@inproceedings{2daa4c65c960412bb4e3d58368bc3946,
title = "Structural Clustering: A New Approach to Support Performance Analysis at Scale",
abstract = "The increasing complexity of high performance computing systems creates high demands on performance tools and human analysts due to an unmanageable volume of data gathered for performance analysis. A promising approach for reducing data volume is classification of data from multiple processes into groups of similar behavior to aid in analyzing application performance and identifying hot spots. However, existing approaches for structural and temporal classification of performance data suffer from lack of scalability or produce misleading results. To address this problem, we present a novel and effective structural similarity measure to efficiently classify data from parallel processes and introduce a method for efficient storage of the classified data. Using four examples, we show how existing performance analysis techniques benefit from our structural classification. Finally, we present a case study with 15 applications on up to 65,536 parallel processes that demonstrates the generality and scalability of our classification approach.",
keywords = "Clustering, Comparison, Performance analysis, Profiling, Tracing, Visualization",
author = "Matthias Weber and Ronny Brendel and Tobias Hilbrich and Kathryn Mohror and Martin Schulz and Holger Brunst",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 30th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2016 ; Conference date: 23-05-2016 Through 27-05-2016",
year = "2016",
month = jul,
day = "18",
doi = "10.1109/IPDPS.2016.27",
language = "English",
series = "Proceedings - 2016 IEEE 30th International Parallel and Distributed Processing Symposium, IPDPS 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "484--493",
booktitle = "Proceedings - 2016 IEEE 30th International Parallel and Distributed Processing Symposium, IPDPS 2016",
}