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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

GPU memory management


When using a CPU, one has to deal with a number of memory hierarchies, in the form of the main memory (slowest), to CPU caches (faster), and CPU registers (fastest). A GPU is much the same, in that, one has to deal with a memory hierarchy that can significantly impact the speed of one's applications.

Fastest on a GPU is also the register (or private) memory, of which we have quite a bit more than on the average CPU. After this, we get local memory, which is a memory shared by a number of processing elements. Slowest on the GPU itself is the memory data cache, also called texture memory. This is a memory on the card that is usually referred to as Video RAM (VRAM) and uses a high-bandwidth, but a relatively high-latency memory such as GDDR5.

The absolute slowest is using the host system's memory (system RAM), as this has to travel across the PCIe bus and through various other subsystems in order to transfer any data. Relative to on-device memory systems, host-device communication...

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