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000906624 0247_ $$2doi$$a10.48550/ARXIV.2109.12855
000906624 0247_ $$2Handle$$a2128/30873
000906624 037__ $$aFZJ-2022-01560
000906624 1001_ $$0P:(DE-Juel1)165321$$aPronold, Jari$$b0$$eCorresponding author
000906624 245__ $$aRouting brain traffic through the von Neumann bottleneck: Efficient cache usage in spiking neural network simulation code on general purpose computers
000906624 260__ $$barXiv$$c2021
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000906624 520__ $$aSimulation is a third pillar next to experiment and theory in the study of complex dynamic systems such as biological neural networks. Contemporary brain-scale networks correspond to directed graphs of a few million nodes, each with an in-degree and out-degree of several thousands of edges, where nodes and edges correspond to the fundamental biological units, neurons and synapses, respectively. When considering a random graph, each node's edges are distributed across thousands of parallel processes. The activity in neuronal networks is also sparse. Each neuron occasionally transmits a brief signal, called spike, via its outgoing synapses to the corresponding target neurons. This spatial and temporal sparsity represents an inherent bottleneck for simulations on conventional computers: Fundamentally irregular memory-access patterns cause poor cache utilization. Using an established neuronal network simulation code as a reference implementation, we investigate how common techniques to recover cache performance such as software-induced prefetching and software pipelining can benefit a real-world application. The algorithmic changes reduce simulation time by up to 50%. The study exemplifies that many-core systems assigned with an intrinsically parallel computational problem can overcome the von Neumann bottleneck of conventional computer architectures.
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000906624 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x2
000906624 536__ $$0G:(EU-Grant)754304$$aDEEP-EST - DEEP - Extreme Scale Technologies (754304)$$c754304$$fH2020-FETHPC-2016$$x3
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000906624 536__ $$0G:(GEPRIS)368482240$$aGRK 2416:  MultiSenses-MultiScales: Novel approaches to decipher neural processing in multisensory integration (368482240)$$c368482240$$x5
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000906624 650_7 $$2Other$$aDistributed, Parallel, and Cluster Computing (cs.DC)
000906624 650_7 $$2Other$$aFOS: Computer and information sciences
000906624 7001_ $$0P:(DE-Juel1)151356$$aJordan, Jakob$$b1
000906624 7001_ $$0P:(DE-Juel1)132302$$aWylie, Brian J. N.$$b2$$ufzj
000906624 7001_ $$0P:(DE-Juel1)187457$$aKitayama, Itaru$$b3$$ufzj
000906624 7001_ $$0P:(DE-Juel1)144174$$aDiesmann, Markus$$b4$$ufzj
000906624 7001_ $$0P:(DE-Juel1)187422$$aKunkel, Susanne$$b5$$eCorresponding author
000906624 773__ $$a10.48550/ARXIV.2109.12855
000906624 8564_ $$uhttps://arxiv.org/abs/2109.12855
000906624 8564_ $$uhttps://juser.fz-juelich.de/record/906624/files/dstream.pdf$$yOpenAccess
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000906624 9141_ $$y2022
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000906624 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
000906624 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x1
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