Performance Modeling for Co-Scheduling in HPC Systems

Abstract

Co-Scheduling jobs in High Performance Computing (HPC) systems offers significant potential to improve system throughput and energy efficiency. However, resource contention in shared node resources can introduce performance degradation, leading to job slowdowns and counteracting these benefits. To address this challenge, sophisticated co-scheduling algorithms must be developed, requiring a good understanding of the submitted applications to make informed scheduling decisions. In this thesis, we classify and present a number of performance models that can be leveraged to support advanced co-scheduling strategies. The methods focus on either assigning specific ‘tags’ to applications or predicting their potential speedup or slowdown when co-executed with other workloads. To achieve this, we explore both empirical approaches alongside Machine Learning-based techniques, assessing their respective benefits and limitations. Furthermore, we discuss key trade-offs that arise when selecting and building the most suitable model for co-location prediction in HPC environments. We then provide preliminary results demonstrating the effectiveness of each model through representative examples across multiple model categories. Finally, we provide an initial evaluation of co-scheduling's potential to enhance the makespan of a given schedule, as well as the trade-offs involved in balancing system performance and user satisfaction in HPC systems.