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.
Bibliographical noteFunding Information:
This research has been performed as a subproject of Project No. K-19-L02-C01-S01 (The National Supercomputing Infrastructure Construction and Service) supported by the KOREA INSTITUTE of SCIENCE and TECHNOLOGY INFORMATION(KISTI).
© 2020 by the authors.
- Job scheduling
- Parallel computing
ASJC Scopus subject areas
- General Materials Science
- General Engineering
- Process Chemistry and Technology
- Computer Science Applications
- Fluid Flow and Transfer Processes