA, such as MPI and OpenCL. Next, we discussed some of the well-known APIs available to those who are interested in applying GPUs to rendering graphics, such as Vulkan and DirectX 12. We then looked at machine learning and went into some of the basic backgrounds that you should have as well as some of the major frameworks available for developing deep neural networks. Finally...

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