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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

Questions

  1. Change the random vector in simple_scalar_multiply_kernel.py so that it is of a length of 10,000, and modify the i index in the definition of the kernel so that it can be used over multiple blocks in the form of a grid. See if you can now launch this kernel over 10,000 threads by setting block and grid parameters to something like block=(100,1,1) and grid=(100,1,1).
  2. In the previous question, we launched a kernel that makes use of 10,000 threads simultaneously; as of 2018, there is no NVIDIA GPU with more than 5,000 cores. Why does this still work and give the expected results?
  3. The naive parallel prefix algorithm has time complexity O(log n) given that we have n or more processors for a dataset of size n. Suppose that we use a naive parallel prefix algorithm on a GTX 1050 GPU with 640 cores. What does the asymptotic time complexity become in the case that n >>...
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