I'm trying to run AMICA across a large number of nodes in a distributed fashion and I'm finding that for certain combinations of (1) num_models, (2) number of compute nodes and (3) the number of channels of the EEG data that AMICA crashes. I suspect it might be for very large number of compute nodes that there isn't enough data per node to do high density decompositions.
I was wondering if there were general rules about when AMICA would work in a distributed manner for say
T = length of the data
N = number of compute nodes
M = number of models
C = number of channels
Seems like T/N is the amount of data given per node, so it comes to some relationship between M and C for T/N, but it'd be nice if there was some guidance on what values of T/N/M/C would work for distributed AMICA.
I'm trying to run AMICA across a large number of nodes in a distributed fashion and I'm finding that for certain combinations of (1) num_models, (2) number of compute nodes and (3) the number of channels of the EEG data that AMICA crashes. I suspect it might be for very large number of compute nodes that there isn't enough data per node to do high density decompositions.
I was wondering if there were general rules about when AMICA would work in a distributed manner for say
T = length of the data
N = number of compute nodes
M = number of models
C = number of channels
Seems like T/N is the amount of data given per node, so it comes to some relationship between M and C for T/N, but it'd be nice if there was some guidance on what values of T/N/M/C would work for distributed AMICA.