Speaker
Description
The ATLAS experiment at the CERN Large Hadron Collider relies on a worldwide distributed computing infrastructure to process millions of production and analysis jobs daily across grid, cloud, and HPC resources. The ATLAS Distributed Computing (ADC) system integrates workload, data, and resource management services to ensure efficient use of heterogeneous environments. Within ADC, the PanDA Workload Management System (WMS) provides large-scale job brokerage, pilot submission, and monitoring. Recent development extends PanDA to address sustainability, hardware diversity, and intelligent automation. A new module estimates per-job CO₂-equivalent emissions, combining runtime metadata with regional carbon-intensity data. The resulting gCO₂ values are stored in the PanDA database and visualized through monitoring dashboards to raise awareness of computing-related emissions. The PanDA brokerage has been extended to support GPU-based scheduling, using a redesigned JSON resource description that encodes CUDA version, GPU model, memory, and benchmark data to enable precise resource matching. The Worker Node Map collects detailed CPU and GPU specifications reported by pilots and correlates them with HEPiX benchmark results to improve CPU-time normalization and provide operational insight into site homogeneity and hardware aging. Ask PanDA introduces an AI-driven assistant that orchestrates multiple specialized clients in a coordinated workflow and employs retrieval-augmented generation to provide contextual answers on PanDA operations. These developments represent a significant step toward a more transparent, efficient, and sustainable computing ecosystem for ATLAS and future large-scale scientific workflows.