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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

Threads, blocks, and grids

So far in this book, we have been taking the term thread for granted. Let's step back for a moment and see exactly what this means—a thread is a sequence of instructions that is executed on a single core of the GPU—cores and threads should not be thought of as synonymous! In fact, it is possible to launch kernels that use many more threads than there are cores on the GPU. This is because, similar to how an Intel chip may only have four cores and yet be running hundreds of processes and thousands of threads within Linux or Windows, the operating system's scheduler can switch between these tasks rapidly, giving the appearance that they are running simultaneously. The GPU handles threads in a similar way, allowing for seamless computation over tens of thousands of threads.

Multiple threads are executed on the GPU in abstract units...

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