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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 the Nsight IDE for CUDA-C development and debugging

Let's now learn how to use the Nsight IDE for developing CUDA-C programs. We will see how to import the program we just wrote, and compile and debug it from within Nsight. Note that there are differences between the Windows and Linux versions of Nsight, since it is effectively a plugin of the Visual Studio IDE under Windows and in the Eclipse IDE under Linux. We will cover both in the following two subsections; feel free to skip whatever operating system does not apply to you here.

Using Nsight with Visual Studio in Windows

Open up Visual Studio, and click on File, then choose New | Project.... A window will pop up where you set the type of project: choose the...

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