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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

Warp shuffling

We will now look at what is known as warp shuffling. This is a feature in CUDA that allows threads that exist within the same CUDA Warp concurrently to communicate by directly reading and writing to each other's registers (that is, their local stack-space variables), without the use of shared variables or global device memory. Warp shuffling is actually much faster and easier to use than the other two options. This almost sounds too good to be true, so there must be a catch—indeed, the catch is that this only works between threads that exist on the same CUDA Warp, which limits shuffling operations to groups of threads of size 32 or less. Another catch is that we can only use datatypes that are 32 bits or less. This means that we can't shuffle 64-bit long long integers or double floating point values across a Warp.

Only 32-bit (or smaller) datatypes...
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