Tagged with: scheduling

PROPOSAL

BLOX for Deep Learning Task Scheduling with GPU Collocation

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 …
Supervisors: Pınar Tözün, Ehsan Yousefzadeh-Asl-Miandoab
Semester: Fall 2025
Tags: machine learning systems, scheduling, resource management, workload collocation

PROPOSAL

Checkpointing during Deep Learning Training

Deep learning training can run for hours or days, causing long queue times and poor quality of service (QoS) on shared clusters. Since schedulers can’t accurately predict training durations, they often wait for jobs to finish or time out, worsening the issue. Frameworks like TensorFlow and PyTorch support model checkpointing, but frequent checkpoints can slow training, while infrequent ones reduce …
Supervisors: Pınar Tözün, Ehsan Yousefzadeh-Asl-Miandoab
Semester: Fall 2025
Tags: machine learning systems, checkpointing, scheduling, resource management