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How to communicate std::vector<> across processor boundaries

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Old   July 23, 2014, 08:10
Question How to communicate std::vector<> across processor boundaries
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Salman Arshad
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I am running a solver in parallel in OpenFOAM. in this solver I compiled the subgrid combustion model (which is not written in OpenFOAM and uses standard C++ library) with OpenFOAM.
On the faces between processor boundaries I want to read and make a local copy of std::vector<double> (contained in the subgrid combustion model on other processor). How can I do that?

Any help is highly appreciated.Thanks a lot
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Old   July 23, 2014, 12:36
Default std::vector<double>
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Hi there Saloo,

Well, I saw your PM message, but it wasn't very clear what you are trying to do.

First of all, I recommend you try to convert your data type to a standard OpenFOAM type, to make things easier.

Then, if what you are trying to do is access/store your data on many processors, you can try to access the global object database as explained here. Look a bit lower in the post, I think I explained some other things there too.

You can also find more information about inter-processor communication here.

And a short intro to the reduce operation.

You might also want to look through the posts I've made, I might have skipped a few here which could be important for you. Come back to me once you've done that.

Good luck!
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Old   March 2, 2016, 09:29
Default Find the duplicate points on different processor boundaries
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Sorry for using this thread to ask a related question. Given a point P on a processor boundary (say processor1to2), is it possible to find the information about the duplicated point of this point P on the other processor boundary (which is procssor2to1) ? Is the order of points and faces the same on these two processor boundaries ?

I am working on dynamic mesh moving in parallel and I have to make sure that the displacements of the duplicated points on different processor boundaries are the same in order to avoid the face match problems. Thanks for any help !

Best regards!

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parallel computing

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