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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

Chapter 11, Performance Optimization in CUDA

  1. The fact that atomicExch is thread-safe doesn't guarantee that all threads will execute this function at the same time (which is not the case since different blocks in a grid can be executed at different times).
  2. A block of size 100 will be executed over multiple warps, which will not be synchronized within the block unless we use __syncthreads. Thus, atomicExch may be called at multiple times.
  3. Since a warp executes in lockstep by default, and blocks of size 32 or less are executed with a single warp, __syncthreads would be unnecessary.
  4. We use a naïve parallel sum within the warp, but otherwise, we are doing as many sums withatomicAdd as we would do with a serial sum. While CUDA automatically parallelizes many of these atomicAdd invocations, we could reduce the total number of required atomicAdd invocations by implementing...
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