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2019 | OriginalPaper | Buchkapitel

Visualizing, Measuring, and Tuning Adaptive MPI Parameters

verfasst von : Matthias Diener, Sam White, Laxmikant V. Kale

Erschienen in: Programming and Performance Visualization Tools

Verlag: Springer International Publishing

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Abstract

Adaptive MPI (AMPI) is an advanced MPI runtime environment that offers several features over traditional MPI runtimes, which can lead to a better utilization of the underlying hardware platform and therefore higher performance. These features are overdecomposition through virtualization, and load balancing via rank migration. Choosing which of these features to use, and finding the optimal parameters for them is a challenging task however, since different applications and systems may require different options. Furthermore, there is a lack of information about the impact of each option. In this paper, we present a new visualization of AMPI in its companion Projections tool, which depicts the operation of an MPI application and details the impact of the different AMPI features on its resource usage. We show how these visualizations can help to improve the efficiency and execution time of an MPI application. Applying optimizations indicated by the performance analysis to two MPI-based applications results in performance improvements of up 18% from overdecomposition and load balancing.

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Vorheriges Kapitel Projecting Performance Data over Simulation Geometry Using SOSflow and ALPINE

Nächstes Kapitel Visual Analytics Challenges in Analyzing Calling Context Trees

https://codesign.llnl.gov/lulesh.php.

https://github.com/ParRes/Kernels.

https://charm.cs.illinois.edu/software.

Acun, B., et al.: Parallel programming with migratable objects: Charm++ in practice. In: SC (2014). https://doi.org/10.1109/SC.2014.58

Acun, B., Kale, L.V.: Mitigating processor variation through dynamic load balancing. In: 2016 IEEE International Parallel and Distributed Processing Symposium Workshops, pp. 1073–1076. IEEE (2016)

Adhianto, L., et al.: HPCToolkit: tools for performance analysis of optimized parallel programs. Concurr. Comput.: Pract. Exp. 22(6), 685–701 (2010). https://doi.org/10.1002/cpe.1553CrossRef

Bhandarkar, M., Kalé, L.V., de Sturler, E., Hoeflinger, J.: Adaptive load balancing for MPI programs. In: Alexandrov, V.N., Dongarra, J.J., Juliano, B.A., Renner, R.S., Tan, C.J.K. (eds.) ICCS 2001. LNCS, vol. 2074, pp. 108–117. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-45718-6_13CrossRef

Gottbrath, C.: Automation assisted debugging on the Cray with TotalView. In: Proceedings of Cray User Group (2011)

Huang, C., Lawlor, O., Kalé, L.V.: Adaptive MPI. In: Rauchwerger, L. (ed.) LCPC 2003. LNCS, vol. 2958, pp. 306–322. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24644-2_20CrossRef

Islam, T., Mohror, K., Schulz, M.: Exploring the capabilities of the new MPI\(\_\)T interface. In: Proceedings of the 21st European MPI Users’ Group Meeting, p. 91. ACM (2014)

Islam, T., Mohror, K., Schulz, M.: Exploring the MPI tool information interface: features and capabilities. Int. J. High Perform. Comput. Appl., pp. 212–222. (2016). https://doi.org/10.1177/1094342015600507CrossRef

Jeannot, E., Meneses, E., Mercier, G., Tessier, F., Zheng, G.: Communication and topology-aware load balancing in Charm++ with TreeMatch. In: 2013 IEEE International Conference on Cluster Computing, CLUSTER, pp. 1–8. IEEE (2013)

10.

Kale, L.V., Krishnan, S.: CHARM++: a portable concurrent object oriented system based on C++. In: Conference on Object-Oriented Programming Systems, Languages, and Applications, OOPSLA, pp. 91–108 (1993)

11.

Karlin, I., et al.: Exploring traditional and emerging parallel programming models using a proxy application. In: 27th IEEE International Parallel & Distributed Processing Symposium, IEEE IPDPS 2013, Boston, USA, May 2013

12.

Karlin, I., Keasler, J., Neely, R.: Lulesh 2.0 updates and changes. Technical report LLNL-TR-641973, August 2013

13.

Karrels, E., Lusk, E.: Performance analysis of MPI programs. In: Environments and Tools for Parallel Scientific Computing, pp. 195–200 (1994)

14.

Knüpfer, A., et al.: The Vampir performance analysis tool-set. In: Resch, M., Keller, R., Himmler, V., Krammer, B., Schulz, A. (eds.) Tools for High Performance Computing, pp. 139–155. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-68564-7_9CrossRef

15.

Knüpfer, A., et al.: Score-P: a joint performance measurement run-time infrastructure for periscope, Scalasca, TAU, and Vampir. In: Brunst, H., Müller, M., Nagel, W., Resch, M. (eds.) Tools for High Performance Computing, pp. 79–91. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31476-6_7CrossRef

16.

Krammer, B., Bidmon, K., Müller, M.S., Resch, M.M.: MARMOT: an MPI analysis and checking tool. In: Advances in Parallel Computing, vol. 13, pp. 493–500 (2004)

17.

Lecomber, D., Wohlschlegel, P.: Debugging at scale with Allinea DDT. In: Cheptsov, A., Brinkmann, S., Gracia, J., Resch, M., Nagel, W. (eds.) Tools for High Performance Computing, pp. 3–12. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37349-7_1CrossRef

18.

Menon, H., Chandrasekar, K., Kale, L.V.: POSTER: automated load balancer selection based on application characteristics. In: Proceedings of the 22nd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, pp. 447–448. ACM (2017)

19.

Message Passing Interface Forum: MPI: A Message-Passing Interface Standard (Version 3.0). Technical report (2012)

20.

Müller, M.S., et al.: Developing scalable applications with Vampir, VampirServer and VampirTrace. In: PARCO, vol. 15, pp. 637–644 (2007)

21.

Pearce, O., Gamblin, T., de Supinski, B.R., Schulz, M., Amato, N.M.: Quantifying the effectiveness of load balance algorithms. In: ACM International Conference on Supercomputing, ICS, pp. 185–194 (2012). https://doi.org/10.1145/2304576.2304601

22.

Vetter, J.: Performance analysis of distributed applications using automatic classification of communication inefficiencies. In: Proceedings of the 14th International Conference on Supercomputing, pp. 245–254. ACM (2000)

23.

Vetter, J.: Dynamic statistical profiling of communication activity in distributed applications. In: Proceedings of the 2002 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2002, pp. 240–250. ACM, New York (2002). https://doi.org/10.1145/511334.511364

24.

Van der Wijngaart, R.F., Mattson, T.G.: The parallel research kernels. In: 2014 IEEE High Performance Extreme Computing Conference, HPEC, pp. 1–6. IEEE (2014)

25.

Wu, C.E., et al.: From trace generation to visualization: a performance framework for distributed parallel systems. In: Proceedings of the 2000 ACM/IEEE Conference on Supercomputing, SC 2000. IEEE Computer Society, Washington, DC (2000). http://dl.acm.org/citation.cfm?id=370049.370458

26.

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)CrossRef

27.

Zhai, J., Sheng, T., He, J.: Efficiently acquiring communication traces for large-scale parallel applications. IEEE Trans. Parallel Distrib. Syst. (TPDS) 22(11), 1862–1870 (2011). https://doi.org/10.1109/TPDS.2011.49CrossRef

Titel: Visualizing, Measuring, and Tuning Adaptive MPI Parameters
verfasst von: Matthias Diener
Sam White
Laxmikant V. Kale
Verlag: Springer International Publishing
Buch: Programming and Performance Visualization Tools
Print ISBN: 978-3-030-17871-0

Electronic ISBN: 978-3-030-17872-7

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-17872-7_13

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