Abstract
Performance data can be complex and potentially high dimensional. Further, it can be collected in multiple, independent domains. For example, one can measure code segments, hardware components, data structures, or an application’s communication structure. Performance analysis and visualization tools require access to this data in an easy way and must be able to specify relationships and mappings between these domains in order to provide users with intuitive, actionable performance analysis results.
In this paper, we describe a data model that can represent such complex performance data, and we discuss how this model helps us to specify mappings between domains. We then apply this model to several use cases both for data acquisition and how it can be mapped into the model, and for performance analysis and how it can be used to gain insight into an application’s performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
We will use these two terms interchangeably in the remainder of the paper.
References
Bell, J., Almgren, A., Beckner, V., Day, M., Lijewski, M., Nonaka, A., Zhang, W.: BoxLib user’s guide. https://ccse.lbl.gov/BoxLib/BoxLibUsersGuide.pdf (2013)
Bremer, P.-T., Mohr, B., Pascucci, V., Schulz, M.: Connecting performance analysis and visualization to advance extreme scale computing. http://www.dagstuhl.de/de/programm/kalender/semhp/?semnr=14022 (2014)
Burtscher, M., Kim, B.-D., Diamond, J., McCalpin, J., Koesterke, L., Browne, J.: Perfexpert: an easy-to-use performance diagnosis tool for HPC applications. In: Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis (SC’10), Washington, DC, pp. 1–11. IEEE Computer Society (2010)
Colella, P., Graves, D.T., Keen, N.D., Ligocki, T.J., Martin, D.F., McCorquodale, P.W., Modiano, D., Schwartz, P.O., Sternberg, T.D., Van Straalen, B.: Chombo software package for AMR applications design document. Technical report, Applied Numerical Algorithms Group, Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, 15 Apr 2009
Eichenberger, A.E., Mellor-Crummey, J., Schulz, M., Wong, M., Copty, N., Dietrich, R., Liu, X., Loh, E., Lorenz, D.: OMPT: an OpenMP tools application programming interface for performance analysis. In: Rendell, A.P., Chapman, B.M., Mller, M.S. (eds.) OpenMP in the Era of Low Power Devices and Accelerators. Number 8122 in Lecture Notes in Computer Science, pp. 171–185. Springer, Berlin/Heidelberg (2013)
Fahringer, T., Gerndt, M., Riley, G., Traff, J.L.: The APART Specification Language (illustrated with MPI). Technical report FZJ-ZAM-IB-2001-08, Forschungszentrum Jülich (2001)
Fürlinger, K., Gerndt, M.: ompP: A profiling tool for OpenMP. In: Proceedings of the First International Workshop on OpenMP (IWOMP’05), Eugene, 1–4 June 2005
Giménez, A., Gamblin, T., Rountree, B., Bhatele, A., Jusufi, I., Bremer, P.-T., Hamann, B.: Dissecting on-node memory access performance: a semantic approach. In: Proceedings of the ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis (SC’14), New Orleans, LA. IEEE Computer Society (2014). LLNL-CONF-658626
Hornung, R.D., Kohn, S.R.: Managing application complexity in the SAMRAI object-oriented framework. Concurr. Comput. Pract. Exp. 14(5), 347–368 (2002)
Hydrodynamics challenge problem, lawrence livermore national laboratory. Technical report LLNL-TR-490254
Isaacs, K., Bremer, P.-T., Jusufi, I., Gamblin, T., Bhatele, A., Schulz, M., Hamann, B.: Combing the communication hairball: visualizing large-scale parallel execution traces using logical time. In: Proceedings of IEEE Symposium on Information Visualization (InfoVis’14), Paris, France (2014)
Landge, A.G., Levine, J.A., Isaacs, K.E., Bhatele, A., Gamblin, T., Schulz, M., Langer, S.H., Bremer, P.-T., Pascucci, V.: Visualizing network traffic to understand the performance of massively parallel simulations. In: Proceedings of IEEE Symposium on Information Visualization (InfoVis’12), Seattle, 14–19 Oct 2012. LLNL-CONF-543359
Mellor-Crummey, J., Fowler, R., Marin, G.: HPCView: a tool for top-down analysis of node performance. J. Supercomput. 23, 81–101 (2002)
MPI Standard 3.0. http://www.mpi-forum.org/docs/docs.html
Nagel, W.E., Arnold, A., Weber, M., Hoppe, H.C., Solchenbach, K.: VAMPIR: visualization and analysis of MPI resources. Supercomputer 12(1), 69–80 (1996)
Schulz, M., Galarowicz, J., Maghrak, D., Hachfeld, W., Montoya, D., Cranford, S.: Open\(\mid\) speedshop: an open source infrastructure for parallel performance analysis. Sci. Program. 16(2–3), 105–121 (2008)
Schulz, M., Levine, J.A., Bremer, P.-T., Gamblin, T., Pascucci, V.: Interpreting performance data across intuitive domains. In: 2011 International Conference on Parallel Processing (ICPP), Taipei City, pp. 206–215 (2011)
Shende, S., Malony, A.D.: The tau parallel performance system. Int. J. High Perform. Comput. Appl., ACTS Collection Special Issue 20(2), 287–311 (2005)
Sigovan, C., Muelder, C.W., Ma, K.-L.: Visualizing large-scale parallel communication traces using a particle animation technique. Comput. Graph. Forum 32(3), 141–150 (2013)
Tallent, N.R., Adhianto, L., Mellor-Crummey, J.M.: Scalable identification of load imbalance in parallel executions using call path profiles. In: Proceedings of IEEE/ACM Supercomputing’10, New Orleans (2010)
Tapus, C., Chung, I.-H., Hollingsworth, J.K.: Active harmony: towards automated performance tuning. In: Proceedings of the Conference on High Performance Networking and Computing, Phoenix, pp. 1–11 (2003)
Vetter, J., Chambreau, C.: mpiP: Lightweight, Scalable MPI Profiling. http://mpip.sourceforge.net, March 13, 2014
Wolf, F., Wylie, B., Abraham, E., Becker, D., Frings, W., Fuerlinger, K., Geimer, M., Hermanns, M.-A., Mohr, B., Moore, S., Szebenyi, Z.: Usage of the SCALASCA toolset for scalable performance analysis of large-scale parallel applications. In: Proceedings of the 2nd HLRS Parallel Tools Workshop, Stuttgart (2008)
Zaki, O., Lusk, E., Gropp, W., Swider, D.: Toward scalable performance visualization with jumpshot. Int. J. High Perform. Comput. Appl. 13(3), 277–288 (1999)
Acknowledgements
This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 (LLNL-CONF-664263) and supported by Office of Science, Office of Advanced Scientific Computing Research as well as the Advanced Simulation and Computing (ASC) program.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Schulz, M. et al. (2015). A Flexible Data Model to Support Multi-domain Performance Analysis. In: Niethammer, C., Gracia, J., Knüpfer, A., Resch, M., Nagel, W. (eds) Tools for High Performance Computing 2014. Springer, Cham. https://doi.org/10.1007/978-3-319-16012-2_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-16012-2_10
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16011-5
Online ISBN: 978-3-319-16012-2
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)