Job runtime prediction of HPC cluster based on PC-Transformer

Job scheduling of high performance cluster is a crucial task that affects the efficiency and performance of the system. The accuracy of job runtime prediction is one of the key factors that influences the quality of job scheduling. In this paper, we propose a novel method for job runtime prediction based on Transformer with plain connection and attention mechanism. The proposed method utilizes the job category information obtained by clustering the historical log datasets, and selects six-dimensional features that are highly correlated with job runtime. We divide the datasets into multiple job sets according to the length of job runtime, train and predict each job set separately. We evaluate the proposed method on the HPC2N dataset, and compare it with several existing methods. The results show that the proposed method achieves an average accuracy of 0.892, with 15.2% MAPE, and outperforms other methods in terms of prediction performance and training time. The proposed method can be applied to improve the efficiency and quality of job scheduling in high performance cluster.