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The Journal of Supercomputing
Erschienen in: The Journal of Supercomputing 5/2018

15.12.2017

Efficient sparse matrix-delayed vector multiplication for discretized neural field model

verfasst von: Jan Fousek

Erschienen in: The Journal of Supercomputing | Ausgabe 5/2018

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Abstract

Computational models of the human brain provide an important tool for studying the principles behind brain function and disease. To achieve whole-brain simulation, models are formulated at the level of neuronal populations as systems of delayed differential equations. In this paper, we show that the integration of large systems of sparsely connected neural masses is similar to well-studied sparse matrix-vector multiplication; however, due to delayed contributions, it differs in the data access pattern to the vectors. To improve data locality, we propose a combination of node reordering and tiled schedules derived from the connectivity matrix of the particular system, which allows performing multiple integration steps within a tile. We present two schedules: with a serial processing of the tiles and one allowing for parallel processing of the tiles. We evaluate the presented schedules showing speedup up to \(2\,\times \) on single-socket CPU, and \(1.25\,\times \) on Xeon Phi accelerator.
Vorheriger Artikel Design of an accurate and high-speed binocular pupil tracking system based on GPGPUs
Nächster Artikel Popularity-based covering sets for energy proportionality in shared-nothing clusters

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Fußnoten
1
For anatomical and functional atlases, the number of areas ranges typically from 80 to 200, e.g., [6, 34].
 
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Metadaten
Titel
Efficient sparse matrix-delayed vector multiplication for discretized neural field model
verfasst von
Jan Fousek
Publikationsdatum
15.12.2017
Verlag
Springer US
Erschienen in
The Journal of Supercomputing / Ausgabe 5/2018
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-017-2194-4

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