Machine learning-based performance models are increasingly being used to
build critical job scheduling and application optimization decisions.
Traditionally, these models assume that data distribution does not change as
more samples are collected over time. However, owing to the complexity and
heterogeneity of production HPC systems, they are susceptible to hardware
degradation, replacement, and/or software patches, which can lead to drift in
the data distribution that can adversely affect the performance models. To this
end, we develop continually learning performance models that account for the
distribution drift, alleviate catastrophic forgetting, and improve
generalizability. Our best model was able to retain accuracy, regardless of
having to learn the new distribution of data inflicted by system changes, while
demonstrating a 2x improvement in the prediction accuracy of the whole data
sequence in comparison to the naive approach.

基于机器学习的性能模型在构建关键的作业调度和应用程序优化决策中越来越被使用。我们开发了持续学习性能模型，考虑到数据分布漂移，减轻灾难性遗忘，并提高泛化能力。我们的最佳模型能够保持准确性，无论系统变化引起的新数据分布，同时相比于朴素方法，在整个数据序列的预测准确度上提升了 2 倍。