Abstract: In the field of high-performance computing(HPC),machine learning driven scheduling algorithms are increasingly becoming a research focus,with their performance optimization highly dependent on the quality of workload data.Therefore,research on workload analysis and modeling methods for HPC environments is of significant importance for improving the efficiency and adaptability of scheduling algorithms.This study conducts an in-depth analysis of HPC workloads based on publicly available operational logs from supercomputing centers,establishing a comprehensive methodology for workload analysis and modeling.The DBSCAN algorithm is first employed to clean anomalous data,followed by a systematic analysis of job arrival patterns,processor core distribution characteristics,application distribution features,runtime distribution attributes,and correlations among different parameters.Based on these findings,a flexible HPC workload generator is developed,supporting both default parameter configurations and automated analysis of SWF-format workload characteristics to meet diverse simulation needs.Experimental validation demonstrates that the synthetic data generated by this tool aligns more closely with real-world workload distributions compared to randomly generated data.The proposed generator can provide high-quality training data for the development of novel scheduling algorithms,contributing to improved resource utilization efficiency in supercomputing centers.

Key words: High performance computing, Workload, Data analysis, Data modeling, Workload generator