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  KEYWORD

Workload characterization of Machine Learning workloads

estero Tesi all'estero


Parole chiave ALGORITHMS, EXPERIMENTAL DESIGN, TESTBED

Riferimenti CLAUDIO ETTORE CASETTI, CARLA FABIANA CHIASSERINI, PAOLO GIACCONE

Riferimenti esterni Prof. Dr. Holger Karl, Hasso Plattner Institute, Potzdam University (Berlin), Germany

Gruppi di ricerca Telecommunication Networks Group

Tipo tesi EXPERIMENTAL AND MODELING

Descrizione When trying to run machine-learning workloads in resource-limited environments, and understanding of their performance characteristics is useful: how much does an ML workload profit from additional cores or additional memory? What does that mean, specifically, for inference or training? If, e.g., training can be split over multiple machines, what data flows ensue, what are performance impacts? Overall, how malleable are these workloads?

The goal of this thesis is to develop the notion of a performance profile (so-far used mostly for conventional applications) to ML workloads. Then, an existing profiling environment should be extended to deal with such workloads and for characteristic examples, the concept should be proven by example characterizations.

Vedi anche  https://hpi.de/karl/teaching/bachelor-and-master-theses.html

Conoscenze richieste Very good understanding of machine-learning techniques and practical implementations
Good understanding of concepts like microservices
Good implementation and system skills (e.g., scripting) are a clear plus


Scadenza validita proposta 02/08/2022      PROPONI LA TUA CANDIDATURA




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