Establishing a Generalizable Framework for Generating Cost-Aware Training Data and Building Unique Context-Aware Walltime Prediction Regression Models

This paper describes a generalizable framework for creating context-aware wall-time prediction models for HPC applications. This framework: (a) cost-effectively generates comprehensive application-specific training data, (b) provides an application-independent machine learning pipeline that trains different regression models over the training datasets, and (c) establishes context-aware selection criteria for model selection. We explain how most of the training data can be generated on commodity or contention-free cyberinfrastructure and how the predictive models can be scaled to the production environment with the help of a limited number of resource-intensive generated runs (we show almost seven-fold cost reductions along with better performance). Our machine learning pipeline does feature transformation, and dimensionality reduction, then reduces sam-pling bias induced by data imbalance. Our context-aware model selection algorithm chooses the most appropriate regression model for a given target application that reduces the number of underpredictions while minimizing overestimation errors.