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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 7, Using the CUDA Libraries with Scikit-CUDA

  1. SBLAH starts with an S, so this function uses 32-bit real floats. ZBLEH starts with a Z, which means it works with 128-bit complex floats.
  2. Hint: set trans = cublas._CUBLAS_OP['T']
  3. Hint: use the Scikit-CUDA wrapper to the dot product, skcuda.cublas.cublasSdot
  4. Hint: build upon the answer to the last problem.
  5. You can put the cuBLAS operations in a CUDA stream and use event objects with this stream to precisely measure the computation times on the GPU.
  6. Since the input appears as being complex to cuFFT, it will calculate all of the values as NumPy.
  7. The dark edge is due to the zero-buffering around the image. This can be mitigated by mirroring the image on its edges rather than by using a zero-buffer.

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