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VESTEC: Visual Exploration and Sampling Toolkit for Extreme Computing

Authors :
Markus Flatken
Artur Podobas
Riccardo Fellegara
Achim Basermann
Johannes Holke
David Knapp
Max Kontak
Christian Krullikowski
Michael Nolde
Nick Brown
Rupert Nash
Gordon Gibb
Evgenij Belikov
Steven W. D. Chien
Stefano Markidis
Pierre Guillou
Julien Tierny
Jules Vidal
Charles Gueunet
Johannes Gunther
Miroslaw Pawlowski
Piero Poletti
Giorgio Guzzetta
Mattia Manica
Agnese Zardini
Jean-Pierre Chaboureau
Miguel Mendes
Adrian Cardil
Santiago Monedero
Joaquin Ramirez
Andreas Gerndt
Source :
IEEE Access, Vol 11, Pp 87805-87834 (2023)
Publication Year :
2023
Publisher :
IEEE, 2023.

Abstract

Natural disasters and epidemics are unfortunate recurring events that lead to huge societal and economic loss. Recent advances in supercomputing can facilitate simulations of such scenarios in (or even ahead of) real-time, therefore supporting the design of adequate responses by public authorities. By incorporating high-velocity data from sensors and modern high-performance computing systems, ensembles of simulations and advanced analysis enable urgent decision-makers to better monitor the disaster and to employ necessary actions (e.g., to evacuate populated areas) for mitigating these events. Unfortunately, frameworks to support such versatile and complex workflows for urgent decision-making are only rarely available and often lack in functionalities. This paper gives an overview of the VESTEC project and framework, which unifies orchestration, simulation, in-situ data analysis, and visualization of natural disasters that can be driven by external sensor data or interactive intervention by the user. We show how different components interact and work together in VESTEC and describe implementation details. To disseminate our experience three different types of disasters are evaluated: a Wildfire in La Jonquera (Spain), a Mosquito-Borne disease in two regions of Italy, and the magnetic reconnection in the Earth magnetosphere.

Details

Language :
English
ISSN :
21693536
Volume :
11
Database :
Directory of Open Access Journals
Journal :
IEEE Access
Publication Type :
Academic Journal
Accession number :
edsdoj.8ada4e4b2baa4f3688f17ff48c402082
Document Type :
article
Full Text :
https://doi.org/10.1109/ACCESS.2023.3301177