PORTALE DELLA DIDATTICA

Ricerca CERCA
  KEYWORD

Telecommunication Networks Group

Workload characterization of Machine Learning workloads

estero Thesis abroad


keywords ALGORITHMS, EXPERIMENTAL DESIGN, TESTBED

Reference persons CLAUDIO ETTORE CASETTI, CARLA FABIANA CHIASSERINI, PAOLO GIACCONE

External reference persons Prof. Dr. Holger Karl, Hasso Plattner Institute, Potzdam University (Berlin), Germany

Research Groups Telecommunication Networks Group

Thesis type EXPERIMENTAL AND MODELING

Description 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.

See also  https://hpi.de/karl/teaching/bachelor-and-master-theses.html

Required skills 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


Deadline 02/08/2023      PROPONI LA TUA CANDIDATURA




© Politecnico di Torino
Corso Duca degli Abruzzi, 24 - 10129 Torino, ITALY
Contatti