A Case Study - Cost of Preemption for Urgent Computing on SuperMUC

Abstract

Urgent computing requires computations to commence in short order and complete within a stipulated deadline so as to support mitigation activities in preparation, response and recovery from an event that requires immediate attention. As such, acquiring computation resources swiftly is crucial. Preemptive scheduling, terminating an existing job(s) to make way for an urgent job, is one of the most common approach considered. However, public resource providers are typically faced with policy restrictions that forbid them from allowing preemption. The interruption of existing jobs is believed to have a significant consequence, i.e. cost, to the users and resource providers. This case study on a public HPC resource, SuperMUC, hosted at Leibniz Supercomputing Centre aims to study the cost of preemption. Two cost models, least cost and least disruptive, will be used. With this, we want to demonstrate that the cost of preemption is in fact much lower in comparison to the loss mitigation that can be achieved by allowing an urgent computation. The ultimate aim is to provide evidence to convince policy makers on the feasibility and benefits of supporting urgent computing on public resources.