Abstract
As High-Performance Computing (HPC) is moving closer to entering the exascale era, monitoring HPC systems is becoming increasingly important and continuously more complex. The innovations in interactive visualization provide tremendous assistance in presenting complex HPC data to be more comprehensible and promote the roles of humans in the analysis process. With the larger number of system components in the monitoring tasks, representing them in order requires a scalable mechanism for handling multiple visual elements and simultaneously preserving the context of ordering. In this paper, we propose a novel method for utilizing the spiral layout for order-preserving visualization in HPC monitoring, called Spiro. To demonstrate the effectiveness and usefulness of Spiro, we present the case studies of the application to a real-world temporal, multivariate HPC dataset.
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Acknowledgments
The authors acknowledge the High-Performance Computing Center (HPCC) at Texas Tech University [19] in Lubbock for providing HPC resources and data that have contributed to the research results reported within this paper. This research is partly supported by the National Science Foundation under grant CNS-1362134, OAC-1835892, and through the IUCRC-CAC (Cloud and Autonomic Computing) Dell Inc. membership contribution.
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Dang, T., Nguyen, N.V.T., Li, J., Sill, A., Chen, Y. (2023). Spiro: Order-Preserving Visualization in High Performance Computing Monitoring. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2023. Lecture Notes in Computer Science, vol 14361. Springer, Cham. https://doi.org/10.1007/978-3-031-47969-4_9
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