Skip to content

Latest commit

 

History

History
31 lines (18 loc) · 1.24 KB

File metadata and controls

31 lines (18 loc) · 1.24 KB

Surrogate Model for Predicting Application Runtime

This repository contains the code and data processing scripts used to train SMART, a surrogate model for predicting application runtime on large-scale Dragonfly systems.

Dataset

We use two datasets, D1 and D2, which are publicly available at:

Files

  • generate_graph_data.ipynb:
    Processes the raw data to generate feature tensors X, labels Y, and the adjacency matrix used for modeling.

  • smart.ipynb:
    Loads the preprocessed data and trains the surrogate model for runtime prediction.

Related Papers

  • SMART: A Surrogate Model for Predicting Application Runtime in Dragonfly Systems
    X. Wang, P. L. Rizzini, S. Medya and Z. Lan, "SMART: A Surrogate Model for Predicting Application Runtime in Dragonfly Systems", In Proceedings of AAAI 2026.

  • Extended Version
    The extended version includes additional experiments, ablation studies, hyperparameter variations, temporal model comparisons, and detailed supplementary analysis that go beyond the main AAAI 2026 paper.
    arXiv Link

Citation

If you use this code or dataset, please cite the AAAI paper above.