Temporal Causal Graph Discovery in Complex HPC Network Traffic Simulations (poster)
May 8th, 2025
Categories: Applications, Supercomputing, Data Science, High Performance Computing
Authors
Ilamathy, S., Joy, R. A., Dearing, M.T., Lan, Z.About
Motivation:
• Parallel Discrete Event Simulations (PDES) offers accurate HPC simulations but is computationally intensive and slow to scale.
• Surrogate models can accelerate simulations, and we explore if causal insights can improve their long-term forecasting stability.
Research Questions:
• Can Causal Signals hidden in HPC simulations unlock better forecasting?
• Can different Causal Discovery methods identify key drivers for accurate surrogate forecasting?
Resources
URL
Citation
Ilamathy, S., Joy, R. A., Dearing, M.T., Lan, Z., Temporal Causal Graph Discovery in Complex HPC Network Traffic Simulations (poster), The 12th Greater Chicago Area Systems Research Workshop (GCASR), Chicago, IL, May 8th, 2025. https://gcasr.org/2025/posters