BanditWare uses a decaying epsilon-greedy contextual bandit with linear runtime models to recommend hardware for scientific workflows, learning online with far fewer samples than offline ML approaches.
Towards an Integrated Performance Framework for Fire Science and Management Workflows
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Reliable performance metrics are necessary prerequisites to building large-scale end-to-end integrated workflows for collaborative scientific research, particularly within context of use-inspired decision making platforms with many concurrent users and when computing real-time and urgent results using large data. This work is a building block for the National Data Platform, which leverages multiple use-cases including the WIFIRE Data and Model Commons for wildfire behavior modeling and the EarthScope Consortium for collaborative geophysical research. This paper presents an artificial intelligence and machine learning (AI/ML) approach to performance assessment and optimization of scientific workflows. An associated early AI/ML framework spanning performance data collection, prediction and optimization is applied to wildfire science applications within the WIFIRE BurnPro3D (BP3D) platform for proactive fire management and mitigation.
fields
cs.DC 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
BanditWare: A Contextual Bandit-based Framework for Hardware Prediction
BanditWare uses a decaying epsilon-greedy contextual bandit with linear runtime models to recommend hardware for scientific workflows, learning online with far fewer samples than offline ML approaches.