Material Calculation Time Prediction Model Based on Gradient Boosting Decision Trees
Prediction of material calculation run time plays a crucial role in improving job scheduling efficiency and new material research and development.Traditional cluster job run time prediction models have poor accuracy and low availability in the field.To this end,a job predic-tion model based on gradient boosting decision tree is proposed,which combines domain knowledge and relevant literature to clean VASP job log data,evaluates the importance of selected features,and then conducts experiments under different data sizes and sample distribution con-ditions.The model is compared with models using traditional machine learning methods.The experiment shows that the average absolute per-centage error of the proposed method is lower than that of traditional machine learning methods under different conditions,and the prediction error of comprehensive job running time is 4.28％,which is better than the RunningNet method's 10.3％.This proves that the proposed model has higher accuracy in predicting material calculation running time,and has a better effect on improving job scheduling efficiency and acceler-ating new material research and development.
material calculationjob run time forecastingdecision treeVASP jobjob scheduling
万方数据
维普
