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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

Thread synchronization and intercommunication

We'll now discuss two important concepts in GPU programming—thread synchronization and thread intercommunication. Sometimes, we need to ensure that every single thread has reached the same exact line in the code before we continue with any further computation; we call this thread synchronization. Synchronization works hand-in-hand with thread intercommunication, that is, different threads passing and reading input from each other; in this case, we'll usually want to make sure that all of the threads are aligned at the same step in computation before any data is passed around. We'll start here by learning about the CUDA __syncthreads device function, which is used for synchronizing a single block in a kernel.

Using...

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