HPC systems are shared between many users.Managing their resources and scheduling compute jobs is a central task of these clusters.Scheduling also allows to control the workload and energy consumption of an HPC system.A Digital Twin of an HPC cluster can aid in the scheduling process by providing energy measurements about the system and predict scheduling decisions with a simulation.For a real-world use case, an integration of the Digital Twin with the scheduler is necessary.A possible use case are energy limitations as part of a demand response process between the HPC operator and energy supplier.Therefore, this paper introduces a plugin for Slurm, an opensource scheduler, that implements a scheduling algorithm for time-dependent cluster-wide power capping.It uses a node energy model to predict the energy consumption of jobs and can start jobs at different frequencies to stay below the configured power limit.The plugin interfaces with the Digital Twin that provides energy measurements for the compute nodes to track the system power consumption in real time and update the power limitations if necessary.The plugin is tested on a cluster and compared against a scheduling simulation of the algorithm.The analysis compares the power profile of the simulation and the real system and the allocation of the jobs over time.Differences in the execution and the power trace are analysed and discussed.

HPC systems have increased in size and power consumption.This has lead to a shift from a pure performance centric standpoint to power and energy aware scheduling and management considerations for HPC.This trend was further accelerated by rising energy prices and the energy crisis that began in 2022.Digital Twins have become valuable tools that enable energy and power aware scheduling of HPC clusters.This paper uses an existing Digital Twin and extends it with a node energy model that allows the prediction of the cluster power consumption.The Digital Twin is then used to simulate system-wide power capping for different energy shortages functions of varying degree.Different policies are proposed and tested towards their effectiveness in improving the job wait times and overall throughput under limiting conditions.Based on a real world HPC cluster, these policies are implemented.Depending on the pattern of the energy limitation and workload, improvements of up to 40 percent are possible compared to scheduling without policies for these conditions.

Demand for compute resources and thus energy demand for HPC is steadily increasing while the energy market transforms to renewable energy and is facing significant price increases.Optimizing energy efficiency of HPC clusters is therefore a major concern.Different possible optimization dimensions are discussed in this paper.This paper presents a digital twin design for analyzing and reducing energy consumption of a real-world HPC system.The digital twin is based on the HPC cluster at PTB.The digital twin receives information from multiple internal and external data sources to cover the different optimization opportunities.The digital twin also consists of a scheduling simulation framework that uses the data from the digital twin and real-world job traces to test the influence of the different parameters on the HPC cluster.