Spiking neuronal network simulation technology for contemporary supercomputers

Abstract

Event Abstract Back to Event Spiking neuronal network simulation technology for contemporary supercomputers Moritz Helias1*, Susanne Kunkel2, Jochen Eppler3, Gen Masumoto4, Jun Igarashi5, Shin Ishii6, Tomoki Fukai5, Abigail Morrison2 and Markus Diesmann1 1 Inst of Neuroscience and Medicine (INM-6) Computational and Systems Neuroscience, Research Center Juelich, Germany 2 Functional Neural Circuits Group Albert-Ludwig University of Freiburg, Germany 3 Inst of Neuroscience and Medicine (INM-6), Computational and Systems Neuroscience, Research Center Juelich, Germany 4 High-Performance Computing Team, RIKEN, Computational Science Research Program Kobe, Japan 5 Laboratory for Neural Circuit Theory, RIKEN Brain Science Institute, Wako, Japan 6 Integrated Systems Biology, Laboratory Department of Systems Science, Graduate School of Informatics, Kyoto University, Japan Functional neuronal networks, like the visual cortex of primates, comprise on the order of 100 million neurons, consisting of areas that exceed 10 million neurons and 100 billion synapses. The memory demands of such simulations are only met by distributed simulation software and supercomputers, like the Jugene BG/P supercomputer in Juelich and the K computer in Kobe. Though connectivity between brain areas is sparse, there are fewer constraints within areas. A general simulation tool needs to be able to simulate networks of 10 million neurons with arbitrary connectivity, often assumed to be random. This presents the worst case scenario: Firstly, there is no redundancy that allows to compress the representation of synaptic connectivity. Secondly, communication between the compute nodes is potentially all-to-all. Here we quantitatively demonstrate the recent advances of neural simulation technology [2] on the example of the simulator NEST [1], which have lead to a readily usable tool for the neuroscientist. As the memory rather than run time limits the maximal size of a neuronal network, we explain the systematic improvements of the distributed data structures adapted to the sparse and random connectivity. High performance and good scaling of network setup and simulation are achieved with a hybrid code combining OpenMP threads and MPI, exploiting the multi-core architectures of K and Jugene. We parameterize and employ a model of memory consumption to estimate the machine size needed for a given neuroscientific question; a crucial tool not only to plan simulations, but also for computation time grant applications. Simulations of networks exceeding 10 million neurons on K and Jugene are shown to determine the limits of the current technology and computer architectures. Partially supported by the Helmholtz Alliance on Systems Biology, the Next-Generation Supercomputer Project of MEXT, EU Grant 269921 (BrainScaleS), the VSR computation time grant JINB33 on the JUGENE supercomputer, and by early access to the K computer at the RIKEN Advanced Institute for Computational Science. [1] Gewaltig M-O and Diesmann M (2007) NEST. Scholarpedia, 2(4):1430. [2] Kunkel S, Potjans TC, Eppler JM, Plesser HE, Morrison A and Diesmann M (2012) Front. Neuroinform. 5:35. doi: 10.3389/fninf.2011.00035 Keywords: Large scale modeling Conference: 5th INCF Congress of Neuroinformatics, Munich, Germany, 10 Sep - 12 Sep, 2012. Presentation Type: Poster Topic: Neuroinformatics Citation: Helias M, Kunkel S, Eppler J, Masumoto G, Igarashi J, Ishii S, Fukai T, Morrison A and Diesmann M (2013). Spiking neuronal network simulation technology for contemporary supercomputers. Front. Neuroinform. Conference Abstract: 5th INCF Congress of Neuroinformatics. doi: 10.3389/conf.fninf.2013.08.00012 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 21 Mar 2013; Published Online: 27 Nov 2013. * Correspondence: Dr. Moritz Helias, Inst of Neuroscience and Medicine (INM-6) Computational and Systems Neuroscience, Research Center Juelich, Jülich, Germany, m.helias@fz-juelich.de Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Moritz Helias Susanne Kunkel Jochen Eppler Gen Masumoto Jun Igarashi Shin Ishii Tomoki Fukai Abigail Morrison Markus Diesmann Google Moritz Helias Susanne Kunkel Jochen Eppler Gen Masumoto Jun Igarashi Shin Ishii Tomoki Fukai Abigail Morrison Markus Diesmann Google Scholar Moritz Helias Susanne Kunkel Jochen Eppler Gen Masumoto Jun Igarashi Shin Ishii Tomoki Fukai Abigail Morrison Markus Diesmann PubMed Moritz Helias Susanne Kunkel Jochen Eppler Gen Masumoto Jun Igarashi Shin Ishii Tomoki Fukai Abigail Morrison Markus Diesmann Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.