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| Hauptseite > Publikationsdatenbank > Routing brain traffic through the von Neumann bottleneck: Efficient cache usage in spiking neural network simulation code on general purpose computers > print |
| Hauptseite > Publikationsdatenbank > Routing brain traffic through the von Neumann bottleneck: Efficient cache usage in spiking neural network simulation code on general purpose computers > print |
| 001 | 906624 | ||
| 005 | 20250314084120.0 | ||
| 024 | 7 | _ | |a 10.48550/ARXIV.2109.12855 |2 doi |
| 024 | 7 | _ | |a 2128/30873 |2 Handle |
| 037 | _ | _ | |a FZJ-2022-01560 |
| 100 | 1 | _ | |a Pronold, Jari |0 P:(DE-Juel1)165321 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Routing brain traffic through the von Neumann bottleneck: Efficient cache usage in spiking neural network simulation code on general purpose computers |
| 260 | _ | _ | |c 2021 |b arXiv |
| 336 | 7 | _ | |a Preprint |b preprint |m preprint |0 PUB:(DE-HGF)25 |s 1648190536_1087 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a WORKING_PAPER |2 ORCID |
| 336 | 7 | _ | |a Electronic Article |0 28 |2 EndNote |
| 336 | 7 | _ | |a preprint |2 DRIVER |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a Output Types/Working Paper |2 DataCite |
| 520 | _ | _ | |a Simulation 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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| 536 | _ | _ | |a HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907) |0 G:(EU-Grant)785907 |c 785907 |f H2020-SGA-FETFLAG-HBP-2017 |x 1 |
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| 650 | _ | 7 | |a Distributed, Parallel, and Cluster Computing (cs.DC) |2 Other |
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| 700 | 1 | _ | |a Jordan, Jakob |0 P:(DE-Juel1)151356 |b 1 |
| 700 | 1 | _ | |a Wylie, Brian J. N. |0 P:(DE-Juel1)132302 |b 2 |u fzj |
| 700 | 1 | _ | |a Kitayama, Itaru |0 P:(DE-Juel1)187457 |b 3 |u fzj |
| 700 | 1 | _ | |a Diesmann, Markus |0 P:(DE-Juel1)144174 |b 4 |u fzj |
| 700 | 1 | _ | |a Kunkel, Susanne |0 P:(DE-Juel1)187422 |b 5 |e Corresponding author |
| 773 | _ | _ | |a 10.48550/ARXIV.2109.12855 |
| 856 | 4 | _ | |u https://arxiv.org/abs/2109.12855 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/906624/files/dstream.pdf |y OpenAccess |
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| 914 | 1 | _ | |y 2022 |
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