Studying Collocation for Machine Learning

Supervisors: Pınar Tözün
Semester: Fall 2022
Tags: benchmarking, workload collocation, machine learning

State-of-the-art machine learning models are known to be compute- and power-hungry. On the other hand, modern servers come equipped with really powerful CPU-GPU co-processors. Not all machine learning models are able to use all the available hardware resources on such servers.

Workload collocation is a mechanism to increase hardware utilization when a single workload is not able to utilize all the resources on its own. While workload collocation has been heavily studied for traditional data-intensive applications such as relational databases or Spark, it has been largely unexplored for machine learning.

In this project, our goal would be to explore the impact of workload collocation for machine learning on modern hardware. There are many ways to go about this exploration, so if you are interested in this, please contact us, and we can chat about which direction to go to scope things down.

The project can be adjusted for a regular semester project, a BSc thesis, or an MSc thesis.