3 research outputs found
A Visual Analytics Framework for Reviewing Streaming Performance Data
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.Comment: This is the author's preprint version that will be published in
Proceedings of IEEE Pacific Visualization Symposium, 202
A Visual Analytics Approach for Hardware System Monitoring withStreaming Functional Data Analysis
Many real-world applications involve analyzing time-dependent phenomena,
which are intrinsically functional, consisting of curves varying over a
continuum (e.g., time). When analyzing continuous data, functional data
analysis (FDA) provides substantial benefits, such as the ability to study the
derivatives and to restrict the ordering of data. However, continuous data
inherently has infinite dimensions, and for a long time series, FDA methods
often suffer from high computational costs. The analysis problem becomes even
more challenging when we must update the FDA results for continuously arriving
data. In this paper, we present a visual analytics approach for monitoring and
reviewing time series data streamed from a hardware system with a focus on
identifying outliers by using FDA. To perform FDA while addressing the
computational problem, we introduce new incremental and progressive algorithms
that promptly generate the magnitude-shape (MS) plot, which conveys both the
functional magnitude and shape outlyingness of time series data. In addition,
by using an MS plot in conjunction with an FDA version of principal component
analysis, we enhance the analyst's ability to investigate the
visually-identified outliers. We illustrate the effectiveness of our approach
with two use scenarios using real-world datasets. The resulting tool is
evaluated by industry experts using real-world streaming datasets.Comment: 10 pages, 10 figure