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Created page with "== OpenMPI / mpi4py == [http://www.nmr-relax.com/manual/Usage_multi_processor.html Manual on Multi processor usage] If you have OpenMPI and mpi4py installed, then you have ac..."
== OpenMPI / mpi4py ==
[http://www.nmr-relax.com/manual/Usage_multi_processor.html Manual on Multi processor usage]
If you have OpenMPI and mpi4py installed, then you have access to Gary
Thompson's multi-processor framework for MPI parallelisation.
However the code in relax must be written to support this. This is the case
for the model-free analysis, in which case Gary has achieved near
perfect scaling efficiency:
https://mail.gna.org/public/relax-devel/2007-05/msg00000.html
For the relaxation dispersion branch, no parallelisation has
been attempted, neither in the original code from Sebastian Morin or
the recent modifications by myself. This is not a simple task and
will take a lot of effort to implement. If this is to be implemented
one day, it is suggested parallelising at the level of the spin
clusters.
It is often quite hard to achieve good scaling
efficiency and often the first attempts will just make the code
slower, even on a 1024 node cluster, due to the bottleneck of data
transfer between the nodes.
The parallelisation will also require 10
times as much code to be written to do the same thing as
non-parallised code, and debugging is much more difficult.
[http://www.nmr-relax.com/manual/Usage_multi_processor.html Manual on Multi processor usage]
If you have OpenMPI and mpi4py installed, then you have access to Gary
Thompson's multi-processor framework for MPI parallelisation.
However the code in relax must be written to support this. This is the case
for the model-free analysis, in which case Gary has achieved near
perfect scaling efficiency:
https://mail.gna.org/public/relax-devel/2007-05/msg00000.html
For the relaxation dispersion branch, no parallelisation has
been attempted, neither in the original code from Sebastian Morin or
the recent modifications by myself. This is not a simple task and
will take a lot of effort to implement. If this is to be implemented
one day, it is suggested parallelising at the level of the spin
clusters.
It is often quite hard to achieve good scaling
efficiency and often the first attempts will just make the code
slower, even on a 1024 node cluster, due to the bottleneck of data
transfer between the nodes.
The parallelisation will also require 10
times as much code to be written to do the same thing as
non-parallised code, and debugging is much more difficult.
