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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? FREE CHAPTER 2. Setting Up Your GPU Programming Environment 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

Questions

  1. In the launch parameters for the kernel in the first example, our kernels were each launched over 64 threads. If we increase the number of threads to and beyond the number of cores in our GPU, how does this affect the performance of both the original to the stream version?
  2. Consider the CUDA C example that was given at the very beginning of this chapter, which illustrated the use of cudaDeviceSynchronize. Do you think it is possible to get some level of concurrency among multiple kernels without using streams and only using cudaDeviceSynchronize?
  3. If you are a Linux user, modify the last example that was given to operate over processes rather than threads.
  4. Consider the multi-kernel_events.py program; we said it is good that there was a low standard deviation of kernel execution durations. Why would it be bad if there were a high standard deviation?
  5. We only used 10 host...
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