GPU Cluster Scheduling for Network-Sensitive Deep Learning. (arXiv:2401.16492v1 [cs.PF])

We propose a novel GPU-cluster scheduler for distributed DL (DDL) workloads
that enables proximity based consolidation of GPU resources based on the DDL
jobs' sensitivities to the anticipated communication-network delays. Our
scheduler consists of three major components: (i) a classical delay scheduling
algorithm to facilitate job placement and consolidation; (ii) a
network-sensitive job preemption strategy; and (iii) an "auto-tuner" mechanism
to optimize delay timers for effective delay scheduling. Additionally, to
enable a cost-effective methodology for large-scale experiments, we develop a …

algorithm arxiv cluster communication components consolidation cs.pf deep learning delay distributed gpu gpu resources job jobs major network novel placement resources scheduling workloads