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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 out in this chapter by seeing how printf can be used within a CUDA kernel to output data from individual threads; we saw in particular how useful this can be for debugging code. We then covered some of the gaps in our knowledge in CUDA-C, so that we can write full test programs that we can compile into proper executable binary files: there is a lot of overhead here that was hidden from us before that we have to be meticulous about. Next, we saw how to create and compile a project in the Nsight IDE and how to use it for debugging. We saw how to stop at any breakpoint we set in a CUDA kernel and switch between individual threads to see the different local variables. We also used the Nsight debugger to learn about the warp lockstep property and why it is important to avoid branch divergence in CUDA kernels. Finally, we had a very brief overview of the NVIDIA command...

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