Visual Analysis of the High-performance Computing Jobs Based on the Comprehensive Load Scoring Algorithm

High-performance computing system is a complex system composed of a large number of computing nodes and storage nodes. The detailed execution of a job on a cluster system is in a black box. Details about system resources such as CPU, network, and I/O used by jobs cannot be perceived by users or cluster administrators. Users and administrators cannot discover problems on time when a job has inefficient running modes, such as load imbalance and I/O bottlenecks, which seriously hinder the maximum performance of the cluster. Therefore, it is very important to provide an effective method to judge the job load balancing. Based on the comprehensive analysis of high-performance cluster data, an integrated load scoring algorithm is proposed to find the computing nodes with performance problems. The algorithm can identify performance anomalies of different dimensions of performance metrics on nodes. Meanwhile, the visualization diagram of job load balance based on force-oriented layout, time area diagram of node health statistics, and river diagram of I/O fluctuation law is designed to facilitate the system operation. It has been proved that our visual analysis system can improve the efficiency of identifying load imbalancing jobs and slow computing nodes, which improves the job performance and system resilience.