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I hear that OpenMP is based on Shared Memory Parallel Computing, and it
needs random accessing to the data (I am not very sure, but just hear about
that, if I am wrong, please forgive me and tell me the right thing,
thanks). For my code, the contacts are stored in a list, that is not
randomly accessable. If it is ture OpenMP needs random accessing, then how
does YADE do that？Is it ok to run YADE on a distributed memory computer
architecture, like in Cluster--I saw somebody run it on a cluster, but I do
not know the cluster architecture. Because in a distributed memory
architechture, the memory is not directlly accessable, which will increase
the programming difficulty.
Currently my speed is 3~4 second to do a iteration for about 100,000
particles, and 33 seconds to do an iterations for 988,000 particles.
I use threads to do the parallel computing, but the speed up is only 2.2
times faster (15 seconds for 988,000 particles.). This speed is terrible
bad, because the dt is in the order of 10^(-7) second for the simulation of
2009/11/4 Václav Šmilauer <eudoxos@xxxxxxxx>
> > What is the parallel structure used in YADE? I remember in one mail
> > (from email-list), it is said that YADE does not use the domain
> > decomposition method, then what is the parallel method?
> Yade parallelizes loops over bodies and interactions using openMP; see
> Speedup depends very much on the computer architecture, RAM speed, cache
> size etc (openmp is shared-memory parallelization). I have speedup over
> 3x on 4 cores (i7 with ddr3 ram) and recently I had 5.78 on a 2x4core
> Xeon X5570.
> I don't know of anyone running on something larger than 8 cores; it
> might scale further, especially for large simulation, where the openMP
> overhead and the non-parallel portions of computation (collider, for
> instance) don't play large role.
> (If you have 2 engines that don't touch the same data and are
> independent, there is ParallelEngine for that, but I don't know of any
> case where it really pays off; maybe the coupling problems could benefit
> from that)
> Cheers, Vaclav
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