Paper Info
Title
Automated Analysis of Time Series Data to Understand Parallel Program Behaviors.
Abstract
Traditionally, performance analysis tools have focused on collecting measurements, attributing them to program source code, and presenting them; responsibility for analysis and interpretation of measurement data falls to application developers. While profiles of parallel programs can identify the presence of performance problems, often developers need to analyze execution behavior over time to understand how and why parallel inefficiencies arise. With the growing scale of supercomputers, such manual analysis is becoming increasingly difficult. In many cases, performance problems of interest only appear at larger scales. Manual analysis of time series data from executions on extreme-scale parallel systems is daunting as the volume of data across processors and time makes it difficult to assimilate. To address this problem, we have developed an automated analysis framework that generates compact summaries of time series data for parallel program executions. These summaries provide users with high-level insight into patterns in the performance data and can quickly direct a user's attention to potential performance bottlenecks. We demonstrate the effectiveness of our framework by applying it to time-series measurements of two scientific codes.
Year
DOI
Venue
2018
10.1145/3205289.3205308
ICS
Keywords
Field
DocType
Automated performance analysis, iteration identification, clustering, performance loss attribution
Analysis tools,Time series,Software engineering,Source code,Computer science,Real-time computing,Cluster analysis
Conference
ISBN
Citations 
PageRank 
978-1-4503-5783-8
0
0.34
References 
Authors
11
2
Name
Order
Citations
PageRank
Lai Wei184.98
John Mellor-Crummey286876.69