GPU Cluster Scheduling for Network-Sensitive Deep Learning

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