Workload characterization of Machine Learning workloads
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.
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