BanditWare: A Contextual Bandit-based Framework for Hardware Prediction

Distributed computing systems are essential for meeting the demands of modern HPC applications, yet transitioning from single-system to distributed environments presents significant challenges. Resource misallocation in shared systems can lead to resource contention, system instability, degraded performance, priority inversion, inefficient utilization, increased latency, and environmental impact. We present BanditWare, an online recommendation system that dynamically selects the most suitable hardware for applications using a contextual multi-armed bandit algorithm. We evaluated BanditWare on two workflow applications: BurnPro3D (a web-based platform for fire science), and a matrix multiplication application. Designed for seamless integration with the National Data Platform (NDP), BanditWare enables users of all experience levels to optimize resource allocation efficiently.