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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 sampling 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. Index Terms—AI4CI, Data Science Workflow, Custom ML Models, HPC, Data Generation, Scheduling, Resource Estimations
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This content will become publicly available on December 17, 2026
Grey-Box Machine Learning Prediction of Parallel Application Scaling
Accurate prediction of parallel application performance in HPC systems is essential for efficient resource allocation and system design. Classical performance models estimate of speedup based on theoretical assumptions, but their applicability is limited by parameter estimation, data acquisition, and real-world system issues such as latency and network congestion. This paper describes performance prediction using classical performance models boosted by a trainable machine learning framework. Domain-informed machine-learning models estimate the overhead of an application for a given problem size and resource configuration as a coefficient of the estimated speedup provided by performance laws. We evaluate this approach on two HPC mini-applications and two full applications with varying patterns of computation and communication and also evaluate the prediction accuracy on runs with varying processors-per-node configurations. Our results show that this method significantly improves the accuracy of performance predictions over standard analytical models and black-box regressors, while remaining robust even with limited training data.
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- Award ID(s):
- 2103510
- PAR ID:
- 10644609
- Publisher / Repository:
- Proceedings of the 32st IEEE International Conference on High Performance Computing, Data, and Analytics
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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