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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

Using PyCUDA's gpuarray class

Much like how NumPy's array class is the cornerstone of numerical programming within the NumPy environment, PyCUDA's gpuarray class plays an analogously prominent role within GPU programming in Python. This has all of the features you know and love from NumPy—multidimensional vector/matrix/tensor shape structuring, array-slicing, array unraveling, and overloaded operators for point-wise computations (for example, +, -, *, /, and **).

gpuarray is really an indispensable tool for any budding GPU programmer. We will spend this section going over this particular data structure and gaining a strong grasp of it before we move on.

Transferring data to and from the GPU with gpuarray

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