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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

Using the NVIDIA nvprof profiler and Visual Profiler

We will end with a brief overview of the command-line Nvidia nvprof profiler. In contrast to the Nsight IDE, we can freely use any Python code that we have written—we won't be compelled here to write full-on, pure CUDA-C test function code.

We can do a basic profiling of a binary executable program with the nvprof program command; we can likewise profile a Python script by using the python command as the first argument, and the script as the second as follows: nvprof python program.py. Let's profile the simple matrix-multiplication CUDA-C executable program that we wrote earlier, with nvprof matrix_ker:

We see that this is very similar to the output of the Python cProfiler module that we first used to analyze a Mandelbrot algorithm way back in Chapter 1, Why GPU Programming?—only now, this exclusively...

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