An Online Drift-Adaptive Framework for Semantic Representation and Classification of HPC Jobs

Abstract

With the rapid advancement of computing technologies, high-performance computing (HPC) systems have continued to expand in scale and have been widely applied across various domains. To improve resource utilization, existing studies commonly employ offline-trained models based on historical resource usage data to classify HPC jobs as compute-bound or memory-bound. However, such methods typically rely on static historical data and struggle to adapt to dynamically changing workloads over time, resulting in degraded prediction performance and reduced stability under concept drift. To address this issue, this paper proposes an online drift-adaptive framework for HPC job semantic representation and classification (DA-HJSC). The framework employs a hybrid mechanism that combines offline semantic learning with online dynamic adaptation. Specifically, an offline classification model is first trained using semantic representations of job descriptions together with user historical behavioral features (UHBF). During the inference stage, an exponentially weighted moving average (EWMA) mechanism is further introduced to dynamically fuse the output probabilities of the classification model, thereby producing the final classification results for HPC jobs. Experimental results on the publicly available F-DATA dataset covering the period from 1 May 2023 to 30 April 2024 demonstrate that DA-HJSC consistently improves HPC job classification performance across different pre-trained language models and base classifiers, achieving a maximum F1macro (overall) score of 0.9272. Compared with the corresponding baselines, DA-HJSC reduces the number of low- and medium-performance days by up to 11 and 35 days, respectively, across all experimental configurations. Overall, the DA-HJSC framework achieves an effective balance among classification accuracy, performance stability, and online adaptability, providing a solution with practical deployment potential for dynamic HPC workload classification.