Hi Robin,
Just to follow up on the discussion from the NEST Open VC last Monday.
Nicolai and Håkon (with early contributions by Stine) have a branch almost ready. We are
currently waiting for JUSUF to come back up so we can run tests against the full Allen
Institute mouse brain model (ca 480 GB of HDF5) with an improved prototype. The code needs
some tidying up, but we could start discussing once we know that it works in principle.
To adapt to your plans, it would be very helpful to know what your data looks like. E.g.,
do you use NumPy arrays with certain columns to store all connections, HDF5, your own
magic format?
Best,
Hans Ekkehard
--
Prof. Dr. Hans Ekkehard Plesser
Head, Department of Data Science
Faculty of Science and Technology
Norwegian University of Life Sciences
PO Box 5003, 1432 Aas, Norway
Phone +47 6723 1560
Email hans.ekkehard.plesser@nmbu.no<mailto:hans.ekkehard.plesser@nmbu.no>
Home
http://arken.nmbu.no/~plesser
From: Robin Gilbert De Schepper <robingilbert.deschepper(a)unipv.it>
Reply to: NEST User Mailing List <users(a)nest-simulator.org>
Date: Friday, 7 October 2022 at 16:33
To: NEST User Mailing List <users(a)nest-simulator.org>
Subject: [NEST Users] Re: Fastest way to transfer dense connectivity data in a parallel
simulation?
What's the timeline for this? Is there any open source discussion or proposal for the
implementation that I could read to understand how the problem is being tackled, and maybe
find out if I can propose a low-effort interface decoupled from SONATA on disk? If
there's something reading data from disk, I might as well be streaming that thing my
data.
On Fri, 7 Oct 2022, 16:09 Hans Ekkehard Plesser,
<hans.ekkehard.plesser@nmbu.no<mailto:hans.ekkehard.plesser@nmbu.no>> wrote:
Hi Robin,
We are currently working with the Allen Institute to develop an efficient reader for large
Sonata network specifications. We assume here that all connectivity is collected in HDF5
files, and expect significant performance gains if data is sorted by target neuron and the
Sonata files provide "indices" tables. Would this help you?
Dividing network data according to a specific compute node configuration seems rather
restrictive to me and right now there is no way to read such data per process.
Best,
Hans Ekkehard
--
Prof. Dr. Hans Ekkehard Plesser
Head, Department of Data Science
Faculty of Science and Technology
Norwegian University of Life Sciences
PO Box 5003, 1432 Aas, Norway
Phone +47 6723 1560
Email hans.ekkehard.plesser@nmbu.no<mailto:hans.ekkehard.plesser@nmbu.no>
Home
http://arken.nmbu.no/~plesser
From: Robin Gilbert De Schepper
<robingilbert.deschepper@unipv.it<mailto:robingilbert.deschepper@unipv.it>>
Reply to: NEST User Mailing List
<users@nest-simulator.org<mailto:users@nest-simulator.org>>
Date: Friday, 7 October 2022 at 15:41
To: NEST User Mailing List
<users@nest-simulator.org<mailto:users@nest-simulator.org>>
Subject: [NEST Users] Fastest way to transfer dense connectivity data in a parallel
simulation?
Hi all!
In the world of biophysical detail, it's commonplace that the connectome is generated
with algorithms that specify connections as dense tabular data, with each row specifying a
synaptic location on a cell pair (SONATA for example).
A) In NEST I can't really find the opportunity to fit this data into any of the
connection rules: I want to specify pairwise connections from the multiset A to multiset
B.
- Is this possible with `pairwise bernoulli`, or do the inputs have to be strict sets?
- The probability step is superfluous, can it be skipped?
B) Then there's the fact that NEST parallelizes transparently, but since this data was
generated in parallel by tiling the biological volume, I have neatly fragmented data
already available on each node in the distributed cluster. It would be such a waste to
communicate all the data to each node, for NEST to communicate and distribute them back
another way.
The data is too big to allgather and fit into memory of any single node. Not only is this
a lot of overhead to implement, but NEST will throw away all but `1 / Nnodes` of the data
on each node again, leaving me with a reshuffled version of my starting data.
Is there a way to bypass the transparency and to imperatively declare the cells and
connections on each machine?
--
Robin De Schepper, MSc (they/them)
Department of Brain and Behavioral Sciences
Unit of Neurophysiology
University of Pavia, Italy
Via Forlanini 6, 27100 Pavia - Italy
Tel: (+39) 038298-7607
http://www-5.unipv.it/dangelo/
Interested in large scale network modelling?
Discover our framework<https://bsb.readthedocs.io/en/latest/>:
Error! Filename not
specified.<https://github.com/dbbs-lab/bsb>
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