Hi there, An effective N-threads implementation of distributed memory parallelization is achieved after minor changes in the source code and some refactoring of the previous 2-threads script. You can find the concrete example testMPIxN.py with comments in [1].
The 10x slow down I mentioned previously was due mainly to triggering contact detection based on false positive. The monolithic run was executing collider only once (correct), the parallel version was triggering detection all the time (superfluous). At the moment the mpi version is on par with monolithic version for 90k bodies (and 3 subdomains for the parallel version). This is more than encouraging, but note the geometry of the example simulation is very pleasently parallel. I cease development at this point and leave it to future works. I am more than open to discussions though, since there is very little documentation and questions/answers might help to explain what happens therein. Cheers Bruno p.s. this is the very-very last email to yade-dev, thank you to those registered to yade-mpi :) [1] https://github.com/bchareyre/yade-mpi/commit/fa48e931cf44c12ed78f288f931f0981955635d7
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