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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 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

MPI communication


At this point, we have a functional MPI cluster, which can be used to execute MPI-based applications (and others, as well) in a parallel fashion. While for some tasks it might be okay to just send dozens or hundreds of processes on their merry way and wait for them to finish, very often it is crucial that these parallel processes are able to communicate with each other.

This is where the true meaning of MPI (being "Message Passing Interface") comes into play. Within the hierarchy created by an MPI job, processes can communicate and share data in a variety of ways. Most fundamentally, they can share and receive messages.

An MPI message has the following properties:

  • A sender
  • A receiver
  • A message tag (ID)
  • A count of the elements in the message
  • An MPI datatype

The sender and receiver should be fairly obvious. The message tag is a numeric ID which the sender can set and which the receiver can use to filter messages, to, for example, allow for the prioritizing of specific messages. The...

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