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

J 2018

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

FOUSEK, Jan

Základní údaje

Originální název

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

Autoři

FOUSEK, Jan (203 Česká republika, garant, domácí)

Vydání

The Journal of Supercomputing, Springer US, 2018, 0920-8542

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 2.157

Kód RIV

RIV/00216224:14610/18:00102136

Organizační jednotka

Ústav výpočetní techniky

DOI

http://dx.doi.org/10.1007/s11227-017-2194-4

UT WoS

000430412400005

Klíčová slova anglicky

Neural field;Sparse matrix;SpMV;Delay differential equations;Data locality

Štítky

rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 24. 1. 2019 16:02, Mgr. Alena Mokrá

Anotace

V originále

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 2x on single-socket CPU, and 1.25x on Xeon Phi accelerator.

Návaznosti

EF16_013/0001802, projekt VaV

Název: CERIT Scientific Cloud

MUNI/A/0897/2016, interní kód MU

Název: Rozsáhlé výpočetní systémy: modely, aplikace a verifikace VI.

Investor: Masarykova univerzita, Rozsáhlé výpočetní systémy: modely, aplikace a verifikace VI., DO R. 2020_Kategorie A - Specifický výzkum - Studentské výzkumné projekty

Citovat

FOUSEK, Jan. Efficient sparse matrix-delayed vector multiplication for discretized neural field model. The Journal of Supercomputing. Springer US, 2018, roč. 74, č. 5, s. 1863-1884. ISSN 0920-8542. Dostupné z: https://dx.doi.org/10.1007/s11227-017-2194-4.

@article{1399331,
   author = {Fousek, Jan},
   article_number = {5},
   doi = {http://dx.doi.org/10.1007/s11227-017-2194-4},
   keywords = {Neural field;Sparse matrix;SpMV;Delay differential equations;Data locality},
   language = {eng},
   issn = {0920-8542},
   journal = {The Journal of Supercomputing},
   title = {Efficient sparse matrix-delayed vector multiplication for discretized neural field model},
   url = {https://doi.org/10.1007/s11227-017-2194-4},
   volume = {74},
   year = {2018}
}

TY  - JOUR
ID  - 1399331
AU  - Fousek, Jan
PY  - 2018
TI  - Efficient sparse matrix-delayed vector multiplication for discretized neural field model
JF  - The Journal of Supercomputing
VL  - 74
IS  - 5
SP  - 1863-1884
EP  - 1863-1884
PB  - Springer US
SN  - 09208542
KW  - Neural field;Sparse matrix;SpMV;Delay differential equations;Data locality
UR  - https://doi.org/10.1007/s11227-017-2194-4
L2  - https://doi.org/10.1007/s11227-017-2194-4
N2  - 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 2x on single-socket CPU, and 1.25x on Xeon Phi accelerator.
ER  -

FOUSEK, Jan. Efficient sparse matrix-delayed vector multiplication for discretized neural field model. \textit{The Journal of Supercomputing}. Springer US, 2018, roč.~74, č.~5, s.~1863-1884. ISSN~0920-8542. Dostupné z: https://dx.doi.org/10.1007/s11227-017-2194-4.

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