I was successful with building FiPy, the dependancies, and mpi4py, on a Mac and 
was able to run examples with MPI. I could see multiple cores engage and 
noticed the improvement in wall time execution. With that success I decided to 
save some time and purchased the enthought.com single user package and I'm 
trying to get that to run on a cluster (SUSE Enterprise 64 bit) from within my 
account (no root). I haven't gone down the route of including Trilinos yet, 
just the basics for now.

I took examples/parallel.py, took out the Trilinos stuff, and renamed it 
simple.py. It appears that the MPI part is working, but the parallel and Grid1D 
is not distributing across the nodes. When I do an mpirun I get:

me@cluster% mpirun -np 3 python simple.py
mpi4py: processor 0 of 3 :: FiPy: 10 cells on processor 0 of 1
mpi4py: processor 1 of 3 :: FiPy: 10 cells on processor 0 of 1
mpi4py: processor 2 of 3 :: FiPy: 10 cells on processor 0 of 1

The mpirun that is running is from my enthought Python install.

I can see that enthought python is built using gcc, but on the cluster I built 
mpi4py with the Intel compiler. I believe I need to do so to be compatible with 
the clusters MPI environment and job queue (PBS). (?) The above run was on a 
development node; not run through PBS.

Am I trying to mix apples and organges? Should I build all the stuff, Python, 
pysparse, etc. with the intel compiler? Should I rebuild mpi4py with gcc? I 
haven't been able to find any general run-fipy-on-a-cluster advice (but did 
find how to set the fipy_viewer to dummy). The cluster is well managed and I 
can change 'module' sets to use mvapich, mpt, mvapich_gcc, mvapich_intel, etc. 
I have been successful building C/MPI programs and getting them to execute on 
the cluster, so I not a total nubie to cluster computing... just getting FiPy 
to run there.

What general guidance can you provide vs. what I need to coordinate with my 
local system admin?

Thanks,

Bill
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