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@INPROCEEDINGS{Klijn:841438,
author = {Klijn, Wouter and Cumming, B. and Yates, S. and Karakasis,
V. and Peyser, Alexander},
title = {{N}est{MC}: {A} morphologically detailed neural network
simulator for modern high performance computer
architectures},
reportid = {FZJ-2017-08484},
year = {2017},
abstract = {NestMC is a new multicompartment neural network simulator
currently under development as a collaboration between the
Neuroscience SimLab at the Forschungszentrum Jülich,
Barcelona Supercomputing Center and the Swiss National
Supercomputing Center under the aegis of the NEST
Initiative. NestMC will enable new scales and classes of
morphologically detailed network simulations on current and
future supercomputing architectures.A number of "many-core"
architectures such as GPU and Intel Xeon Phi based systems
are currently available, to optimally use these emerging
architecture new approaches in software development and
algorithm design are needed. NestMC is being written
specifically with this in mind; it aims to be a flexible
platform for neural network simulation, while keeping
interoperability with models and workflows of NEST and
NEURON.The improvements in performance and flexibility in
themselves will enable a variety of novel experiments, but
the design is not finalised, and is driven by the
requirements of the neuroscientific community. The prototype
is open source (1) and we invite you to have a look. We are
interested in your ideas for features which will make new
science possible: we ask you to think outside of the box and
build this next generation neurosimulator together with
us.What directions do you want us to go in?• Simulate
large morphological detailed networks for longer time
scales: Study of slow developing phenomena.• Reduce the
time to solution: Perform more repeat experiments for
increased statistical power.• Create high performance
interfaces with other software: Perform online statistical
analysis and visualization of your running models, study the
brain at multiple scales with specialized tools, or embed
detailed networks in physically modelled animals.•
Optimize dynamic data structures for models with
time-varying number of neurons, synapses and compartments:
simulate neuronal development, healing after injury and age
related neuronal degeneration.Do you have other great ideas?
Let us know!},
month = {Feb},
date = {2017-02-08},
organization = {HBP student conference 2017, Vienna
(Austria), 8 Feb 2017 - 10 Feb 2017},
subtyp = {Other},
cin = {JSC},
cid = {I:(DE-Juel1)JSC-20090406},
pnm = {511 - Computational Science and Mathematical Methods
(POF3-511) / SMHB - Supercomputing and Modelling for the
Human Brain (HGF-SMHB-2013-2017) / HBP SGA1 - Human Brain
Project Specific Grant Agreement 1 (720270) / SLNS - SimLab
Neuroscience (Helmholtz-SLNS)},
pid = {G:(DE-HGF)POF3-511 / G:(DE-Juel1)HGF-SMHB-2013-2017 /
G:(EU-Grant)720270 / G:(DE-Juel1)Helmholtz-SLNS},
typ = {PUB:(DE-HGF)24},
url = {https://juser.fz-juelich.de/record/841438},
}