Abstract
Performance analysis is an integral part of developing and optimizing parallel applications for high performance computing (HPC) platforms. Hierarchical data from different sources is typically available to identify performance issues or anomalies. Some hierarchical data such as the calling context can be very large in terms of breadth and depth of the hierarchy. Classic tree visualizations quickly reach their limits in analyzing such hierarchies with the abundance of information to display. In this position paper, we identify the challenges commonly faced by the HPC community in visualizing hierarchical performance data, with a focus on calling context trees. Furthermore, we motivate and lay out the bases of a visualization that addresses some of these challenges.
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Acknowledgment
The ideas presented in this paper originated during the GI-Dagstuhl Seminar 18283, sponsored by the Gesellschaft für Informatik e.V. (GI), where all the authors on this paper were participants. The first author would like to thank LAM Research for its financial support.
This work was partially funded by the Deutsche Forschungsgemeinschaft (DFG) in context of SFB 716, project D.3, as well as the Priority Programme “DFG-SPP 1593: Design For Future—Managed Software Evolution” (HO 5721/1-1), and by the Excellence Initiative of the German federal and state governments. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 (LLNL-CONF-756548).
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Bergel, A. et al. (2019). Visual Analytics Challenges in Analyzing Calling Context Trees. In: Bhatele, A., Boehme, D., Levine, J., Malony, A., Schulz, M. (eds) Programming and Performance Visualization Tools. ESPT ESPT VPA VPA 2017 2018 2017 2018. Lecture Notes in Computer Science(), vol 11027. Springer, Cham. https://doi.org/10.1007/978-3-030-17872-7_14
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DOI: https://doi.org/10.1007/978-3-030-17872-7_14
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