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Relating memory performance data to application domain data using an integration API

Published: 15 November 2015 Publication History

Abstract

Understanding performance data, and more specifically memory access pattern is essential in optimizing scientific applications. Among the various factors affecting performance, such as the hardware architecture, the algorithms, or the system software stack, performance is also often related to the applications' physics. While there exists a number of techniques to collect relevant performance metrics, such as number of cache misses, traditional tools almost exclusively present this data relative to the code or as abstract tuples. This can obscure the data dependent nature of performance bottlenecks and make root-cause analysis difficult. Here we take advantage of the fact that a large class of applications are defined over some domain discretized by a mesh. By projecting the performance data directly onto these meshes, we enable developers to explore the performance data in the context of their application resulting in more intuitive visualizations. We introduce a lightweight, general interface to couple a performance visualization tool, MemAxes, to an external visualization tool, VisIt. This allows us to harness the advanced analytic capabilities of MemAxes to drive the exploration while exploiting the capabilities of VisIt to visualize both application and performance data in the application domain.

References

[1]
Hydrodynamics Challenge Problem, Lawrence Livermore National Laboratory. Technical Report LLNLTR-490254.
[2]
M. Adams et al. Package for AMR Applications - Design Document, Lawrence Berkeley National Laboratory Technical Report LBNL-6616E.
[3]
Alfredo Giménez, Todd Gamblin, Barry Rountree, Abhinav Bhatele, Ilir Jusufi, Peer-Timo Bremer, and Bernd Hamann. Dissecting on-node memory access performance: A semantic approach. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC '14, pages 166--176, Piscataway, NJ, USA, 2014. IEEE Press.
[4]
Hank Childs. Visit: An end-user tool for visualizing and analyzing very large data. 2013.
[5]
Nick Rutar and Jeffrey K. Hollingsworth. Data centric techniques for mapping performance data to program variables. Parallel Computing, 38(1-2):2--14, 2012. Extensions for Next-Generation Parallel Programming Models.
[6]
M. Schulz, J.A. Levine, P.-T. Bremer, T. Gamblin, and V. Pascucci. Interpreting performance data across intuitive domains. In Parallel Processing (ICPP), 2011 International Conference on, pages 206--215, Sept 2011.
[7]
David Böhme. Characterizing load and communication imbalance in parallel applications, volume 23. Forschungszentrum Jülich, 2014.
[8]
Kevin A. Huck, Kristin Potter, Doug W. Jacobsen, Hank Childs, and Allen D. Malony. Linking performance data into scientific visualization tools. In Proceedings of the First Workshop on Visual Performance Analysis, VPA '14, pages 50--57, Piscataway, NJ, USA, 2014. IEEE Press.
[9]
S. Shende and A. D. Malony. The tau parallel performance system. In International Journal of High Performance Computing Applications, vol. 20, page 287?311, 2006.
[10]
Lanl and ncar. MPAS, http://mpas-dev.github.io.
[11]
Conduit. Technical Report LLNL-CODE-666778.

Cited By

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  • (2019)Projecting Performance Data over Simulation Geometry Using SOSflow and ALPINEChildhood, Science Fiction, and Pedagogy10.1007/978-3-030-17872-7_12(201-218)Online publication date: 24-Apr-2019

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cover image ACM Conferences
VPA '15: Proceedings of the 2nd Workshop on Visual Performance Analysis
November 2015
44 pages
ISBN:9781450340137
DOI:10.1145/2835238
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 15 November 2015

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  • National Science Foundation

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VPA '15 Paper Acceptance Rate 5 of 6 submissions, 83%;
Overall Acceptance Rate 5 of 6 submissions, 83%

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Cited By

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  • (2019)Projecting Performance Data over Simulation Geometry Using SOSflow and ALPINEChildhood, Science Fiction, and Pedagogy10.1007/978-3-030-17872-7_12(201-218)Online publication date: 24-Apr-2019

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