<div class="csl-bib-body">
<div class="csl-entry">Sarközi, B. A. (2021). <i>To Co-schedule or not to co-schedule? Efficiently utilizing large multicore machines</i> [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2021.87732</div>
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dc.identifier.uri
https://doi.org/10.34726/hss.2021.87732
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/18868
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dc.description.abstract
Hardware components of computing systems are improving constantly, which leads to an increasing number of cores on multicore machines and computing clusters. The most powerful supercomputer in Austria, the VSC-4, has 37920 cores, and it is important to utilize the cores efficiently. Therefore, it is necessary to execute parallel applications methodically, e.g., by simultaneously executing (co-scheduling) them on compute nodes. In this work, we predict whether two parallel applications running on a multicore machine should be co-scheduled or not. Two applications should only be co scheduled, if there is at most a small slowdown of the co-scheduled runtime compared to the dedicated runtime of an application. We are interested in predicting this co-scheduling potential of long executions by sampling a short execution. We start by assessing runtime and scalability behaviors of diverse OpenMP applications and determine co-scheduling strategies leading to resource sharing conflicts of compute kernel sections. Then, we analyze hardware performance counters and select a subset of relevant counters indicating slowdowns. Finally, we create a prediction model using logistic regression and predict the co scheduling potential of two applications using performance counters. This thesis shows that executions on different sockets do not lead to resource sharing conflicts, whereas sharing sockets with a scatter affinity mapping lead to increased kernel times. We show the necessity of synchronizing kernel sections of co-scheduled applications, and therefore introduce a synchronization library. Our prediction models demonstrate the possibility of predicting co-scheduling potentials of two applications, which represents an efficient way of utilizing multicore machines.
en
dc.language
English
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dc.language.iso
en
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dc.rights.uri
http://rightsstatements.org/vocab/InC/1.0/
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dc.subject
HPC
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dc.subject
Co-Scheduling
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dc.subject
Parallel Computing
en
dc.subject
OpenMP
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dc.subject
Hardware Performance Counters
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dc.subject
Performance Prediction
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dc.title
To Co-schedule or not to co-schedule? Efficiently utilizing large multicore machines
en
dc.title.alternative
Co-Scheduling oder kein Co-Scheduling? Effiziente Ausnutzung von Großen Mehrkernrechnern
de
dc.type
Thesis
en
dc.type
Hochschulschrift
de
dc.rights.license
In Copyright
en
dc.rights.license
Urheberrechtsschutz
de
dc.identifier.doi
10.34726/hss.2021.87732
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dc.contributor.affiliation
TU Wien, Österreich
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dc.rights.holder
Barbara Anna Sarközi
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dc.publisher.place
Wien
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tuw.version
vor
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tuw.thesisinformation
Technische Universität Wien
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tuw.publication.orgunit
E191 - Institut für Computer Engineering
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dc.type.qualificationlevel
Diploma
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dc.identifier.libraryid
AC16384789
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dc.description.numberOfPages
112
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dc.thesistype
Diplomarbeit
de
dc.thesistype
Diploma Thesis
en
dc.rights.identifier
In Copyright
en
dc.rights.identifier
Urheberrechtsschutz
de
tuw.advisor.staffStatus
staff
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item.languageiso639-1
en
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item.fulltext
with Fulltext
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item.openaccessfulltext
Open Access
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item.mimetype
application/pdf
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item.openairetype
master thesis
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item.grantfulltext
open
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item.openairecristype
http://purl.org/coar/resource_type/c_bdcc
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item.cerifentitytype
Publications
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crisitem.author.dept
E194 - Institut für Information Systems Engineering