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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 that you use nvcc to compile a single .cu file containing both host and kernel code into an EXE file, and also into a PTX file. Which file will contain the host functions, and which file will contain the GPU code?
  2. Why do we have to destroy a context if we are using the CUDA Driver API?
  3. At the beginning of this chapter when we first saw how to use Ctypes, notice that we had to typecast the floating point value 3.14 to a Ctypes c_double object in a call to printf before it would work. Yet we can see many working cases of not typecasting to Ctypes in this chapter. Why do you think printf is an exception here?
  4. Suppose you want to add functionality to our Python CUDA Driver interface module to support CUDA streams. How would you represent a single stream object in Ctypes?
  5. Why do we use extern "C" for functions in mandelbrot.cu?
  6. Look at mandelbrot_driver...
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