"Temporal Causal Graph Discovery in Complex HPC Network Traffic Simulations (poster)"

Temporal Causal Graph Discovery in Complex HPC Network Traffic Simulations (poster)

Ilamathy, S., Joy, R. A., Dearing, M.T., Lan, Z.

• Publication: Chicago, IL
• Link: https://gcasr.org/2025/posters
• PDF: subha_ilamathy_causal_hpc_traffic.pdf

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?