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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? 2. Setting Up Your GPU Programming Environment FREE CHAPTER 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

We started this chapter by looking at how to use the wrappers for the cuBLAS library from Scikit-CUDA; we have to keep many details in mind here, such as when to use column-major storage, or if an input array will be overwritten in-place. We then look at how to perform one- and two-dimensional FFTs with cuFFT from Scikit-CUDA, and how to create a simple convolutional filter. We then showed you how to apply this for a simple Gaussian blurring effect on an image. Finally, we looked at how to perform a singular value decomposition (SVD) on the GPU with cuSolver, which is normally a very computationally onerous operation, but which parallelizes fairly well onto the GPU. We ended this chapter by looking at how to use the SVD for basic PCA.

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