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dc.contributor.author
Mendler-Dünner, Celestine
dc.contributor.supervisor
Hofmann, Thomas
dc.contributor.supervisor
Pozidis, Haris
dc.contributor.supervisor
Jaggi, Martin
dc.contributor.supervisor
Hardt, Moritz
dc.contributor.supervisor
Püschel, Markus
dc.date.accessioned
2019-05-07T11:37:18Z
dc.date.available
2019-05-07T11:27:27Z
dc.date.available
2019-05-07T11:37:18Z
dc.date.issued
2019
dc.identifier.uri
http://hdl.handle.net/20.500.11850/341033
dc.identifier.doi
10.3929/ethz-b-000341033
dc.description.abstract
The design of machine learning algorithms is often conducted in a system-agnostic manner. As a consequence, established methods may not be well aligned with the particularities of the systems on which they are deployed. In this thesis we demonstrate that there is a huge potential for improving the performance and efficiency of machine learning applications by incorporating system characteristics into the algorithm design. We develop new principled tools and methods for training machine learning models that are theoretically sound and enable the systematic utilization of individual hardware resources available in heterogeneous systems. In particular, we focus on lowering the impact of slow interconnects on distributed training and exploiting hierarchical memory structures, compute parallelism and accelerator units.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
ETH Zurich
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.subject
machine learning
en_US
dc.subject
distributed algorithms
en_US
dc.subject
GPU acceleration
en_US
dc.subject
convex optimization
en_US
dc.subject
generalized linear models
en_US
dc.subject
heterogeneous system
en_US
dc.subject
snap machine learning
en_US
dc.subject
parallel algorithms
en_US
dc.title
System-Aware Algorithms For Machine Learning
en_US
dc.type
Doctoral Thesis
dc.rights.license
In Copyright - Non-Commercial Use Permitted
dc.date.published
2019-05-07
ethz.size
170 p.
en_US
ethz.code.ddc
DDC - DDC::0 - Computer science, information & general works::004 - Data processing, computer science
ethz.identifier.diss
25740
en_US
ethz.publication.place
Zurich
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02661 - Institut für Maschinelles Lernen / Institute for Machine Learning::09462 - Hofmann, Thomas / Hofmann, Thomas
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02661 - Institut für Maschinelles Lernen / Institute for Machine Learning::09462 - Hofmann, Thomas / Hofmann, Thomas
en_US
ethz.date.deposited
2019-05-07T11:28:01Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2019-05-07T11:37:36Z
ethz.rosetta.lastUpdated
2021-02-15T04:30:18Z
ethz.rosetta.versionExported
true
ethz.COinS
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