Assessing Measurement and Analysis Performance and Scalability of Scalasca 2.0

Zhukov, Ilya; Wylie, Brian J. N.

doi:10.1007/978-3-642-54420-0_61

Contribution to a conference proceedings/Contribution to a book

FZJ-2014-03075

Assessing Measurement and Analysis Performance and Scalability of Scalasca 2.0

Zhukov, I. (Corresponding Author)FZJ* ; Wylie, B. J. N.FZJ*

2014
Springer Berlin Heidelberg Berlin, Heidelberg
ISBN: 978-3-642-54419-4 (print), 978-3-642-54420-0 (electronic)

Euro-Par 2013: Parallel Processing Workshops
Euro-Par 2013: Parallel Processing Workshops, Aachen, Germany, 26 Aug 2013 - 27 Aug 2013 Berlin, Heidelberg : Springer Berlin Heidelberg, Lecture Notes in Computer Science 8374, 627 - 636 (2014) [10.1007/978-3-642-54420-0_61]

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Please use a persistent id in citations: doi:10.1007/978-3-642-54420-0_61

Abstract: The Scalasca toolset was developed to provide highly scalable performance measurement and analysis of scientific applications on current HPC platforms, including leadership systems such as IBM BlueGene/Q and more traditional Linux clusters. Its primary focus is support for C/C++/Fortran applications using MPI and OpenMP, and mixed-mode combinations thereof, offering detailed call-path profiles for each process and thread produced by runtime summarization or augmented with wait-state analysis of event traces. A new generation of Scalasca (2.0) uses the community-developed infrastructure comprising of Score-P and associated components, while continuing to provide the previous functionality. By comparing the new version of Scalasca with its predecessor, using the applications from the NPB3.3-MZ-MPI benchmark suite, we validate core functionality and assess overheads and scalability. Although adequate for general use, various aspects are identified for further improvement, particularly for larger scales.

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