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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14610%2F18%3A00102136" target="_blank" >RIV/00216224:14610/18:00102136 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/s11227-017-2194-4" target="_blank" >https://doi.org/10.1007/s11227-017-2194-4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11227-017-2194-4" target="_blank" >10.1007/s11227-017-2194-4</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF16_013%2F0001802" target="_blank" >EF16_013/0001802: CERIT Scientific Cloud</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

The Journal of Supercomputing

ISSN

0920-8542

e-ISSN

—

Svazek periodika

74

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

22

Strana od-do

1863-1884

Kód UT WoS článku

000430412400005

EID výsledku v databázi Scopus

2-s2.0-85038114439