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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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Table of Contents (11) Chapters Close

Preface 1. Revisiting Multithreading 2. Multithreading Implementation on the Processor and OS FREE CHAPTER 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

Debugging GPGPU applications


The biggest challenge with GPGPU applications is that of debugging a kernel. CUDA comes with a simulator for this reason, which allows one to run and debug a kernel on a CPU. OpenCL allows one to run a kernel on a CPU without modification, although this may not get the exact same behavior (and bugs) as when run on a specific GPU device.

A slightly more advanced method involves the use of a dedicated debugger such as Nvidia's Nsight, which comes in versions both for Visual Studio (https://developer.nvidia.com/nvidia-nsight-visual-studio-edition) and Eclipse (https://developer.nvidia.com/nsight-eclipse-edition).

According to the marketing blurb on the Nsight website:

NVIDIA Nsight Visual Studio Edition brings GPU computing into Microsoft Visual Studio (including multiple instances of VS2017). This application development environment for GPUs allows you to build, debug, profile and trace heterogeneous compute, graphics, and virtual reality applications built with CUDA...

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