Workload characterization of Machine Learning workloads
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