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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. Try rewriting the Monte Carlo integration examples (in the __main__ function in monte_carlo_integrator.py) to use the CUDA instrinsic functions. How does the accuracy compare to before?
  2. We only used the uniform distribution in all of our cuRAND examples. Can you name one possible use or application of using the normal (Gaussian) random distribution in GPU programming?
  3. Suppose that we use two different seeds to generate a list of 100 pseudo-random numbers. Should we ever concatenate these into a list of 200 numbers?
  4. In the last example, try adding __host__ before __device__ in the definition of our operator() function in the multiply_functor struct. Now, see if you can directly implement a host-side dot-product function using this functor without any further modifications.
  5. Take a look at the strided_range.cu file in the Thrust examples directory. Can you think of how...
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