article

Scalable parallel performance measurement and analysis tools - state-of-the-art and future challenges

Author:

Bernd MohrAuthors Info & Claims

Supercomputing Frontiers and Innovations: an International Journal, Volume 1, Issue 2

Pages 108 - 123

https://doi.org/10.14529/jsfi140207

Published: 09 July 2014 Publication History

Abstract

Current large-scale HPC systems consist of complex configurations with a huge number of potentially heterogeneous components. As the systems get larger, their behavior becomes more and more dynamic and unpredictable because of hard and software re-configurations due to fault recovery and power usage optimizations. Deep software hierarchies of large, complex system software and middleware components are required to operate such systems. Therefore, porting, adapting and tuning applications to today's complex systems is a complicated and time-consuming task. Sophisticated integrated performance measurement, analysis, and optimization capabilities are required to efficiently utilize such systems. This article will summarize the state-of-the-art of scalable and portable parallel performance tools and the challenges these tools are facing on future extreme-scale and big data systems.

References

[1]

R. Klar and N. Luttenberger: VLSI-based Monitoring of the Inter-Process-Communication of Multi-Microcomputer Systems with Shared Memory. Proceedings EUROMICRO '86, Microprocessing and Microprogramming, vol. 18, no. 15, 195-204, Venice, Italy, 1986.

Digital Library

[2]

Virtual Institute - High Productivity Supercomputing VI-HPS: VI-HPS Tools Guide. Available at http://www.vi-hps.org/tools/.

[3]

S. Shende and A. D. Malony: The TAU Parallel Performance System. Intl. Journal of High Performance Computing Applications, 202:287-331, 2006. SAGE Publications.

Digital Library

[4]

TAU homepage. University of Oregon. http://tau.uoregon.edu

[5]

K. A. Huck and A. D. Malony: 2005. PerfExplorer: A Performance Data Mining Framework For Large-Scale Parallel Computing. Proceedings ACM/IEEE conference on Supercomputing SC '05. IEEE, Washington, DC, USA, 2005.

Digital Library

[6]

L. Adhianto, S. Banerjee, M. Fagan, M. Krentel, G. Marin, J. Mellor-Crummey, and N. R. Tallent: HPCToolkit: Tools for performance analysis of optimized parallel programs. Concurrency and Computation: Practice and Experience, 226:685-701, 2010.

Digital Library

[7]

HPCToolkit homepage. Rice University. http://hpctoolkit.org

[8]

J. Labarta, S. Girona, V. Pillet, T. Cortes, L. Gregoris, DiP: A parallel program development environment. Proceedings 2nd Intl. Euro-Par Conference, Lyon, France, Springer, 1996.

Digital Library

[9]

H. Servat Gelabert, G. Llort Sanchez, J. Gimenez, and J. Labarta: Detailed performance analysis using coarse grain sampling. Proceedings Euro-Par 2009 - Parallel Processing Workshops, Delft, The Netherlands, August 2009, 185-198. Springer, 2010.

Digital Library

[10]

Paraver homepage. Barcelona Supercomputing Center. http://www.bsc.es/paraver

[11]

Dyninst homepage. University of Wisconsin - Madison. http://www.dyninst.org/

[12]

M. S. Müller, A. Knüpfer, M. Jurenz, M. Lieber, H. Brunst, H. Mix, W. E. Nagel: Developing Scalable Applications with Vampir, VampirServer and Vampir Trace. Proceedings of ParCo 2007, Jülich, Germany, 637-644, IOS Press, 2007.

[13]

Knüpfer, H. Brunst, J. Doleschal, M. Jurenz, M. Lieber, H. Mickler, M. S. Müller, W. E. Nagel: The Vampir Performance Analysis Tool-Set. Proceedings Parallel Tools Workshop 2008, 139-155, 2008.

[14]

Vampir homepage. Technical University Dresden. http://www.vampir.eu

[15]

Knüpfer, R. Brendel, H. Brunst, H. Mix, W. E. Nagel: Introducing the Open Trace Format OTF, Proceedings Computational Science - ICCS 2006: 6th Intl. Conference, Reading, UK, Springer, 526-533, 2006.

Digital Library

[16]

D. Eschweiler, M. Wagner, M. Geimer, A. Knüpfer, W. E. Nagel, F. Wolf: Open Trace Format 2 The Next Generation of Scalable Trace Formats and Support Libraries. Proceedings of ParCo 2011, Ghent, Belgium, 481-490, IOS Press, 2012.

[17]

F. Wolf, B. Mohr: EPILOG Binary Trace-Data Format. Technical Report FZJ-ZAM-IB-2004-06, Forschungszentrum Jülich, 2004.

[18]

M. Geimer, F. Wolf, B.J.N. Wylie, E. Ábrahám, D. Becker, B. Mohr: The Scalasca performance toolset architecture. Concurrency and Computation: Practice and Experience, 226:702-719, 2010.

Digital Library

[19]

Scalasca homepage. Jülich Supercomputing Centre and German Research School for Simulation Sciences. http://www.scalasca.org

[20]

D. Böhme, B. R. de Supinski, M. Geimer, M. Schulz, F. Wolf: Scalable Critical-Path Based Performance Analysis. Proceedings IEEE Intl. Parallel &Distributed Processing Symposium IPDPS, Shanghai, China, 1330-1340, IEEE, 2012.

Digital Library

[21]

D. Böhme, M. Geimer, F. Wolf, L. Arnold: Identifying the root causes of wait states in large-scale parallel applications. Proceedings Intl. Conference on Parallel Processing ICPP, San Diego, CA, USA, 90-100, IEEE, 2010.

Digital Library

[22]

D. an Mey, S. Biersdorff, C. Bischof, K. Diethelm, D. Eschweiler, M. Gerndt, A. Knüpfer, D. Lorenz, A.D. Malony, W.E. Nagel, Y. Oleynik, C. Rössel, P. Saviankou, D. Schmidl, S.S. Shende, M. Wagner, B. Wesarg, F. Wolf: Score-P: A Unified Performance Measurement System for Petascale Applications. In: Competence in High Performance Computing 2010 CiHPC, 85-97. Springer, 2012.

[23]

Score-P homepage. Score-P Consortium. http://www.score-p.org

[24]

M. Gerndt and M. Ott: Automatic Performance Analysis with Periscope. Concurrency and Computation: Practice and Experience, 226:736-748, 2010.

Digital Library

[25]

PAPI homepage. University of Tennessee - Knoxville. http://icl.cs.utk.edu/papi/

[26]

W. Frings, F. Wolf, V. Petko:. Scalable massively parallel I/O to task-local files. Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis SC'09. ACM, New York, NY, USA, Article 17, 2009.

Digital Library

[27]

B. Mohr, V. Voevodin, J. Giméenez, E. Hagersten, A. Knüpfer, D. A. Nikitenko, M. Nilsson, H. Servat, A. Shah, F. Winkler, F. Wolf, and I. Zhukov: The HOPSA Workflow and Tools. Proceedings 6th Intl. Parallel Tools Workshop, Stuttgart, September 2012.

[28]

E. Berg, E. Hagersten: StatCache: A Probabilistic Approach to Efficient and Accurate Data Locality Analysis. Proceedings IEEE Intl. Symposium on Performance Analysis of Systems and Software ISPASS-2004, Austin, Texas, USA, 2004.

Digital Library

[29]

A.V. Adinets, P.A. Bryzgalov, Vad.V. Voevodin, S.A. Zhumatiy, D.A. Nikitenko: About one approach to monitoring, analysis and visualization of jobs on cluster system In Russian. Numerical Methods and Programming, vol. 12, 90-93, 2011.

[30]

Calotoiu, T. Hoefler, M. Poke, F. Wolf: Using Automated Performance Modeling to Find Scalability Bugs in Complex Codes. Proceedings ACM/IEEE Conference on Supercomputing SC13, Denver, CO, USA, 1-12, ACM, 2013.

Digital Library

Cited By

Jin YWang HZhong RZhang CLiao XZhang FZhai J(2024)Graph-Centric Performance Analysis for Large-Scale Parallel ApplicationsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2024.339684935:7(1221-1238)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.1109/TPDS.2024.3396849
Roth ASüß TVieira MCardellini VDi Marco ATuma P(2023)Performance Analysis Tools for MPI Applications and their Use in Programming EducationCompanion of the 2023 ACM/SPEC International Conference on Performance Engineering10.1145/3578245.3584358(361-368)Online publication date: 15-Apr-2023
https://dl.acm.org/doi/10.1145/3578245.3584358
Cankur OBhatele A(2022)Comparative Evaluation of Call Graph Generation by Profiling ToolsHigh Performance Computing10.1007/978-3-031-07312-0_11(213-232)Online publication date: 29-May-2022
https://dl.acm.org/doi/10.1007/978-3-031-07312-0_11

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Scalable parallel performance measurement and analysis tools - state-of-the-art and future challenges

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cover image Supercomputing Frontiers and Innovations: an International Journal

Supercomputing Frontiers and Innovations: an International Journal Volume 1, Issue 2

July 2014

120 pages

ISSN:2409-6008

EISSN:2313-8734

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South Ural State University

Chelyabinsk, Russian Federation

Publication History

Published: 09 July 2014

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Cited By

Jin YWang HZhong RZhang CLiao XZhang FZhai J(2024)Graph-Centric Performance Analysis for Large-Scale Parallel ApplicationsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2024.339684935:7(1221-1238)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.1109/TPDS.2024.3396849
Roth ASüß TVieira MCardellini VDi Marco ATuma P(2023)Performance Analysis Tools for MPI Applications and their Use in Programming EducationCompanion of the 2023 ACM/SPEC International Conference on Performance Engineering10.1145/3578245.3584358(361-368)Online publication date: 15-Apr-2023
https://dl.acm.org/doi/10.1145/3578245.3584358
Cankur OBhatele A(2022)Comparative Evaluation of Call Graph Generation by Profiling ToolsHigh Performance Computing10.1007/978-3-031-07312-0_11(213-232)Online publication date: 29-May-2022
https://dl.acm.org/doi/10.1007/978-3-031-07312-0_11
Jin YWang HYu TTang XHoefler TLiu XZhai JCuicchi CQualters IKramer W(2020)ScalAnaProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.5555/3433701.3433738(1-14)Online publication date: 9-Nov-2020
https://dl.acm.org/doi/10.5555/3433701.3433738

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