000864815 001__ 864815
000864815 005__ 20240313094933.0
000864815 037__ $$aFZJ-2019-04471
000864815 1001_ $$0P:(DE-Juel1)165321$$aPronold, Jari$$b0$$eCorresponding author
000864815 1112_ $$aRCCS Institutional seminar$$cRCCS Kobe$$wJapan
000864815 245__ $$aMeeting the performance challenges of spiking network simulations on general purpose computers
000864815 260__ $$c2019
000864815 3367_ $$033$$2EndNote$$aConference Paper
000864815 3367_ $$2DataCite$$aOther
000864815 3367_ $$2BibTeX$$aINPROCEEDINGS
000864815 3367_ $$2ORCID$$aLECTURE_SPEECH
000864815 3367_ $$0PUB:(DE-HGF)31$$2PUB:(DE-HGF)$$aTalk (non-conference)$$btalk$$mtalk$$s1567761480_29885$$xInvited
000864815 3367_ $$2DINI$$aOther
000864815 520__ $$aToday’s extremely scalable simulation technology for spiking neuronal networks enables the representation of models of more than a billion of neurons and their connections using the entire K computer. However, the runtimes of the largest possible simulations carried out so far were too long to allow for observations of the network dynamics over long periods of time, and also small to medium-scale simulations typically run in far more than real-time. The performance challenges for spiking neuronal network simulators such as NEST on general purpose computers arise from the inherent sparse but broad connectivity between neurons and from the unpredictable neuronal spiking activity. In distributed simulations of spiking networks, this requires frequent communication of spike data, and on each compute node routing of the incoming spikes to the local targets. This entails irregular memory access and hence constitutes a major performance bottleneck, which is a problem that I will address in my talk. I will present recent developments in simulation technology that aim at meeting such performance challenges.
000864815 536__ $$0G:(DE-HGF)POF3-574$$a574 - Theory, modelling and simulation (POF3-574)$$cPOF3-574$$fPOF III$$x0
000864815 536__ $$0G:(DE-Juel1)aca_20190115$$aAdvanced Computing Architectures (aca_20190115)$$caca_20190115$$fAdvanced Computing Architectures$$x1
000864815 536__ $$0G:(DE-Juel1)jinb33_20121101$$aBrain-Scale Simulations (jinb33_20121101)$$cjinb33_20121101$$fBrain-Scale Simulations$$x2
000864815 536__ $$0G:(DE-Juel1)PHD-NO-GRANT-20170405$$aPhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405)$$cPHD-NO-GRANT-20170405$$x3
000864815 7001_ $$0P:(DE-Juel1)151364$$aKunkel, Susanne$$b1
000864815 909CO $$ooai:juser.fz-juelich.de:864815$$pVDB
000864815 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)165321$$aForschungszentrum Jülich$$b0$$kFZJ
000864815 9131_ $$0G:(DE-HGF)POF3-574$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$vTheory, modelling and simulation$$x0
000864815 9141_ $$y2019
000864815 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
000864815 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x1
000864815 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x2
000864815 980__ $$atalk
000864815 980__ $$aVDB
000864815 980__ $$aI:(DE-Juel1)INM-6-20090406
000864815 980__ $$aI:(DE-Juel1)IAS-6-20130828
000864815 980__ $$aI:(DE-Juel1)INM-10-20170113
000864815 980__ $$aUNRESTRICTED
000864815 981__ $$aI:(DE-Juel1)IAS-6-20130828