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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

Questions

  1. Suppose you get a job translating some old legacy FORTRAN BLAS code to CUDA. You open a file and see a function called SBLAH, and another called ZBLEH. Can you tell what datatypes these two functions use without looking them up?
  2. Can you alter the cuBLAS level-2 GEMV example to work by directly copying the matrix A to the GPU, without taking the transpose on the host to set it column-wise?
  3. Use cuBLAS 32-bit real dot-product (cublasSdot) to implement matrix-vector multiplication using one row-wise matrix and one stride-1 vector.
  4. Implement matrix-matrix multiplication using cublasSdot.
  5. Can you implement a method to precisely measure the GEMM operations in the performance measurement example?
  6. In the example of the 1D FFT, try typecasting x as a complex64 array, and then switching the FFT and inverse FFT plans to be complex64 valued in both directions. Then confirm whether...
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