[CrashLaker]

Originally Posted by nilesh

Is there a way to mark this post so that it could be easier for other users facing a similar problem?

haha I'm glad that you've managed to solve it!

I also received a lot of help when I first started SU2. There's a lot of experts here so don't forget to ask/and answer anytime hehe

About your question I don't know what could me done since I'm a newbie just like you haha. But it couldn't be helped considering that you didn't know the exact problem.

GL!
Carlos.

[aero_amit]

Hello Guys,

Actually you can run it on a cluster by using ---

mpirun -np 30 --hostfile hostfile SU2_CFD filename.cfg

(without using python script)

Here number of codes are 30 (can be any number).

In hostfile (In local directory), you should write nodes for example --

node201
node202
node203
.......... so on

This works for me...

[ernestyalumni]

Originally Posted by copeland

GPU computing has the potential to be very powerful. The development team has discussed this, but, at the current time, we don't have anyone working on it. If members of the community are interested, we encourage folks to take on the challenge!

-Sean

Sean Copeland, I was curious, as this post was almost 3 years ago, and searching here and on Google for "SU2 GPU parallel computing", I only came across this post. What's the status for SU2 on GPU parallel computing and/or any plans? Let us know; I myself am reading up on parallel computing with combustion CFD on parallel threads on GPU in mind.

[Samirs]

Originally Posted by aero_amit

Hello Guys,

Actually you can run it on a cluster by using ---

mpirun -np 30 --hostfile hostfile SU2_CFD filename.cfg

(without using python script)

Here number of codes are 30 (can be any number).

In hostfile (In local directory), you should write nodes for example --

node201
node202
node203
.......... so on

This works for me...

Are you sure that each process is not doing redundant work?

I tried it like first exporting

export SU2_MPI_COMMAND="mpirun -np %i %s"

and then running parallel_computation.py -f turb_ONERA_M6.cfg -n 10

It works well but my only concern is i'm not getting good scalability.

Please correct me if it's wrong

[aero_amit]

Scalability..... I have not benchmarked any thing but it seems to scale reasonably I feel. Can u share the details i. e. number of cells, cores used, memory etc..

[Samirs]

Originally Posted by aero_amit

Scalability..... I have not benchmarked any thing but it seems to scale reasonably I feel. Can u share the details i. e. number of cells, cores used, memory etc..

Thanks Amit

I'm running testcase from https://github.com/su2code/su2code.g...bulent_ONERAM6

like "parallel_computation.py -f turb_ONERAM6.cfg -n 4"

I'm running it on Intel broadwell nodes with each of 128GB RAM.

Thanks again for your help

[aero_amit]

Originally Posted by Samirs

Thanks Amit

I'm running testcase from https://github.com/su2code/su2code.g...bulent_ONERAM6

like "parallel_computation.py -f turb_ONERAM6.cfg -n 4"

I'm running it on Intel broadwell nodes with each of 128GB RAM.

Thanks again for your help

Hi samirs,

Sorry for the late reply. Actually if u have a smaller mesh, it will not scale beyond few cores. I could not give a try to above problem personally but I have given experimental try to couple of cases having mesh size in few 10s of million and they scaled well upto 512 cores or so.

[Samirs]

Originally Posted by aero_amit

Hi samirs,

Sorry for the late reply. Actually if u have a smaller mesh, it will not scale beyond few cores. I could not give a try to above problem personally but I have given experimental try to couple of cases having mesh size in few 10s of million and they scaled well upto 512 cores or so.

Thanks Amit,

As you mentioned about mesh size, do you know any such tool to generate mesh of larger size which we can use in SU2?

[hlk]

There are a number of different mesh generation options.

Pointwise is a commercial tool that outputs directly into the SU2 format, and there are several options (including some free options) that output to CGNS format, which SU2 can read while using the mesh_format config options.

Originally Posted by Samirs

Thanks Amit,

As you mentioned about mesh size, do you know any such tool to generate mesh of larger size which we can use in SU2?

[kont87]

Hello, it is an old topic but I have a similar issue.

When I execute the run with mpiexec command, it works flawlessly. However, when I switch to parallel_computation.py and lets say run the solver on 4 cores, it seems each core does the same computation (4 solutions).

I have win 8.1 OS, MS-MPI, python 3.8 (and all packages) are set, the paths are linked.

To sum up, I can simulate by using:
mpiexec -n 4 SU2_CFD default.cfg (gives 1 solution, ~4x speed up)

but not by this one:
python parallel_computation.py -f default.cfg -n 4 (gives 4 identical solutions, no speed up)

Any help is appreciated.