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Partitioned High Performance Code Coupling Applied to CFD Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Towards Simulating Data-Driven Brain Models at the Point Neuron Level on Petascale Computers

Authors : Till Schumann, Csaba Erő, Marc-Oliver Gewaltig, Fabien Jonathan Delalondre

Published in: High-Performance Scientific Computing

Publisher: Springer International Publishing

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Abstract

We present a solution to two important problems that arise in the simulation of large data-driven neural networks: (a) efficient loading of network descriptions and (b) efficient instantiation of the network by executing the model specification. To address the first problem, we present a general data-format PointBrainH5, to store the network information along with the parallel-distributed RTC algorithm to efficiently load and instantiate a network model. We test data-format and algorithm on a data-driven simulation of the size of a full mouse brain on 4 racks of a IBM Blue Gene/Q. The model comprised 75 million neurons with 664 billion synapses and occupied 15 TB on disk. Loading and instantiation of the network on 4 racks of the BlueGene/Q took 30 min. We observe good scaling for up to 16,384 nodes.

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Metadata
Title
Towards Simulating Data-Driven Brain Models at the Point Neuron Level on Petascale Computers
Authors
Till Schumann
Csaba Erő
Marc-Oliver Gewaltig
Fabien Jonathan Delalondre
Copyright Year
2017
Book
High-Performance Scientific Computing

Print ISBN: 978-3-319-53861-7

Electronic ISBN: 978-3-319-53862-4

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-53862-4_14

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