https://dasya.itu.dk/tags/workload-collocation/ Recent content in workload collocation on DASYA Hugo -- gohugo.io en-us © DASYA, IT University of Copenhagen Wed, 05 Aug 2026 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/current/pito-dgxspark/ Wed, 05 Aug 2026 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/current/pito-dgxspark/ This project investigates how running multiple deep learning tasks simultaneously (collocation) affects performance on resource-constrained edge devices, specifically the NVIDIA DGX Spark. Students will deploy and benchmark various model in isolated vs. different collocated scenarios, measure metrics such as GPU utilization, memory usage, training time, and analyze the trade-offs between throughput, fairness, and system efficiency. This project would be suitable as a BSc or MSc thesis. The workload can be adjusted based on the thesis and group size. https://dasya.itu.dk/for-students/proposals/archive/pito-ml-scheduling/ Thu, 05 Jun 2025 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/pito-ml-scheduling/ Workload collocation has been shown as an effective method to reduce the hardware requirements for certain deep learning (DL) training tasks. On the other hand, there hasn’t been many robust open-source implementations of schedulers that incorporate workload collocation on GPUs for DL. BLOX is a framework that aims at standardizing the way we implement deep learning schedulers. In this project, our goal is to investigate ways to integrate a collocation-aware scheduler in this framework. https://dasya.itu.dk/for-students/proposals/archive/mlcollocation/ Mon, 01 Aug 2022 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/mlcollocation/ 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.