Scalable parallel performance measurement and analysis tools - state-of-the-art and future challenges
DOI:
https://doi.org/10.14529/jsfi140207Abstract
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 faultrecovery 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
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.
Virtual Institute - High Productivity Supercomputing (VI-HPS): VI-HPS Tools Guide. Available at http://www.vi-hps.org/tools/.
S. Shende and A. D. Malony: The TAU Parallel Performance System. Intl. Journal of High Performance Computing Applications, 20(2):287-331, 2006. SAGE Publications.
TAU homepage. University of Oregon. http://tau.uoregon.edu
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.
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, 22(6):685-701, 2010.
HPCToolkit homepage. Rice University. http://hpctoolkit.org
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.
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.
Paraver homepage. Barcelona Supercomputing Center. http://www.bsc.es/paraver
Dyninst homepage. University of Wisconsin - Madison. http://www.dyninst.org/
M. S. Müller, A. Knüpfer, M. Jurenz, M. Lieber, H. Brunst, H. Mix, W. E. Nagel: Developing Scalable Applications with Vampir, VampirServer and VampirTrace. Proceedings of ParCo 2007, Jülich, Germany, 637-644, IOS Press, 2007.
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.
Vampir homepage. Technical University Dresden. http://www.vampir.eu
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.
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.
F. Wolf, B. Mohr: EPILOG Binary Trace-Data Format. Technical Report FZJ-ZAM-IB-2004-06, Forschungszentrum Jülich, 2004.
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, 22(6):702-719, 2010.
Scalasca homepage. Jülich Supercomputing Centre and German Research School for Simulation Sciences. http://www.scalasca.org
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.
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.
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.
Score-P homepage. Score-P Consortium. http://www.score-p.org
M. Gerndt and M. Ott: Automatic Performance Analysis with Periscope. Concurrency and Computation: Practice and Experience, 22(6):736-748, 2010.
PAPI homepage. University of Tennessee - Knoxville. http://icl.cs.utk.edu/papi/
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.
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.
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.
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.
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.
Downloads
Published
How to Cite
Issue
License
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-Non Commercial 3.0 License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.