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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

Chapter 3, Getting Started with PyCUDA

  1. Yes.
  2. Memory transfers between host/device, and compilation time.
  3. You can, but this will vary depending on your GPU and CPU setup.
  4. Do this using the C ? operator for both the point-wise and reduce operations.
  5. If a gpuarray object goes out of scope its destructor is called, which will deallocate (free) the memory it represents on the GPU automatically.
  6. ReductionKernel may perform superfluous operations, which may be necessary depending on how the underlying GPU code is structured. A neutral element will ensure that no values are altered as a result of these superfluous operations.
  7. We should set neutral to the smallest possible value of a signed 32-bit integer.
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