Scalable Event Trace Visualization

Parallel event trace visualizations can aid in discovery of the root causes of certain performance problems on high-end systems. However, traditional trace visualizations are not inherently scalable and require considerable effort on the part of the user to identify similarities and differences in performance across parallel entities. In this work, we evaluate several methods for deciding when traces of different processes in a run are similar enough that only one of the traces needs to be retained and rendered in the visualization. We show visualizations of reduced traces and evaluate them for compression, error, and retention of correct diagnostic information.

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