Multiple Datasets Performance Visualization-Final GSoC Report

Monalisha Ojha
4 min readSep 12, 2022

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This summer, I worked as a Google Summer of Code mentee in STE||AR Group on “Upgrading Multiple Datasets Performance Visualization feature in Traveler” under the mentorship of Kate Isaacs. This blog summarizes my work on the Traveler Platform during Google Summer of Code 2022 program.

About Traveler

Traveler-Integrated is a web-based visualization system for parallel performance data, such as OTF2 traces and HPX execution trees. HPX traces are collected with APEX and written as OTF2 files with extensions. It is developed by the HDC Lab (Humans, Data and Computers Lab) at the University of Arizona. The major goal of this platform is to provide meaningful insights into parallel performance data in the form of Gantt charts (trace data timelines with dependencies), source code, expression tree, aggregated time series line charts for counter data, utilization chart and task level histograms.

Abstract

The aim of this project, “Multiple Datasets Performance Visualization,’’ is to add specific features in the platform that will help in managing multiple data files and organizing traveler interface windows to handle the comparison of data. Organizing multiple datasets in the platform, implementing a highlighted linking system for multiple datasets and organizing datasets efficiently for visualization are some of the major sub-goals.

Project

Updated the Tagging system of Traveler Interface to accommodate multiple datasets

Issue : Organizing the datasets according to their assigned tags.

Made changes in the interface main menu to display the datasets according to their tags names. Tested the tagging system back-end to accommodate multiple datasets. The screenshot displays the fixes made when tested with 2 datasets.

Issue Link: https://github.com/hdc-arizona/traveler-integrated/issues/90

Pull Request: https://github.com/hdc-arizona/traveler-integrated/pull/91

Added Dataset Color Picker option in Interface

Issue: Adding a color picker system to distinguish between multiple datasets.

“Change Datasets color” option is added to datasets context menu. With this feature, a user can change the datasets selection color and main menu color to be distinguishable from other datasets. The screenshots of changes done till now are displayed below:

Pull request link: https://github.com/hdc-arizona/traveler-integrated/pull/94

Added “color” parameter in the bundle.py

Issue: Adding color parameter in bundle.py to enter the color of the dataset.

Bundle.py is used by traveler users to enter the dataset in the traveler interface. I have added a “ — — color “ option to enable the users to choose the dataset color while entering the dataset before starting the interface.

Pull request link: https://github.com/hdc-arizona/traveler-integrated/pull/94

Fixed glitches related Traveler front-end

Issue: Displaying a clear relationship between a folder and its datasets.

Made changes in the front-end to make the lines visible that shows the connection between folder and its datasets. Adjusted the tag header to solve the tag overlapping issue for multiple datasets. The screenshot of the changes are shown below.

Issue link: https://github.com/hdc-arizona/traveler-integrated/issues/92

Pull request link: https://github.com/hdc-arizona/traveler-integrated/pull/93

Work in Progress

The dynamic color linking system needs to be completed. The backend part needs to be completed and merged. A side by side window needs to be formed in the traveler interface to compare two datasets at the same time.

Conclusion

The journey from working on GSoC application to preparing this report was challenging yet fruitful. The major advantage of open source is to learn and work simultaneously. I got to learn many new concepts during GSoC. Interacting with mentor, discussing new ideas and turning those ideas into working code was a great learning experience for me. The success of this project is dedicated to the mentor Kate Isaacs. She helped me out whenever i had an issue. I am grateful to Ste||ar group and GSoC team for making this experience a memorable one.

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