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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

Using printf from within CUDA kernels

It may come as a surprise, but we can actually print text to the standard output from directly within a CUDA kernel; not only that, each individual thread can print its own output. This will come in particularly handy when we are debugging our kernels, as we may need to monitor the values of particular variables or computations at particular points in our code and it will also free us from the shackles of using a debugger to go through step by step. Printing output from a CUDA kernel is done with none other than the most fundamental function in all of C/C++ programming, the function that most people will learn when they write their first Hello world program in C: printf. Of course, printf is the standard function that prints a string to the standard output, and is really the equivalent in the C programming language of Python's print function...

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