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000808937 037__ $$aFZJ-2016-02457
000808937 1001_ $$0P:(DE-HGF)0$$avon Rüden, Laura$$b0
000808937 1112_ $$a2nd Workshop on Visual Performance Analysis$$cAustin$$d11/15/2015 - 11/15/2015$$gVPA '15$$wTexas
000808937 245__ $$aSeparating the wheat from the chaff: Identifying Relevant and Similar Performance Data with Visual Analytics
000808937 260__ $$aNew York, New York, USA$$bACM Press$$c2015
000808937 29510 $$aProceedings of the 2nd Workshop on Visual Performance Analysis - VPA '15. - ISBN 9781450340137
000808937 300__ $$aArticle No. 4
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000808937 520__ $$aPerformance-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%.
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000808937 7001_ $$0P:(DE-Juel1)168253$$aHermanns, Marc-André$$b1$$ufzj
000808937 7001_ $$0P:(DE-HGF)0$$aBehrisch, Michael$$b2
000808937 7001_ $$0P:(DE-HGF)0$$aKeim, Daniel$$b3
000808937 7001_ $$0P:(DE-Juel1)132199$$aMohr, Bernd$$b4$$ufzj
000808937 7001_ $$0P:(DE-HGF)0$$aWolf, Felix$$b5
000808937 773__ $$a10.1145/2835238.2835242
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000808937 9141_ $$y2016
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