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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

The cuRAND device function library

Let's start with cuRAND. This is a standard CUDA library that is used for generating pseudo-random values within a CUDA kernel on a thread-by-thread basis, which is initialized and invoked by calling device functions from each individual thread within a kernel. Let's emphasize again that this is a pseudo-random sequence of values—since the digital hardware is always deterministic and never random or arbitrary, we use algorithms to generate a sequence of apparently random values from an initial seed value. Usually, we can set the seed value to a truly random value (such as the clock time in milliseconds), which will yield us with a nicely arbitrary sequence of random values. These generated random values have no correlation with prior or future values in the sequence generated by the same seed, although there can be correlations...

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