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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

Events

Events are objects that exist on the GPU, whose purpose is to act as milestones or progress markers for a stream of operations. Events are generally used to provide measure time duration on the device side to precisely time operations; the measurements we have been doing so far have been with host-based Python profilers and standard Python library functions such as time. Additionally, events they can also be used to provide a status update for the host as to the state of a stream and what operations it has already completed, as well as for explicit stream-based synchronization.

Let's start with an example that uses no explicit streams and uses events to measure only one single kernel launch. (If we don't explicitly use streams in our code, CUDA actually invisibly defines a default stream that all operations will be placed into).

Here, we will use the same useless...

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