Abstract
Modern HPC workloads involve large data transfers that can become bottlenecks. Existing analysis tools identify bottle- necks from per-file performance data but have limitations in parallelizability and rigid heuristic-based rules, necessitating an automated, efficient, and multi-perspective solution. We designed an automated tool, WisIO, that enables parallel and distributed analysis of multi-terabyte-scale workflow performance data. WisIO examines performance data from multiple perspectives, uses metric-driven bottleneck classifi- cation, and allows extensible mapping of bottlenecks to root causes. Experimental results demonstrate that WisIO's multi- perspective views substantially improve bottleneck coverage, showing an average increase of up to 805× when compared to analyzing performance data from a single perspective. In our performance evaluation, WisIO's metric-driven classification processed 340K bottlenecks per second, while its reasoning engine handled around 35K bottlenecks per sec- ond. In an analysis of five real-world HPC workloads, WisIO demonstrated up to 11× faster analysis time and identified up to 144x more bottlenecks compared to existing solutions.