Difference between revisions of "Publications/guirado.05.pdmc"
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{{Publication |
{{Publication |
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| − | | |
+ | | published = true |
| + | | date = 2005-05-23 |
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| authors = Guillaume Guirado, Thomas Herault, Richard Lassaigne, Sylvain Peyronnet |
| authors = Guillaume Guirado, Thomas Herault, Richard Lassaigne, Sylvain Peyronnet |
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| title = Distribution, approximation and probabilistic model checking |
| title = Distribution, approximation and probabilistic model checking |
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| booktitle = Proceedings of the 4th international workshop on Parallel and Distributed Model Checking (PDMC) |
| booktitle = Proceedings of the 4th international workshop on Parallel and Distributed Model Checking (PDMC) |
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| − | | project = APMC |
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| − | | urllrde = 200507-Pdmc |
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| ⚫ | | abstract = APMC is a model checker dedicated to the quantitative verification of fully probabilistic systems against LTL formulas. Using a Monte-Carlo method in order to efficiently approximate the verification of probabilistic specifications, it could be used naturally in a distributed framework. We present here the tool and his distribution scheme, together with extensive performance evaluationshowing the scalability of the method, even on clusters containing 500+ heterogeneous workstations. |
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| lrdeprojects = APMC |
| lrdeprojects = APMC |
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| ⚫ | | abstract = APMC is a model checker dedicated to the quantitative verification of fully probabilistic systems against LTL formulas. Using a Monte-Carlo method in order to efficiently approximate the verification of probabilistic specifications, it could be used naturally in a distributed framework. We present here the tool and his distribution scheme, together with extensive performance evaluationshowing the scalability of the method, even on clusters containing 500+ heterogeneous workstations. |
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| + | | lrdenewsdate = 2005-05-23 |
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| type = inproceedings |
| type = inproceedings |
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| id = guirado.05.pdmc |
| id = guirado.05.pdmc |
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and Distributed Model Checking (PDMC)<nowiki>}</nowiki>, |
and Distributed Model Checking (PDMC)<nowiki>}</nowiki>, |
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year = 2005, |
year = 2005, |
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| − | project = <nowiki>{</nowiki>APMC<nowiki>}</nowiki>, |
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abstract = <nowiki>{</nowiki>APMC is a model checker dedicated to the quantitative |
abstract = <nowiki>{</nowiki>APMC is a model checker dedicated to the quantitative |
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verification of fully probabilistic systems against LTL |
verification of fully probabilistic systems against LTL |
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scheme, together with extensive performance evaluation, |
scheme, together with extensive performance evaluation, |
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showing the scalability of the method, even on clusters |
showing the scalability of the method, even on clusters |
||
| − | containing 500+ heterogeneous workstations.<nowiki>}</nowiki> |
+ | containing 500+ heterogeneous workstations.<nowiki>}</nowiki> |
| − | lrdeprojects = <nowiki>{</nowiki>APMC<nowiki>}</nowiki> |
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<nowiki>}</nowiki> |
<nowiki>}</nowiki> |
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Latest revision as of 12:15, 26 April 2016
- Authors
- Guillaume Guirado, Thomas Herault, Richard Lassaigne, Sylvain Peyronnet
- Where
- Proceedings of the 4th international workshop on Parallel and Distributed Model Checking (PDMC)
- Type
- inproceedings
- Projects
- APMC
- Date
- 2005-05-23
Abstract
APMC is a model checker dedicated to the quantitative verification of fully probabilistic systems against LTL formulas. Using a Monte-Carlo method in order to efficiently approximate the verification of probabilistic specifications, it could be used naturally in a distributed framework. We present here the tool and his distribution scheme, together with extensive performance evaluationshowing the scalability of the method, even on clusters containing 500+ heterogeneous workstations.
Bibtex (lrde.bib)
@InProceedings{ guirado.05.pdmc,
author = {Guillaume Guirado and Thomas Herault and Richard Lassaigne
and Sylvain Peyronnet},
title = {Distribution, approximation and probabilistic model
checking},
booktitle = {Proceedings of the 4th international workshop on Parallel
and Distributed Model Checking (PDMC)},
year = 2005,
abstract = {APMC is a model checker dedicated to the quantitative
verification of fully probabilistic systems against LTL
formulas. Using a Monte-Carlo method in order to
efficiently approximate the verification of probabilistic
specifications, it could be used naturally in a distributed
framework. We present here the tool and his distribution
scheme, together with extensive performance evaluation,
showing the scalability of the method, even on clusters
containing 500+ heterogeneous workstations.}
}