ORA: Job Runtime Prediction for High-Performance Computing Platforms Using the Online Retrieval-Augmented Language Model
Hongyi Liu, Yinping Ma, Xiaosong Huang, Lingzhe Zhang, Tong Jia, Ying Li · 2025
Accurate job runtime prediction is critical for efficient scheduling in high-performance computing (HPC) platforms.For instance, precise predictions enable techniques such as backfilling, where small jobs are executed ahead of schedule to maximize resource utilization and enhance computational efficiency.However, existing runtime prediction methods primarily rely on job metadata (e.g., submission time, requested runtime, and required memory) while ignoring the content of job scripts, which limits their accuracy.To address this issue, we propose an Online Retrieval-Augmented Language Model (ORA) for job runtime prediction.ORA encodes both metadata and script information from historical jobs into feature vectors to form a database, enabling similarity-based retrieval to assist in predicting the runtime of new jobs.To address distribution shifts, ORA incrementally updates the database without requiring model retraining.Additionally, personalized retrieval mechanisms are employed to mitigate the * Co-corresponding author.