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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 first CUDA-C program that we wrote, we didn't use a cudaDeviceSynchronize command after the calls we made to allocate memory arrays on the GPU with cudaMalloc. Why was this not necessary? (Hint: Review the last chapter.)
  2. Suppose we have a single kernel that is launched over a grid consisting of two blocks, where each block has 32 threads. Suppose all of the threads in the first block execute an if statement, while all of the threads in the second block execute the corresponding else statement. Will all of the threads in the second block have to "lockstep" through the commands in the if statement as the threads in the first block are actually executing them?
  3. What if we executed a similar piece of code, only over a grid consisting of one single block executed over 64 threads, where the first 32 threads execute an if and the second 32 execute an else...
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