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Mastering C++ Multithreading

You're reading from   Mastering C++ Multithreading Write robust, concurrent, and parallel applications

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781787121706
Length 244 pages
Edition 1st Edition
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Author (1):
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Maya Posch Maya Posch
Author Profile Icon Maya Posch
Maya Posch
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Toc

Table of Contents (17) Chapters Close

Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Revisiting Multithreading FREE CHAPTER 2. Multithreading Implementation on the Processor and OS 3. C++ Multithreading APIs 4. Thread Synchronization and Communication 5. Native C++ Threads and Primitives 6. Debugging Multithreaded Code 7. Best Practices 8. Atomic Operations - Working with the Hardware 9. Multithreading with Distributed Computing 10. Multithreading with GPGPU

Setting up a development environment


Regardless of which platform and GPU you have, the most important part of doing OpenCL development is to obtain the OpenCL runtime for one's GPU from its manufacturer. Here, AMD, Intel, and NVidia all provide an SDK for all mainstream platforms. For NVidia, OpenCL support is included in the CUDA SDK.

Along with the GPU vendor's SDK, one can also find details on their website on which GPUs are supported by this SDK.

Linux

After installing the vendor's GPGPU SDK using the provided instructions, we still need to download the OpenCL headers. Unlike the shared library and runtime file provided by the vendor, these headers are generic and will work with any OpenCL implementation.

For Debian-based distributions, simply execute the following command line:

$ sudo apt-get install opencl-headers

For other distributions, the package may be called the same, or something different. Consult the manual for one's distribution on how to find out the package name.

After installing...

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