I just have some minor additions to what Hans said.
You mention that you collect senders, times, sources and targets. What do you mean by senders vs sources? And wouldn’t it suffice to just record the senders and times (using the NEST spike_detector)? If you then have a one-time-dump of connection information, you could infer targets for each spike during analysis.
spike_detector is called
spike_recorder in current master.
For highly parallel simulations, the current master branch of NEST (NEST3) allows you to use SIONLib for efficient parallel recording.
On 02/11/2020, 18:35, "swathi anil" <email@example.com> wrote:
I use NEST with OpenMPI on a high performance cluster to run plasticity related network simulations.
So far I have divided my whole job into:
phase 1: data collection (data collected: senders, times, sources, targets for each time point)
phase 2: data anaylsis (refers to spike count and connectivity calculation)
I face an issue with data-handling. In phase 1, the data pertaining to each of the four variables is saved in X different files (X = number of virtual processes), rank-wise. This means that the total number of files generated goes [ n(time-points)*X*4 ], which surpasses the chunk file storage limit, per user for the cluster. Each file here is an ndarray saved as a *.npy file.
I wonder if there is a way to retrieve the data from each of the X processes while collecting data, concatenating and then saving them? So instead of X number of files, I can save the concatenated version. This probably involves recruiting a single VP to collect and concatenate and save the datapoints, but i am not quite sure how to execute this using NEST. Any help would be highly appreciated!
Thanks in advance!
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