Abstract: In the field of high-performance computing(HPC),supercomputing platforms leverage large-scale computational resource pools to enable parallel processing of multiple tasks,significantly accelerating job execution.Their resource scheduling mechanisms require users to explicitly specify requested resources and their quantities when submitting jobs.However,current practice still heavily relies on manual experience-based parallel parameter configuration,often resulting in hardware resource utilization failing to approach theoretical optimal values,creating significant resource efficiency bottlenecks.For structurally complex HPC software,individual input parameters are tightly coupled with actual hardware resources;adjusting isolated parameters alone makes it difficult to construct a concise yet effective framework for thorough exploration,while exhaustive traversal of the entire parameter search space incurs prohibitively expensive configuration costs.Machine learning(ML)-based runtime optimization methods for parallel computing applications employ multiple efficient models to analyze and process extensive execution data,providing superior parameter configuration recommendations.These methods autonomously determine hardware parameter configurations for job requests without requiring users to specify resource amounts,thereby lowering the barrier to cluster software usage and improving work efficiency.This effectively addresses the problems of suboptimal parallel parameter configuration efficiency for supercomputing cluster users and the lack of high-quality job execution datasets for specific tasks in existing technologies.Experimental results demonstrate that compared to default or commonly used parameter configurations,ML-derived parameter configurations achieve consistent speedup and significantly reduced core-hour consumption across multiple typical VASP instances,exhibiting substantial practical value for widespread adoption.

Key words: Parallel optimization, High performance computing, Machine learning, Parameter search, VASP