Predicting running time of aerodynamic jobs in HPC system by combining supervised and unsupervised learning method  

作　　者：Hao Wang Yi-Qin Dai Jie Yu Yong Dong 

机构地区：[1]College of Computer Science and Technology,National University of Defense Technology,Changsha,China [2]China Aerodynamics Research and Development Center,Mianyang,China.

出　　处：《Advances in Aerodynamics》2021年第1期380-397,共18页空气动力学进展（英文）

基　　金：supported by the National Numerical Windtunnel project,project number 2018-ZT6B13.

摘　　要：Improving resource utilization is an important goal of high-performance computing systems of supercomputing centers.To meet this goal,the job scheduler of high-performance computing systems often uses backfilling scheduling to fill short-time jobs into job gaps at the front of the queue.Backfilling scheduling needs to obtain the running time of the job.In the past,the job running time is usually given by users and often far exceeded the actual running time of the job,which leads to inaccurate backfilling and a waste of computing resources.In particular,when the predicted job running time is lower than the actual time,the damage caused to the utilization of the system’s computing resources becomes more serious.Therefore,the prediction accuracy of the job running time is crucial to the utilization of system resources.The use of machine learning methods can make more accurate predictions of the job running time.Aiming at the parallel application of aerodynamics,we propose a job running time prediction framework SU combining supervised and unsupervised learning and verify it on the real historical data of the high-performance computing systems of China Aerodynamics Research and Development Center(CARDC).The experimental results show that SU has a high prediction accuracy(80.46%)and a low underestimation rate(24.85%).

关 键 词：High-performance computing Job scheduling Job running time prediction Machine learning Prediction accuracy Underestimation rate 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]