Szczegóły publikacji

Opis bibliograficzny

HPC large-scale pedestrian simulation based on proxemics rules / Paweł Renc, Maciej Bielech, Tomasz Pęcak, Piotr Morawiecki, Mateusz PACIOREK, Wojciech TUREK, Aleksander BYRSKI, Jarosław WĄS // W: Parallel Processing and Applied Mathematics : 13th international conference, PPAM 2019 : Białystok, Poland, September 8–11, 2019 : revised selected papers, Pt. 2 / eds. Roman Wyrzykowski [et al.]. — Wersja do Windows. — Dane tekstowe. — Cham : Springer Nature Switzerland AG, cop. 2020. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 12044. Theoretical Computer Science and General Issues ; ISSN 0302-9743). — ISBN: 978-3-030-43221-8; e-ISBN:  978-3-030-43222-5. — S. 489–499. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 498-499, Abstr.

Autorzy (8)

Słowa kluczowe

crowd dynamicsproxemicspedestrian simulationsupercomputingHPC

Dane bibliometryczne

ID BaDAP128090
Data dodania do BaDAP2020-03-30
DOI10.1007/978-3-030-43222-5_43
Rok publikacji2020
Typ publikacjimateriały konferencyjne (aut.)
Otwarty dostęptak
WydawcaSpringer
KonferencjaInternational Conference on Parallel Processing and Applied Mathematics 2019
Czasopisma/serieLecture Notes in Computer Science, Theoretical Computer Science and General Issues

Abstract

The problem of efficient pedestrian simulation, when large-scale environment is considered, poses a great challenge. When the simulation model size exceeds the capabilities of a single computing node or the results are expected quickly, the simulation algorithm has to use many cores and nodes. The problem considered in the presented work is the task of splitting the data-intensive computations with a common data structure into separate computational domains, while preserving the crucial features of the simulation model. We propose a new model created on the basis of some popular pedestrian models, which can be applied in parallel processing. We describe its implementation in a highly scalable simulation framework. Additionally, the preliminary results are presented and outcomes are discussed.

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