A Novel Approach for Job Scheduling Optimizations Under Power Cap for ARM and Intel HPC Systems

• Published in: 2017 IEEE 24th International Conference on High Performance Computing (HiPC) pp. 142 - 151
• Main Authors: Rajagopal, Dineshkumar, Tafani, Daniele, Georgiou, Yiannis, Glesser, David, Ott, Michael
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2017
• Subjects: Adaptive scheduling; ARM; Energy consumption; energy efficiency; Hardware; Intel; job scheduling; Monitoring; powercapping; Processor scheduling; Prototypes; Resource management; SLURM
• DOI: 10.1109/HiPC.2017.00025

The ever-increasing energy demands of modern High Performance Computing (HPC) platforms is undeniably one of the most critical aspects for the future design and evolution of such systems. The capability of managing their energy consumption not only allows for significant reduction in electricity costs but is also a step forward on the road towards the exascale. Powercapping is a widely studied technique that contributes to address this challenge by instantaneously setting and maintaining a predefined power threshold (power cap) that cannot be exceeded. However, the lack of a centralized mechanism responsible for efficiently allocating the available power among resources and jobs may ultimately yield to fragmentation, low system utilization and increased user waiting times. Additionally, power cap violations can lead to high risk scenarios and/or increase operational costs. This paper proposes to prevent such issues with the introduction of the Enhanced Power Adaptive Scheduling (E-PAS) algorithm. The E-PAS algorithm combines scheduling and resource management mechanisms, correlating estimated and real power consumption data in order to optimize the resource utilization of the platform under a predefined power cap. The algorithm has been implemented in the widely used open-source resource and job management system SLURM and is planned to be pushed in a future mainstream version. Its effectiveness has been evaluated through real-scale experiments respectively on an ARM- and an Intel-based cluster of comparable size. All experiments have been performed using synthetic workloads from a set of mini-applications.