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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

Filling in the gaps with CUDA-C

We will now go through the very basics of how to write a full-on CUDA-C program. We'll start small and just translate the fixed version of the little matrix multiplication test program we just debugged in the last section to a pure CUDA-C program, which we will then compile from the command line with NVIDIA's nvcc compiler into a native Windows or Linux executable file (we will see how to use the Nsight IDE in the next section, so we will just be doing this with only a text editor and the command line for now). Again, the reader is encouraged to look at the code we are translating from Python as we go along, which is available as the matrix_ker.py file in the repository.

Now, let's open our favorite text editor and create a new file entitled matrix_ker.cu. The extension will indicate that this is a CUDA-C program, which can be compiled...

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