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000172421 0247_ $$2doi$$a10.3389/fninf.2014.00078
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000172421 1001_ $$0P:(DE-Juel1)151364$$aKunkel, Susanne$$b0$$eCorresponding Author$$ufzj
000172421 245__ $$aSpiking network simulation code for petascale computers
000172421 260__ $$aLausanne$$bFrontiers Research Foundation$$c2014
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000172421 520__ $$aBrain-scale networks exhibit a breathtaking heterogeneity in the dynamical properties and parameters of their constituents. At cellular resolution, the entities of theory are neurons and synapses and over the past decade researchers have learned to manage the heterogeneity of neurons and synapses with efficient data structures. Already early parallel simulation codes stored synapses in a distributed fashion such that a synapse solely consumes memory on the compute node harboring the target neuron. As petaflop computers with some 100,000 nodes become increasingly available for neuroscience, new challenges arise for neuronal network simulation software: Each neuron contacts on the order of 10,000 other neurons and thus has targets only on a fraction of all compute nodes; furthermore, for any given source neuron, at most a single synapse is typically created on any compute node. From the viewpoint of an individual compute node, the heterogeneity in the synaptic target lists thus collapses along two dimensions: the dimension of the types of synapses and the dimension of the number of synapses of a given type. Here we present a data structure taking advantage of this double collapse using metaprogramming techniques. After introducing the relevant scaling scenario for brain-scale simulations, we quantitatively discuss the performance on two supercomputer. We show that the novel architecture scales to the largest petascale supercomputers available today.
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000172421 536__ $$0G:(DE-Juel1)jinb33_20121101$$aBrain-Scale Simulations (jinb33_20121101)$$cjinb33_20121101$$fBrain-Scale Simulations$$x2
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000172421 536__ $$0G:(EU-Grant)269921$$aBRAINSCALES - Brain-inspired multiscale computation in neuromorphic hybrid systems (269921)$$c269921$$fFP7-ICT-2009-6$$x6
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000172421 7001_ $$0P:(DE-Juel1)145897$$aSchmidt, Maximilian$$b1$$ufzj
000172421 7001_ $$0P:(DE-Juel1)142538$$aEppler, Jochen M.$$b2$$ufzj
000172421 7001_ $$0P:(DE-HGF)0$$aPlesser, Hans E.$$b3
000172421 7001_ $$0P:(DE-HGF)0$$aMasumoto, Gen$$b4
000172421 7001_ $$0P:(DE-HGF)0$$aIgarashi, Jun$$b5
000172421 7001_ $$0P:(DE-HGF)0$$aIshii, Shin$$b6
000172421 7001_ $$0P:(DE-HGF)0$$aFukai, Tomoki$$b7
000172421 7001_ $$0P:(DE-Juel1)151166$$aMorrison, Abigail$$b8$$ufzj
000172421 7001_ $$0P:(DE-Juel1)144174$$aDiesmann, Markus$$b9$$ufzj
000172421 7001_ $$0P:(DE-Juel1)144806$$aHelias, Moritz$$b10$$ufzj
000172421 773__ $$0PERI:(DE-600)2452979-5$$a10.3389/fninf.2014.00078$$gVol. 8$$p78$$tFrontiers in neuroinformatics$$v8$$x1662-5196$$y2014
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