Abstract: In the commonly used first-principles methods,density functional theory(DFT) has the characteristics of low scale and high accuracy,so it has been more and more widely used in the fields of chemistry,biology,medicine and so on.However,in practical applications,its relatively high computational cost has posed new challenges to the decision-making on calculation parameters for users and the assignment of tasks for the computing centers.We have recently developed a time prediction system for DFT calculations based on machine learning technique,which can predict the actual computational cost before calculations.The mean relative errors are normally less than 0.15,so that it meets the prediction accuracy requirements in actual scenarios.In this work,we further promote and improve the prediction system,providing multi-GPU parallel computing functions and modular additions to the machine learning models;combined it with the biomed community to realize real-time display of the computing tasks submitted to the platform,which will be convenient for users to coordinate;an intelligent load balancing module is developed,which can improve the efficiency of first-principles calculations for the super-large molecules and cluster systems.These efforts improve the practicalities of the forecasting system,and the preliminary applications are reported in both the community platform and parallel computing.

Key words: Density functional theory, High-performance computing, Community services, Machine learning, Load balancing