The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Workshop: AI4S: 6th Workshop on Artificial Intelligence and Machine Learning for Scientific Applications
Authors: Aymen Alsaadi (Rutgers University); Tianle Wang (Brookhaven National Laboratory); Sumeet Atul Vadhavkar (Rochester Institute of Technology); Andrew Park (Rutgers University); Pradeep Bajracharya and Linwei Wang (Rochester Institute of Technology); Fanbo Sun (Indiana University); Sudip Seal (Oak Ridge National Laboratory (ORNL)); Vikram Jadhao (Indiana University); Geoffrey Fox (University of Virginia); and Shantenu Jha (Rutgers University, Princeton University)
Abstract: Scientific computing increasingly uses surrogate models to accelerate high-fidelity simulations, enable real-time predictions, and explore large design spaces. Building surrogates at scale is challenging: simulations are costly, data generation must be managed, and surrogate learning involves large, heterogeneous, evolving workflows. In active learning, where models guide data acquisition, these challenges intensify due to tight coupling between simulation, inference, and training. We present ROSE (RADICAL Orchestrator for Surrogate Exploration), a flexible, portable, and scalable framework supporting the full surrogate modeling lifecycle in HPC environments. ROSE integrates active learning with scalable orchestration, managing asynchronous execution across diverse resources while minimizing user effort. It supports in-situ/ex-situ workflows, online/offline training, and adaptive sampling. Applied to three use cases—electrolyte structure extraction, neutron diffraction structure recovery, and colloid phase classification—ROSE sustains high throughput with low overhead on Polaris, Perlmutter, and Delta, achieving 4–8× end-to-end speedups, with asynchronous orchestration delivering 1.5–3× gains over synchronous baselines.
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