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Completion Probabilities and Parallel Restart Strategies under an Imposed Deadline

Let A be any fixed cut-off restart algorithm running in parallel on multiple processors. If the algorithm is only allowed to run for up to time D, then it is no longer guaranteed that a result can be found. In this case, the probability of finding a solution within the time D becomes a measure for t...

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Autor principal: Lorenz, Jan-Hendrik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5061357/
https://www.ncbi.nlm.nih.gov/pubmed/27732631
http://dx.doi.org/10.1371/journal.pone.0164605
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author Lorenz, Jan-Hendrik
author_facet Lorenz, Jan-Hendrik
author_sort Lorenz, Jan-Hendrik
collection PubMed
description Let A be any fixed cut-off restart algorithm running in parallel on multiple processors. If the algorithm is only allowed to run for up to time D, then it is no longer guaranteed that a result can be found. In this case, the probability of finding a solution within the time D becomes a measure for the quality of the algorithm. In this paper we address this issue and provide upper and lower bounds for the probability of A finding a solution before a deadline passes under varying assumptions. We also show that the optimal restart times for a fixed cut-off algorithm running in parallel is identical for the optimal restart times for the algorithm running on a single processor. Finally, we conclude that the odds of finding a solution scale superlinearly in the number of processors.
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spelling pubmed-50613572016-10-27 Completion Probabilities and Parallel Restart Strategies under an Imposed Deadline Lorenz, Jan-Hendrik PLoS One Research Article Let A be any fixed cut-off restart algorithm running in parallel on multiple processors. If the algorithm is only allowed to run for up to time D, then it is no longer guaranteed that a result can be found. In this case, the probability of finding a solution within the time D becomes a measure for the quality of the algorithm. In this paper we address this issue and provide upper and lower bounds for the probability of A finding a solution before a deadline passes under varying assumptions. We also show that the optimal restart times for a fixed cut-off algorithm running in parallel is identical for the optimal restart times for the algorithm running on a single processor. Finally, we conclude that the odds of finding a solution scale superlinearly in the number of processors. Public Library of Science 2016-10-12 /pmc/articles/PMC5061357/ /pubmed/27732631 http://dx.doi.org/10.1371/journal.pone.0164605 Text en © 2016 Jan-Hendrik Lorenz http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Lorenz, Jan-Hendrik
Completion Probabilities and Parallel Restart Strategies under an Imposed Deadline
title Completion Probabilities and Parallel Restart Strategies under an Imposed Deadline
title_full Completion Probabilities and Parallel Restart Strategies under an Imposed Deadline
title_fullStr Completion Probabilities and Parallel Restart Strategies under an Imposed Deadline
title_full_unstemmed Completion Probabilities and Parallel Restart Strategies under an Imposed Deadline
title_short Completion Probabilities and Parallel Restart Strategies under an Imposed Deadline
title_sort completion probabilities and parallel restart strategies under an imposed deadline
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5061357/
https://www.ncbi.nlm.nih.gov/pubmed/27732631
http://dx.doi.org/10.1371/journal.pone.0164605
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