System-Aware Algorithms For Machine Learning
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Author / Producer
Date
2019
Publication Type
Doctoral Thesis
ETH Bibliography
yes
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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.
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published
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Contributors
Examiner : Hofmann, Thomas
Examiner : Pozidis, Haris
Examiner : Jaggi, Martin
Examiner : Hardt, Moritz
Examiner: Püschel, Markus
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Journal / series
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Pages / Article No.
Publisher
ETH Zurich
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Methods
Software
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Subject
machine learning; distributed algorithms; GPU acceleration; convex optimization; generalized linear models; heterogeneous system; snap machine learning; parallel algorithms
Organisational unit
09462 - Hofmann, Thomas / Hofmann, Thomas