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<identifier>oai:HAL:hal-00015137v2</identifier>
<datestamp>2018-01-11</datestamp>
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<publisher>HAL CCSD</publisher>
<title lang=en>DAMNED: A Distributed and Multithreaded Neural Event-Driven simulation framework</title>
<creator>Mouraud, Anthony</creator>
<creator>Puzenat, Didier</creator>
<creator>Paugam-Moisy, Hélène</creator>
<contributor>Groupe de Recherche en Informatique et Mathématiques Appliquées Antilles-Guyane (GRIMAAG) ; Université des Antilles et de la Guyane (UAG)</contributor>
<contributor>Institut des Sciences Cognitives (ISC) ; Université Claude Bernard Lyon 1 (UCBL) - Centre National de la Recherche Scientifique (CNRS)</contributor>
<description>6 pages</description>
<identifier>hal-00015137</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-00015137</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-00015137v2/document</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-00015137/file/hal_damned.pdf</identifier>
<source>https://hal.archives-ouvertes.fr/hal-00015137</source>
<source>6 pages. 2006</source>
<identifier>ARXIV : cs.NE/0512018</identifier>
<relation>info:eu-repo/semantics/altIdentifier/arxiv/cs.NE/0512018</relation>
<language>en</language>
<subject lang=en>Spiking Neural Networks</subject>
<subject lang=en>Event-Driven Simulations</subject>
<subject lang=en>Parallel Computing</subject>
<subject lang=en>Multi-threading</subject>
<subject lang=en>Scheduling</subject>
<subject>[INFO.INFO-NE] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]</subject>
<subject>[INFO.INFO-DC] Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC]</subject>
<subject>[INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG]</subject>
<subject>[SCCO.COMP] Cognitive science/Computer science</subject>
<type>info:eu-repo/semantics/preprint</type>
<type>Preprints, Working Papers, ...</type>
<description lang=en>In a Spiking Neural Networks (SNN), spike emissions are sparsely and irregularly distributed both in time and in the network architecture. Since a current feature of SNNs is a low average activity, efficient implementations of SNNs are usually based on an Event-Driven Simulation (EDS). On the other hand, simulations of large scale neural networks can take advantage of distributing the neurons on a set of processors (either workstation cluster or parallel computer). This article presents DAMNED, a large scale SNN simulation framework able to gather the benefits of EDS and parallel computing. Two levels of parallelism are combined: Distributed mapping of the neural topology, at the network level, and local multithreaded allocation of resources for simultaneous processing of events, at the neuron level. Based on the causality of events, a distributed solution is proposed for solving the complex problem of scheduling without synchronization barrier.</description>
<date>2006-03-21</date>
</dc>
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