% IMPORTANT: The following is UTF-8 encoded. This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.
@INPROCEEDINGS{Klijn:836542,
author = {Klijn, Wouter and Cumming, Benjamin and Yates, Stuart and
Karakasis, Vasileios and Peyser, Alexander},
title = {{A}rbor: {A} morphologically detailed neural network
simulator for modern high performance computer
architectures},
school = {University of Antwerp},
reportid = {FZJ-2017-05647},
year = {2017},
abstract = {Arbor is a new multicompartment neural network simulator
currently under development as a collaboration between the
Simulation Lab Neuroscience at the Forschungszentrum
Jülich, the Barcelona Supercomputing Center and the Swiss
National Supercomputing Center. Arbor will enable new scales
and classes of morphologically detailed neuronal 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. Arbor is being written specifically with performance
for this hardware in mind (Fig. 1); it aims to be a flexible
platform for neural network simulation while keeping
interoperability with models and workflows developed for
NEST and NEURON.The improvements in performance and
flexibility in themselves will enable a variety of novel
experiments, but the design is not yet finalized, and is
driven by the requirements of the neuroscientific community.
The prototype is open source
(https://github.com/eth-cscs/nestmc-proto ,
http://usertest.cscs.ch/nestmc/) 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.Which directions do you want us to go
in?Simulate large morphological detailed networks for longer
time scales: Study 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 modeled animals.Optimize dynamic
structures for models with time-varying number of neurons,
synapses and compartments: simulate neuronal development,
healing after injury and age related neuronal degeneration.},
month = {Jul},
date = {2017-07-15},
organization = {26th Computational Neuroscience
Meeting, Antwerp (Belgium), 15 Jul 2017
- 20 Jul 2017},
subtyp = {Other},
cin = {JSC},
cid = {I:(DE-Juel1)JSC-20090406},
pnm = {511 - Computational Science and Mathematical Methods
(POF3-511) / 574 - Theory, modelling and simulation
(POF3-574) / 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-HGF)POF3-574 /
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/836542},
}