HPC System Data Pipeline to Enable Meaningful Insights through Analysis-Driven Visualizations

The increasing complexity of High Performance Computing (HPC) systems has created a growing need for facilitating insight into system performance and utilization for administrators and users. The strides made in HPC system monitoring data collection have produced terabyte/day sized time-series data sets rich with critical information, but it is onerous to extract and construe meaningful information from these metrics. We have designed and developed an architecture that enables flexible, as-needed, run-time analysis and presentation capabilities for HPC monitoring data. Our architecture enables quick and efficient data filtration and analysis. Complex runtime or historical analyses can be expressed as Python-based computations. Results of analyses and a variety of HPC oriented summaries are displayed in a Grafana front-end interface. To demonstrate our architecture, we have deployed it in production for a 1500-node HPC system and have developed analyses and visualizations requested by system administrators, and later employed by users, to track key metrics about the cluster at a job, user, and system level. Our architecture is generic, applicable to any *-nix based system, and it is extensible to supporting multi-cluster HPC centers. We structure it with easily replaced modules that allow unique customization across clusters and centers. In this paper, we describe the data collection and storage infrastructure, the application created to query and analyze data from a custom database, and the visual displays created to provide clear insights into HPC system behavior.