A Visual Analytics Framework for Analyzing Parallel and Distributed Computing Applications

Li, Jianping Kelvin; Fujiwara, Takanori; Kesavan, Suraj P.; Ross, Caitlin; Mubarak, Misbah; Carothers, Christopher D.; Ross, Robert; Ma, Kwan‐Liu

doi:10.1109/vds48975.2019.8973380

Cited by 9 publications

(3 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, a drawback is that this approach might be perceptually daunting for inexperienced users of VA tools. Interactive tools with multiple views provide better readability and higher chances of detecting patterns (Nguyen et al 2020;Li et al 2019;Pham et al 2019;Stopar et al 2019;Dang et al 2020;Schlegel et al 2020). Furthermore, users are able to explore the data in an agile manner when the data are visualized from both global and local perspectives (Bernard et al 2019;Chen et al 2020;Guo et al 2020).…”

Section: Related Workmentioning

confidence: 99%

Visual analysis of blow molding machine multivariate time series data

Musleh

Chatzimparmpas

Jusufi

2022

J Vis

View full text Add to dashboard Cite

The recent development in the data analytics field provides a boost in production for modern industries. Small-sized factories intend to take full advantage of the data collected by sensors used in their machinery. The ultimate goal is to minimize cost and maximize quality, resulting in an increase in profit. In collaboration with domain experts, we implemented a data visualization tool to enable decision-makers in a plastic factory to improve their production process. The tool is an interactive dashboard with multiple coordinated views supporting the exploration from both local and global perspectives. In summary, we investigate three different aspects: methods for preprocessing multivariate time series data, clustering approaches for the already refined data, and visualization techniques that aid domain experts in gaining insights into the different stages of the production process. Here we present our ongoing results grounded in a human-centered development process. We adopt a formative evaluation approach to continuously upgrade our dashboard design that eventually meets partners’ requirements and follows the best practices within the field. We also conducted a case study with a domain expert to validate the potential application of the tool in the real-life context. Finally, we assessed the usability and usefulness of the tool with a two-layer summative evaluation that showed encouraging results. Graphical Abstract

show abstract

Section: Related Workmentioning

confidence: 99%

Visual analysis of blow molding machine multivariate time series data

Musleh

Chatzimparmpas

Jusufi

2022

J Vis

View full text Add to dashboard Cite

show abstract

“…Several approaches eschew the Gantt chart and plot events or metrics about resources in the same space [12,22,24,26,27]. We also aggregate metric data by resource in our metric views.…”

Section: Related Workmentioning

confidence: 99%

Traveler: Navigating Task Parallel Traces for Performance Analysis

Sakin¹,

Bigelow²,

Tohid³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…These lines show an overview of the behavioral similarity of multivariate time-varying performance data. To analyze the performance behaviors from the large-scale data, Fujiwara et al [16,28] integrated advanced time-series analysis methods, including clustering, dimensionality reduction, and change point detection methods, into their visual analytics system. In terms of coupling the advanced time-series analysis methods with visualizations, [16] is the most closely related work.…”

Section: Performance Visualization Of Parallel Applicationsmentioning

confidence: 99%

A Visual Analytics Framework for Reviewing Streaming Performance Data

Kesavan

Fujiwara

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Understanding and tuning the performance of extreme-scale parallel computing systems demands a streaming approach due to the computational cost of applying offline algorithms to vast amounts of performance log data. Analyzing large streaming data is challenging because the rate of receiving data and limited time to comprehend data make it difficult for the analysts to sufficiently examine the data without missing important changes or patterns. To support streaming data analysis, we introduce a visual analytic framework comprising of three modules: data management, analysis, and interactive visualization. The data management module collects various computing and communication performance metrics from the monitored system using streaming data processing techniques and feeds the data to the other two modules. The analysis module automatically identifies important changes and patterns at the required latency. In particular, we introduce a set of online and progressive analysis methods for not only controlling the computational costs but also helping analysts better follow the critical aspects of the analysis results. Finally, the interactive visualization module provides the analysts with a coherent view of the changes and patterns in the continuously captured performance data. Through a multi-faceted case study on performance analysis of parallel discrete-event simulation, we demonstrate the effectiveness of our framework for identifying bottlenecks and locating outliers.

show abstract

A Visual Analytics Framework for Analyzing Parallel and Distributed Computing Applications

Cited by 9 publications

References 31 publications

Visual analysis of blow molding machine multivariate time series data

Visual analysis of blow molding machine multivariate time series data

Traveler: Navigating Task Parallel Traces for Performance Analysis

A Visual Analytics Framework for Reviewing Streaming Performance Data

Contact Info

Product

Resources

About