JobViewer: Graph-based Visualization for Monitoring High-Performance Computing System


Visualization aims to strengthen data exploration and analysis, especially for complex and high-dimensional data. High-performance computing (HPC) systems are typically large and complicated instruments that generate massive performance and operation time series. Monitoring HPC systems’ performance is a daunting task for HPC admins and researchers due to their dynamic natures. This work proposes a visual design using the bipartite graph’s idea to visualize HPC clusters’ structure, metrics, and job scheduling data. We built a web-based prototype, called JobViewer, that integrates advanced methods in visualization and human-computer interaction (HCI) to demonstrate the benefits of visualization in real-time monitoring HPC centers. We also showed real use cases and a user study to validate the efficiency and highlight the current approach’s drawbacks.

In IEEE/ACM International Conference on Big Data Computing, Applications and Technologies
Jie Li
Jie Li
Ph.D. candidate in Computer Science

My research interests include High-Performance Computing, Advanced Computer Architecture, and Parallel and Distributed Computing.