Statistical Performance Analysis for Scientific Applications
Authors: 
Fei Xing, Haihang You, Charng-Da LuAuthors Info & Claims
Fei Xing, Haihang You, Charng-Da LuAuthors Info & ClaimsArticle No.: 62, Pages 1 - 8
Abstract
As high-performance computing (HPC) heads towards the exascale era, application performance analysis becomes more complex and less tractable. It usually requires considerable training, experience, and a good working knowledge of hardware/software interaction to use performance tools effectively, which becomes a barrier for domain scientists. Moreover, instrumentation and profiling activities from a large run can easily generate gigantic data volume, making both data management and characterization another challenge. To cope with these, we develop a statistical method to extract the principal performance features and produce easily interpretable results. This paper introduces a performance analysis methodology based on the combination of Variable Clustering (VarCluster) and Principal Component Analysis (PCA), describes the analysis process, and gives experimental results of scientific applications on a Cray XT5 system. As a visualization aid, we use Voronoi tessellations to map the numerical results into graphical forms to convey the performance information more clearly.
References
[1]
http://ambermd.org.
[2]
http://www.mpa-garching.mpg.de/gadget/.
[3]
http://www.gromacs.org.
[4]
http://lammps.sandia.gov.
[5]
http://www.physics.utah.edu/~detar/milc/.
[6]
http://www.ks.uiuc.edu.
[7]
http://www.wrf-model.org.
[8]
Cray performance analysis tools. http://docs.cray.com.
[9]
D. H. Ahn and J. S. Vetter. Scalable analysis techniques for microprocessor performance counter metrics. In IEEE/ACM Int'l Conf. on High Performance Computing, Networking, Storage and Analysis (SC), 2002.
[10]
K. Asanovic and et al. The landscape of parallel computing research: A view from Berkeley. Technical Report No. UCB/EECS-2006-183, University of California, Berkeley.
[11]
M. Balzer, O. Deussen, and C. Lewerentz. Voronoi treemaps for the visualization of software metrics. In ACM Symposium on Software Visualization, 2005.
[12]
S. Browne, J. Dongarra, N. Garner, G. Ho, and P. Mucci. A portable programming interface for performance evaluation on modern processors. Int'l Journal of High Performance Computing Applications, 14(3):189--204, 2000.
[13]
M. Chavent, V. Kuentz, B. Liquet, and J. Saracco. ClusterOfVar: An R package for the clustering of variables. Journal of Statistical Software, 50(13), 2012.
[14]
T. Chen, M. Gunn, B. Simon, L. Carrington, and A. Snavely. Metrics for ranking the performance of supercomputers. Cyberinfrastructure Technology Watch (CTWatch) Journal: Special Issue on High Productivity Computer Systems, 2(4), 2007.
[15]
W.-C. Feng and K. Cameron. The Green500 list: Encouraging sustainable supercomputing. IEEE Computer, 40(12):50--55, 2007.
[16]
W. Gropp and K. Buschelman. Fast profiling library for MPI. http://www.mcs.anl.gov/fpmpi.
[17]
K. Hoste and L. Eeckhout. Microarchitecture-independent workload characterization. IEEE Micro, 27(3), 2007.
[18]
R. Johnson and D. Wichern. Applied Multivariate Statistical Analysis. Pearson Prentice Hall, 2007.
[19]
A. Kaiser, S. Williams, K. Madduri, and K. Ibrahim. Principled kernel testbed for hardware/software co-design research. In USENIX Workshop on Hot Topics in Parallelism (HotPar), 2010.
[20]
P. Luszczek and J. Dongarra. Anatomy of a globally recursive embedded LINPACK benchmark. In IEEE High Performance Extreme Computing Conference (HPEC), 2012.
[21]
P. Luszczek and et al. Introduction to the HPC Challenge benchmark suite. http://www.hpcchallenge.org/pubs, 2005.
[22]
H. Meuer, E. Strohmaier, J. Dongarra, and H. Simon. TOP500 Supercomputing Sites. http://www.top500.org.
[23]
R. C. Murphy, K. B. Wheeler, B. W. Barrett, and J. A. Ang. Introducing the Graph 500. In Proceedings of the Cray User's Group Meeting (CUG), 2010.
[24]
R. Nair, H. Choi, and H. Tufo. Computational aspects of a scalable high-order discontinuous Galerkin atmospheric dynamical core. Computers & Fluids, 38(2), 2009.
[25]
W. Pfeiffer and N. Wright. Modeling and predicting application performance on parallel computers using HPC Challenge benchmarks. In IEEE Int'l Parallel & Distributed Processing Symposium (IPDPS), 2008.
[26]
A. Stamatakis and et al. RAxML-Light: A tool for computing terabyte phylogenies. Bioinformatics, 28(15), 2012.
[27]
Standard Performance Evaluation Corporation. SPEC MPI 2007. http://www.spec.org/mpi/.
[28]
J. S. Vetter and D. A. Reed. Managing performance analysis with dynamic statistical projection pursuit. In IEEE/ACM Int'l Conf. on High Performance Networking and Computing (SC), 1999.
Index Terms
- Statistical Performance Analysis for Scientific Applications
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July 2014
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In-Cooperation
- NSF: National Science Foundation
- Drexel University
- Indiana University: Indiana University
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Association for Computing Machinery
New York, NY, United States
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Published: 13 July 2014
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XSEDE '14
XSEDE '14: Annual Conference of the Extreme Science and Engineering Discovery Environment
July 13 - 18, 2014
GA, Atlanta, USA
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XSEDE '14 Paper Acceptance Rate 80 of 120 submissions, 67%;
Overall Acceptance Rate 129 of 190 submissions, 68%
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