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dc.contributor.author
Pauli, Stefan
dc.contributor.author
Arbenz, Peter
dc.contributor.author
Schwab, Christoph
dc.date.accessioned
2022-09-19T09:30:23Z
dc.date.available
2022-09-19T09:30:23Z
dc.date.issued
2012-08
dc.identifier.uri
http://hdl.handle.net/20.500.11850/571352
dc.identifier.doi
10.3929/ethz-a-010387066
dc.description.abstract
Monte Carlo (MC) and Multilevel Monte Carlo (MLMC) methods applied to solvers for Partial Differential Equations with random input data are shown to exhibit intrinsic failure resilience. Sufficient conditions are provided for non-recoverable loss of a random fraction of samples not to fatally damage the asymptotic accuracy vs. work of an MC simulation. Specifically, the convergence behavior of MLMC methods on massively parallel hardware is analyzed mathematically and computationally, under general assumptions on the node failures and on the sample failure statistics on the different MC levels, in the absence of checkpointing, i.e. we assume irrecoverable sample failures with complete loss of data. Modifications of the MLMC with enhanced resilience are proposed. The theoretical results are obtained under general statistical models of CPU failure at runtime. Specifically, node failures with the so-called Weibull failure models on massively parallel stochastic Finite Volume computational fluid dynamics simulations are discussed.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Seminar for Applied Mathematics, ETH Zurich
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.subject
Multilevel Monte Carlo
en_US
dc.subject
Fault tolerance
en_US
dc.subject
Failure resilience
en_US
dc.subject
Exascale parallel computing
en_US
dc.title
Intrinsic Fault Tolerance of Multi Level Monte Carlo Methods
en_US
dc.type
Report
dc.rights.license
In Copyright - Non-Commercial Use Permitted
ethz.journal.title
SAM Research Report
ethz.journal.volume
2012-24
en_US
ethz.size
21 p.
en_US
ethz.code.ddc
DDC - DDC::5 - Science::510 - Mathematics
en_US
ethz.grant
Automated Urban Parking and Driving
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ethz.publication.place
Zurich
en_US
ethz.publication.status
published
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ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02643 - Institut für Theoretische Informatik / Inst. Theoretical Computer Science::03340 - Widmayer, Peter (emeritus) / Widmayer, Peter (emeritus)::08805 - Arbenz, Peter (Tit.-Prof.)
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02000 - Dep. Mathematik / Dep. of Mathematics::02501 - Seminar für Angewandte Mathematik / Seminar for Applied Mathematics::03435 - Schwab, Christoph / Schwab, Christoph
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02643 - Institut für Theoretische Informatik / Inst. Theoretical Computer Science::03340 - Widmayer, Peter (emeritus) / Widmayer, Peter (emeritus)::08805 - Arbenz, Peter (Tit.-Prof.)
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02000 - Dep. Mathematik / Dep. of Mathematics::02501 - Seminar für Angewandte Mathematik / Seminar for Applied Mathematics::03435 - Schwab, Christoph / Schwab, Christoph
ethz.identifier.url
https://math.ethz.ch/sam/research/reports.html?id=467
ethz.grant.agreementno
247277
ethz.grant.fundername
EC
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.program
FP7
ethz.date.deposited
2017-06-10T11:38:20Z
ethz.source
ECOL
ethz.source
ECIT
ethz.identifier.importid
imp59366b6f527bf74823
ethz.identifier.importid
imp5936500cc4b7023615
ethz.ecolpid
eth:47372
ethz.ecitpid
pub:94794
ethz.eth
yes
en_US
ethz.availability
Open access
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ethz.rosetta.installDate
2022-09-19T09:30:34Z
ethz.rosetta.lastUpdated
2023-02-07T06:24:12Z
ethz.rosetta.versionExported
true
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/154926
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/59268
ethz.COinS
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