TY  - CONF
AU  - von Rüden, Laura
AU  - Hermanns, Marc-André
AU  - Behrisch, Michael
AU  - Keim, Daniel
AU  - Mohr, Bernd
AU  - Wolf, Felix
TI  - Separating the wheat from the chaff: Identifying Relevant and Similar Performance Data with Visual Analytics
CY  - New York, New York, USA
PB  - ACM Press
M1  - FZJ-2016-02457
SP  - Article No. 4
PY  - 2015
AB  - Performance-analysis tools are indispensable for understanding and optimizing the behavior of parallel programs running on increasingly powerful supercomputers. However, with size and complexity of hardware and software on the rise, performance data sets are becoming so voluminous that their analysis poses serious challenges. In particular, the search space that must be traversed and the number of individual performance views that must be explored to identify phenomena of interest becomes too large. To mitigate this problem, we use visual analytics. Specifically, we accelerate the analysis of performance profiles by automatically identifying (1) relevant and (2) similar data subsets and their performance views. We focus on views of the virtual-process topology, showing that their relevance can be well captured with visual-quality metrics and that they can be further assigned to topical groups according to their visual features. A case study demonstrates that our approach helps reduce the search space by up to 80%.
T2  - 2nd Workshop on Visual Performance Analysis
CY  - 15 Nov 2015 - 15 Nov 2015, Austin (Texas)
Y2  - 15 Nov 2015 - 15 Nov 2015
M2  - Austin, Texas
LB  - PUB:(DE-HGF)8
DO  - DOI:10.1145/2835238.2835242
UR  - https://juser.fz-juelich.de/record/808937
ER  -