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Large-scale multiuser scientific facilities, such as geographically distributed observatories, remote instruments, and experimental platforms, represent some of the largest national investments and can enable dramatic advances across many areas of science. Recent examples of such advances include the detection of gravitational waves and the imaging of a black hole’s event horizon. However, as the number of such facilities and their users grow, along with the complexity, diversity, and volumes of their data products, finding and accessing relevant data is becoming increasingly challenging, limiting the potential impact of facilities. These challenges are further amplified as scientists and application workflows increasingly try to integrate facilities’ data from diverse domains. In this paper, we leverage concepts underlying recommender systems, which are extremely effective in e-commerce, to address these data-discovery and data-access challenges for large-scale distributed scientific facilities. We first analyze data from facilities and identify and model user-query patterns in terms of facility location and spatial localities, domain-specific data models, and user associations. We then use this analysis to generate a knowledge graph and develop the collaborative knowledge-aware graph attention network (CKAT) recommendation model, which leverages graph neural networks (GNNs) to explicitly encode the collaborative signals through propagation and combine them with knowledge associations. Moreover, we integrate a knowledge-aware neural attention mechanism to enable the CKAT to pay more attention to key information while reducing irrelevant noise, thereby increasing the accuracy of the recommendations. We apply the proposed model on two real-world facility datasets and empirically demonstrate that the CKAT can effectively facilitate data discovery, significantly outperforming several compelling state-of-the-art baseline models. 

Computational demand has brought major changes to Advanced Cyber-Infrastructure (ACI) architectures. It is now possible to run scientific simulations faster and obtain more accurate results. However, power and energy have become critical concerns. Also, the current roadmap toward the new generation of ACI includes power budget as one of the main constraints. Current research efforts have studied power and performance tradeoffs and how to balance these (e.g., using Dynamic Voltage and Frequency Scaling (DVFS) and power capping for meeting power constraints, which can impact performance). However, applications may not tolerate degradation in performance, and other tradeoffs need to be explored to meet power budgets (e.g., involving the application in making energy-performance-quality tradeoff decisions). This paper proposes using the properties of AMR-based algorithms (e.g., dynamically adjusting the resolution of a simulation in combination with power capping techniques) to schedule or re-distribute the power budget. It specifically explores the opportunities to realize such an approach using checkpointing as a proof-of-concept use case and provides a characterization of a representative set of applications that use Adaptive Mesh Refinement (AMR) methods, including a Low- Mach-Number Combustion (LMC) application. It also explores the potential of utilizing power capping to understand power- quality tradeoffs via simulation.