Improving Performance of HPC Systems Based on Robust Survival Machine Learning Methods

Abstract

High-performance computing (HPC) systems face growing complexity and uncertainty that limit the accuracy of classical methods for predicting computational processes and task execution. To address the problem of tasks failing to complete within allotted time windows, termed "censored" events, survival machine learning methods are proposed for predicting execution time and minimizing such losses. Survival analysis plays an important role for solving the task of time-to-event prediction. While traditional methods handle censored data effectively, they often rely on strong parametric assumptions that may limit their exibility. This paper introduces a novel survival analysis framework, called CiSurv (Contaminated imprecise Survival model), that integrates imprecise probability theory with attention-based multi-label classification. By reformulating survival prediction as an imprecise classification problem, CiSurv provides an approach to modeling uncertainty in censored observations while improving predictive accuracy. The proposed framework incorporates the imprecise contaminated model to refine interval-valued probabilities associated with censored data. Two models are developed: CiSurvN, which uses the neural network-based attention mechanism for solving the classification task, and CiSurvG, which employs Gaussian kernel-based attention with a single kernel parameter. Experiments on real and synthetic data demonstrate that CiSurvN generally outperforms CiSurvG due to its ability to learn complex feature dependencies. Key findings reveal that intermediate contamination parameter values yield optimal performance, outperforming a special case when the parameter is 1 in most cases. Codes implementing the proposed models are publicly available.