Capacity Estimation in HPC Systems: Simulation Approach

Abstract

As HPC (high performance computing) systems are extensively employed for heavy computational problems throughout heterogeneous environments, the scale and complexity of applications raises the issue of capacity planning. A cardinal aspect of efficiency is the job scheduler in any HPC systems. The job scheduling techniques can worsen or mitigate issues such as job starvation, increased queue time, and decreased system utilization. Since the impact of scheduling techniques is dependent on the workload of a supercomputer, this research proposes to analyze various scheduling disciplines on a given workload. By simulating HPC system, for any given workload, we can find the paradigm that yields the best performance, i.e. minimizing the wait time of jobs in the queue while maximizing resource utilization. Furthermore, given a fixed configuration of a HPC system, this research can be used to determine an appropriate workload that optimizes the system's performance. The development and implementation of such complex simulation framework for HPC does not yet exist in HPC's literature. The efficiency of the proposed simulation framework is illustrated through simulation results of performance measures such as average queuing time, average number of jobs in the queue, and system utilization. These results are verified by a developed mathematical model for job load characterization.