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Visualizing a Moving Target: A Design Study on Task Parallel Programs in the Presence of Evolving Data and Concerns.

Katy Williams, Alex Bigelow, Kate Isaacs

IEEE Transactions on Visualization and Computer Graphics

|August 20, 2019

Summary

Visualization can refine data collection in task parallel computing, even with changing needs. We developed Atria, an execution graph visualizer, to address these challenges and improve performance analysis.

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Area of Science:

Computer Science
Data Visualization
High-Performance Computing

Background:

Visualization projects often face challenges with data availability and evolving user needs.
Task parallel computing presents unique difficulties due to the dynamic nature of data and analysis tools.

Purpose of the Study:

To investigate the benefits of engaging in visualization projects despite data and user requirement volatility.
To develop and evaluate Atria, a multi-view execution graph visualization tool for performance analysis in task parallel computing.

Main Methods:

Employed a tightly-coupled iterative design process.
Developed Atria with a node aggregation strategy based on lines of code.
Adapted design study methodology to accommodate a "moving target" of data and domain expert concerns.

Main Results:

Successfully built and deployed Atria on multiple platforms, with necessary design adaptations.
Demonstrated that the visualization process can help refine data collection and address "chicken and egg" problems.
Showcased how adapting to changing data and user needs maintained project health.

Conclusions:

Engaging in visualization projects with evolving data and user needs can be beneficial.
Iterative design and adaptive methodologies are crucial for success in dynamic research domains.
Atria provides a simplified representation of execution graphs, aiding performance analysis in task parallel computing.