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 |
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| dc.type.full | 01 Contributo su Rivista::01.01 Articolo in rivista | it |
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