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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

Contexts

A CUDA context is usually described as being analogous to a process in an operating system. Let's review what this means—a process is an instance of a single program running on a computer; all programs outside of the operating system kernel run in a process. Each process has its own set of instructions, variables, and allocated memory, and is, generally speaking, blind to the actions and memory of other processes. When a process ends, the operating system kernel performs a cleanup, ensuring that all memory that the process allocated has been de-allocated, and closing any files, network connections, or other resources the process has made use of. (Curious Linux users can view the processes running on their computer with the command-line top command, while Windows users can view them with the Windows Task Manager).

Similar to a process, a context is associated...

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