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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. There are three for statements in this chapter's Mandelbrot example; however, we can only parallelize over the first two. Why can't we parallelize over all of the for loops here?
  2. What is something that Amdahl's Law doesn't account for when we apply it to offloading a serial CPU algorithm to a GPU?
  3. Suppose that you gain exclusive access to three new top-secret GPUs that are the same in all respects, except for core counts—the first has 131,072 cores, the second has 262,144 cores, and the third has 524,288 cores. If you parallelize and offload the Mandelbrot example onto these GPUs (which generates a 512 x 512 pixel image), will there be a difference in computation time between the first and second GPU? How about between the second and third GPU?
  4. Can you think of any problems with designating certain algorithms or blocks of code as parallelizable in the context of Amdahl's Law?
  5. Why should we use profilers instead of just using Python's time function?
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