The NEURON simulation environment has been extended to support parallel network simulations. Each processor integrates the equations for its subnet over an interval equal to the minimum (interprocessor) presynaptic spike generation to postsynaptic spike delivery connection delay. The performance of three published network models with very different spike patterns exhibits superlinear speedup on Beowulf clusters and demonstrates that spike communication overhead is often less than the benefit of an increased fraction of the entire problem fitting into high speed cache. On the EPFL IBM Blue Gene, almost linear speedup was obtained up to 100 processors. Increasing one model from 500 to 40,000 realistic cells exhibited almost linear speedup on 2000 processors, with an integration time of 9.8 seconds and communication time of 1.3 seconds. The potential for speed-ups of several orders of magnitude makes practical the running of large network simulations that could otherwise not be explored.

Parallel Network simulations with NEURON.

Migliore M;
2006

Abstract

The NEURON simulation environment has been extended to support parallel network simulations. Each processor integrates the equations for its subnet over an interval equal to the minimum (interprocessor) presynaptic spike generation to postsynaptic spike delivery connection delay. The performance of three published network models with very different spike patterns exhibits superlinear speedup on Beowulf clusters and demonstrates that spike communication overhead is often less than the benefit of an increased fraction of the entire problem fitting into high speed cache. On the EPFL IBM Blue Gene, almost linear speedup was obtained up to 100 processors. Increasing one model from 500 to 40,000 realistic cells exhibited almost linear speedup on 2000 processors, with an integration time of 9.8 seconds and communication time of 1.3 seconds. The potential for speed-ups of several orders of magnitude makes practical the running of large network simulations that could otherwise not be explored.
Campo DC Valore Lingua
dc.authority.ancejournal JOURNAL OF COMPUTATIONAL NEUROSCIENCE -
dc.authority.orgunit Istituto di Biofisica - IBF -
dc.authority.people Migliore M it
dc.authority.people Cannia C it
dc.authority.people Lytton WW it
dc.authority.people Markram H it
dc.authority.people Hines M L it
dc.collection.id.s b3f88f24-048a-4e43-8ab1-6697b90e068e *
dc.collection.name 01.01 Articolo in rivista *
dc.contributor.appartenenza Istituto di Biofisica - IBF *
dc.contributor.appartenenza.mi 846 *
dc.date.accessioned 2024/02/19 15:04:14 -
dc.date.available 2024/02/19 15:04:14 -
dc.date.issued 2006 -
dc.description.abstracteng The NEURON simulation environment has been extended to support parallel network simulations. Each processor integrates the equations for its subnet over an interval equal to the minimum (interprocessor) presynaptic spike generation to postsynaptic spike delivery connection delay. The performance of three published network models with very different spike patterns exhibits superlinear speedup on Beowulf clusters and demonstrates that spike communication overhead is often less than the benefit of an increased fraction of the entire problem fitting into high speed cache. On the EPFL IBM Blue Gene, almost linear speedup was obtained up to 100 processors. Increasing one model from 500 to 40,000 realistic cells exhibited almost linear speedup on 2000 processors, with an integration time of 9.8 seconds and communication time of 1.3 seconds. The potential for speed-ups of several orders of magnitude makes practical the running of large network simulations that could otherwise not be explored. -
dc.description.affiliations 1. CNR, Inst Biophys, I-90146 Palermo, Italy 2. Yale Univ, Sch Med, Dept Neurobiol, New Haven, CT USA 3. Univ Palermo, Dipartimento Matemat & Applicaz, Palermo, Italy 4. Suny Downstate Med Ctr, Dept Physiol Pharmacol & Neurol, Brooklyn, NY 11203 USA 5. Ecole Polytech Fed Lausanne, Brain Mind Inst, Lab Neural Microcircuitry, CH-1015 Lausanne, Switzerland 6. Yale Univ, Dept Comp Sci, New Haven, CT 06520 USA -
dc.description.allpeople Migliore, M; Cannia, C; Lytton, Ww; Markram, H; Hines, M L -
dc.description.allpeopleoriginal Migliore M.; Cannia C.; Lytton W.W; Markram H.; Hines M. L. -
dc.description.fulltext none en
dc.description.numberofauthors 5 -
dc.identifier.doi 10.1007/s10827-006-7949-5 -
dc.identifier.isi WOS:000239869500001 -
dc.identifier.uri https://hdl.handle.net/20.500.14243/166408 -
dc.language.iso eng -
dc.relation.firstpage 119 -
dc.relation.issue 2 -
dc.relation.lastpage 129 -
dc.relation.volume 21 -
dc.subject.keywords EVENT-DRIVEN SIMULATION -
dc.subject.keywords SPIKING NEURONS -
dc.subject.keywords MODEL -
dc.subject.keywords CONDUCTANCE -
dc.subject.singlekeyword EVENT-DRIVEN SIMULATION *
dc.subject.singlekeyword SPIKING NEURONS *
dc.subject.singlekeyword MODEL *
dc.subject.singlekeyword CONDUCTANCE *
dc.title Parallel Network simulations with NEURON. en
dc.type.driver info:eu-repo/semantics/article -
dc.type.full 01 Contributo su Rivista::01.01 Articolo in rivista it
dc.type.miur 262 -
dc.type.referee Sì, ma tipo non specificato -
dc.ugov.descaux1 9527 -
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