Log Analysis-Based Resource and Execution Time Improvement in HPC: A Case Study

Abstract

High-performance computing (HPC) uses many distributed computing resources to solve large computational science problems through parallel computation. Such an approach can reduce overall job execution time and increase the capacity of solving large-scale and complex problems. In the supercomputer, the job scheduler, the HPC’s flagship tool, is responsible for distributing and managing the resources of large systems. In this paper, we analyze the execution log of the job scheduler for a certain period of time and propose an optimization approach to reduce the idle time of jobs. In our experiment, it has been found that the main root cause of delayed job is highly related to resource waiting. The execution time of the entire job is affected and significantly delayed due to the increase in idle resources that must be ready when submitting the large-scale job. The backfilling algorithm can optimize the inefficiency of these idle resources and help to reduce the execution time of the job. Therefore, we propose the backfilling algorithm, which can be applied to the supercomputer. This experimental result shows that the overall execution time is reduced.

Improvements to Supercomputing Service Availability Based on Data Analysis

“…The Nurion system is a cluster-type supercomputer [9], the hardware architecture of which is illustrated in Figure 1. It consists of various infrastructure nodes connected by a high-performance interconnect, including computing nodes, storage systems, login nodes, management nodes, datemover (DM) nodes, and web servers.…”