Interactive Interface for StarVZ Plot Visualization
Abstract
This work introduces an interactive interface developed in R using the Shiny framework for visualizing plots generated by StarVZ. Visualization of execution traces is a key tool for performance analysis of parallel applications, helping to identify inefficiencies and optimize resource utilization. The interface allows users to load data, configure different visualization parameters, and switch between ggplot2 and plotly-generated graphs. The goal is to provide a more intuitive and flexible experience for execution trace analysis, making it easier to extract insights from processed data.
Keywords:
Evaluation, Measurement, and Performance Prediction, Languages, Compilers, and Tools for High Performance, Basic Software for Parallel and Distributed Computing
References
Attali, D. (2021). shinyjs: Easily Improve the User Experience of Your Shiny Apps in Seconds. R package version 2.1.0.
Chang, W., Cheng, J., Allaire, J., Sievert, C., Schloerke, B., Xie, Y., Allen, J., McPherson, J., Dipert, A., and Borges, B. (2024). shiny: Web Application Framework for R. R package version 1.9.1.
Lucas Mello Schnorr, Vinicius Garcia Pinto, Lucas Leandro Nesi, and Marcelo Cogo Miletto (2022). starvz: R-Based Visualization Techniques for Task-Based Applications. R package version 0.7.1.
Müller, K. and Wickham, H. (2023). tibble: Simple Data Frames. R package version 3.2.1.
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Pedersen, T. L., Nijs, V., Schaffner, T., and Nantz, E. (2022). shinyFiles: A Server-Side File System Viewer for Shiny. R package version 0.9.3.
Pinto, V. G., Leandro Nesi, L., Miletto, M. C., and Mello Schnorr, L. (2021). Providing in-depth performance analysis for heterogeneous task-based applications with starvz. In 2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pages 16–25.
Sievert, C. (2020). Interactive Web-Based Data Visualization with R, plotly, and shiny. Chapman and Hall/CRC.
ViTE Team (2024). ViTE: Trace Explorer. ViTE is a trace explorer. It is a tool to visualize execution traces in Pajé or OTF format for debugging and profiling parallel or distributed applications. It is an open source software licenced under CeCILL-A.
Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York.
Chang, W., Cheng, J., Allaire, J., Sievert, C., Schloerke, B., Xie, Y., Allen, J., McPherson, J., Dipert, A., and Borges, B. (2024). shiny: Web Application Framework for R. R package version 1.9.1.
Lucas Mello Schnorr, Vinicius Garcia Pinto, Lucas Leandro Nesi, and Marcelo Cogo Miletto (2022). starvz: R-Based Visualization Techniques for Task-Based Applications. R package version 0.7.1.
Müller, K. and Wickham, H. (2023). tibble: Simple Data Frames. R package version 3.2.1.
Ordronneau, C. (2024). Développement et maintenance du logiciel vite.
Pedersen, T. L., Nijs, V., Schaffner, T., and Nantz, E. (2022). shinyFiles: A Server-Side File System Viewer for Shiny. R package version 0.9.3.
Pinto, V. G., Leandro Nesi, L., Miletto, M. C., and Mello Schnorr, L. (2021). Providing in-depth performance analysis for heterogeneous task-based applications with starvz. In 2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pages 16–25.
Sievert, C. (2020). Interactive Web-Based Data Visualization with R, plotly, and shiny. Chapman and Hall/CRC.
ViTE Team (2024). ViTE: Trace Explorer. ViTE is a trace explorer. It is a tool to visualize execution traces in Pajé or OTF format for debugging and profiling parallel or distributed applications. It is an open source software licenced under CeCILL-A.
Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York.
Published
2025-04-23
How to Cite
SOUSA FILHO, Christian Einhardt; PINTO, Vinícius Garcia.
Interactive Interface for StarVZ Plot Visualization. In: REGIONAL SCHOOL OF HIGH PERFORMANCE COMPUTING FROM SOUTHERN BRAZIL (ERAD-RS), 25. , 2025, Foz do Iguaçu/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 85-88.
ISSN 2595-4164.
DOI: https://doi.org/10.5753/eradrs.2025.6805.
