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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

Summary

We started with an implementation of Conway's Game of Life, which gave us an idea of how the many threads of a CUDA kernel are organized in a block-grid tensor-type structure. We then delved into block-level synchronization by way of the CUDA function, __syncthreads(), as well as block-level thread intercommunication by using shared memory; we also saw that single blocks have a limited number of threads that we can operate over, so we'll have to be careful in using these features when we create kernels that will use more than one block across a larger grid.

We gave an overview of the theory of parallel prefix algorithms, and we ended by implementing a naive parallel prefix algorithm as a single kernel that could operate on arrays limited by a size of 1,024 (which was synchronized with ___syncthreads and performed both the for and parfor loops internally), and...

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