From laptops to supercomputers: a single highly scalable code base for spiking neuronal network simulations
BMC Neuroscience ,
Jul 2013
Susanne Kunkel , Maximilian Schmidt , Jochen M Eppler , Hans E Plesser , Jun Igarashi , Gen Masumoto , Tomoki Fukai , Shin Ishii , Abigail Morrison , Markus Diesmann , et al.
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From laptops to supercomputers: a single highly scalable code base for spiking neuronal network simulations
Susanne Kunkel
0
Maximilian Schmidt
Jochen M Eppler
Hans E Plesser
Jun Igarashi
Gen Masumoto
Tomoki Fukai
Shin Ishii
Abigail Morrison
0
Markus Diesmann
0
Moritz Helias
0
Simulation Laboratory Neuroscience - Bernstein Facility Simulation and Database Technology, Institute for Advanced Simulation, Julich Aachen Research Alliance, Julich Research Centre
,
Germany
From Twenty Second Annual Computational Neuroscience Meeting: CNS*2013
Paris, France. 13-18 July 2013
Over the last couple of years, supercomputers such as the
Blue Gene/Q system JUQUEEN in Jlich and the K
computer in Kobe have become available for neuroscience
research. These massively parallel systems open the field
for a new class of scientific questions as they provide the
resources to represent and simulate brain-scale networks,
but they also confront the developers of simulation
software with a new class of problems. Initial tests with our
neuronal network simulator NEST [1] on JUGENE (the
predecessor of JUQUEEN) revealed that in order to
exploit the memory capacities of such machines, we
needed to improve the parallelization of the fundamental
data structures. To address this, we developed an
analytical framework [2], which serves as a guideline for a
systematic and iterative restructuring of the simulation
kernel. In December 2012, the 3rd generation technology
was released with NEST 2.2, which enables simulations
of 108 neurons and 10,000 synapses per neuron on the K
computer [3].
Even though the redesign of the fundamental data
structures of NEST is driven by the demand for simulations of
interacting brain areas, we do not aim at solutions tailored
to a specific brain-scale model or computing architecture.
Our goal is to maintain a single highly scalable code base
that meets the requirements of such simulations whilst
still performing well on modestly dimensioned lab clusters
and even laptops.
Here, we introduce the 4th generation simulation
kernel and describe the development workflow that yielded
the following three major improvements: the
self-collapsing connection infrastructure, which takes up
significantly less memory in the case of few local targets, the
compacted node infrastructure, which causes only
negligible constant serial memory overhead, and the reduced
memory usage of synapse objects, which does not affect
the precision of synaptic state variables. The improved
code does not compromise on the general usability of
NEST and will be merged into the common code base
to be released with NEST 2.4. We show that with the 4g
technology it will be possible to simulate networks of
109 neurons and 10,000 synapses per neuron on the K
computer.
Acknowledgements
Partly supported by the early access to the K computer at the RIKEN
Advanced Institute for Computational Science, by the VSR computation time
grant JINB33 on the JUGENE and JUQUEEN supercomputers in Jlich, the
Alliance on Systems Biology, Initiative and Networking Fund and Portfolio
theme SMHB of the Helmholtz Association, the Jlich-Aachen Research
Alliance (JARA), the Next-Generation Supercomputer Project of MEXT, EU
Grant 269921 (BrainScaleS), Research Council of Norway Grant 178892/V30
(eNeuro) and access to NOTUR supercomputing facilities. All network
simulations carried out with NEST (http://www.nest-initiative.org).
(...truncated)
This is a preview of a remote PDF: http://www.biomedcentral.com/content/pdf/1471-2202-14-S1-P163.pdf
Susanne Kunkel, Maximilian Schmidt, Jochen M Eppler, Hans E Plesser, Jun Igarashi, Gen Masumoto, Tomoki Fukai, Shin Ishii, Abigail Morrison, Markus Diesmann, Moritz Helias.
From laptops to supercomputers: a single highly scalable code base for spiking neuronal network simulations ,
BMC Neuroscience,
2013, pp. P163, 14,