Improving wildland fire prediction on MPI clusters

B. Abdalhaq, G. Bianchini, A. Cortés, T. Margalef, E. Luque

    Research output: Contribution to journalArticleResearchpeer-review

    2 Citations (Scopus)

    Abstract

    One of the challenges still open to wildland fire simulators is the capacity of working under real-time constrains with the aim of providing fire spread predictions that could be useful in fire mitigation interventions. In this paper, a parallel optimization framework for improving wildland fire prediction is applied to a real laboratory fire. The proposed prediction methodology has been tested on a Linux cluster using MPI. © Springer-Verlag Berlin Heidelberg 2003.
    Original languageEnglish
    Pages (from-to)520-528
    JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume2840
    Publication statusPublished - 1 Dec 2003

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