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HPC Fastpass: Visualizing Descriptive and Predictive HPC Queue Time Data

SessionResearch, ACM SRC, and Doctoral Showcase Posters

DescriptionWith large HPC systems, users will often jockey for better queue times to get quicker results. Unfortunately, getting accurate estimations of queue times requires understanding complex and abundant data collected from myriad HPC system loggers. To aid with this, researchers are exploring machine learning to shortcut the analysis of these factors and give discrete predictions. Unfortunately, these models are imperfect, expressing varying degrees of accuracy. This imperfection must be conveyed to users in the form of uncertainty quantification. Thus, to provide users with a better understanding of queue wait times on NREL's Eagle HPC system, we developed a visualization that simplifies this complex data and aids decision making. This visualization summarizes uncertainty information associated with a user's specific queue time prediction and places it into the larger context of historical data, encoding job submission variables that users can change to show the impact of their choices on queue wait time.

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