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Managing performance analysis with dynamic statistical projection pursuit

Authors:

Jeffrey S. Vetter,

Daniel A. ReedAuthors Info & Claims

SC '99: Proceedings of the 1999 ACM/IEEE conference on Supercomputing

Pages 44 - es

https://doi.org/10.1145/331532.331576

Published: 01 January 1999 Publication History

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References

[1]

R. Aydt, "The Pablo Self-Defining Data Format," University of Illinois at Urbana- Champaign, Department of Computer Science, Urbana, IL, USA 1997.

Google Scholar

[2]

T. Ball and J. R. Larus, "Efficient path profiling," Proc. 29th Annual IEEE/ACM Int'l Symp. Microarchitecture (MICRO-29), 1996, pp. 46-57.

Digital Library

Google Scholar

[3]

P. C. Bates, "Debugging heterogeneous distributed systems using event-based models of behavior," ACM Trans. Computer Systems, 13(1):1-31, 1995.

Digital Library

Google Scholar

[4]

C. Bona, J. Masso, E. Seidel, and P. Walker, "Three Dimensional Numerical Relativity with a Hyperbolic Formulation," Paper submitted to Phys. Rev. D, 1999.

Google Scholar

[5]

P. N. Brown, R. D. Falgout, and J. E. Jones, "Semicoarsening multigrid on distributed memory machines," SIAM Journal Scientific Computing.

Digital Library

Google Scholar

[6]

D. Cook, A. Buja, J. Cabrera, and C. Hurley, "Grand Tour and Projection Pursuit," J. Computational and Graphical Statistics, 4:155-172, 1995.

Google Scholar

[7]

U. M. Fayyad, "Advances in knowledge discovery and data mining," . Menlo Park, Calif.: AAAI Press : MIT Press, 1996, pp. xiv, 611.

Digital Library

Google Scholar

[8]

J. H. Friedman, "Exploratory Projection Pursuit," J. American Statistical Association, 82:249-266, 1987.

Crossref

Google Scholar

[9]

C. Hurley and A. Buja, "Analyzing highdimensional data with motion graphics," SIAM J. Scientific & Statistical Computing, 11(6):1193-211, 1990.

Digital Library

Google Scholar

[10]

R. A. Johnson, Applied multivariate statistical analysis. Englewood Cliffs, New Jersey, USA: Prentice-Hall, 1982.

Digital Library

Google Scholar

[11]

K. R. Koch, R. S. Baker, and R. E. Alcouffe, "Solution of the First-Order Form of the 3-D Discrete Ordinates Equation on a Massively Parallel Processor," Trans. Amer. Nuc. Soc., 65(198), 1992.

Google Scholar

[12]

T. T. Kwan, R. E. McGrath, and D. A. Reed, "NCSA's World Wide Web Server: Design and Performance," IEEE Computer, 28(11):68--74, 1995.

Digital Library

Google Scholar

[13]

A. D. Malony, D. A. Reed, and H. A. G. Wijshoff, "Performance measurement intrusion and perturbation analysis," IEEE Trans. Parallel and Distributed Systems, 3(4):433-50, 1992.

Digital Library

Google Scholar

[14]

B. P. Miller, M. D. Callaghan, J. M. Cargille, J. K. Hollingsworth, R. B. Irvin, K. L. Karavanic, K. Kunchithapadam, and T. Newhall, "The Paradyn parallel performance measurement tool," IEEE Computer, 28(11):37-46, 1995.

Digital Library

Google Scholar

[15]

G. Nason, "Three-Dimensional Projection Pursuit," J. Royal Statistical Society, Series C, 44:411-430, 1995.

Google Scholar

[16]

D. A. Reed, O. Y. Nickolayev, and P. C. Roth, "Real-Time Statistical Clustering and for Event Trace Reduction," J. Supercomputing Applications and High-Performance Computing, 11(2):144-159, 1997.

Digital Library

Google Scholar

[17]

R. L. Ribler, J. S. Vetter, H. Simitci, and D. A. Reed, "Autopilot: adaptive control of distributed applications," Proc. Seventh Int'l Symp. High Performance Distributed Computing (HPDC), 1998.

Digital Library

Google Scholar

[18]

H. Spath, Cluster analysis algorithms. West Sussex, England: Ellis Horwood Limited, 1980.

Google Scholar

[19]

P. A. Tukey and J. W. Tukey, "Preparation; Prechosen Sequences of Views," in Interpreting multivariate data, vol. 1, V. Barnett, Ed. New York: John Wiley & Sons, 1981, pp. 189-213.

Google Scholar

[20]

E. J. Wegman and L. Qiang, "High dimensional clustering using parallel coordinates and the grand tour," Proc. 28th Symp. Interface of Computing Science and Statistics, 1996.

Google Scholar

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Xing FYou HLu CLathrop SAlameda J(2014)Statistical Performance Analysis for Scientific ApplicationsProceedings of the 2014 Annual Conference on Extreme Science and Engineering Discovery Environment10.1145/2616498.2616555(1-8)Online publication date: 13-Jul-2014
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Nguyen TNagappan MHassan ANasser MFlora PDevanbu PKim SPinzger M(2014)An industrial case study of automatically identifying performance regression-causesProceedings of the 11th Working Conference on Mining Software Repositories10.1145/2597073.2597092(232-241)Online publication date: 31-May-2014
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Managing performance analysis with dynamic statistical projection pursuit

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Published In

SC '99: Proceedings of the 1999 ACM/IEEE conference on Supercomputing

January 1999

1015 pages

ISBN:1581130910

DOI:10.1145/331532

General Chair:
Cherri Pancake

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 01 January 1999

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SC '99: International Conference for High Performance Computing, Networking, Storage and Analysis

November 14 - 19, 1999

Oregon, Portland, USA

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Cited By

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Xing FYou HLu CLathrop SAlameda J(2014)Statistical Performance Analysis for Scientific ApplicationsProceedings of the 2014 Annual Conference on Extreme Science and Engineering Discovery Environment10.1145/2616498.2616555(1-8)Online publication date: 13-Jul-2014
https://dl.acm.org/doi/10.1145/2616498.2616555
Nguyen TNagappan MHassan ANasser MFlora PDevanbu PKim SPinzger M(2014)An industrial case study of automatically identifying performance regression-causesProceedings of the 11th Working Conference on Mining Software Repositories10.1145/2597073.2597092(232-241)Online publication date: 31-May-2014
https://dl.acm.org/doi/10.1145/2597073.2597092
Xing FYou HLu C(2014)HPC Benchmark Assessment with Statistical AnalysisProcedia Computer Science10.1016/j.procs.2014.05.01929(210-219)Online publication date: 2014
https://doi.org/10.1016/j.procs.2014.05.019
Szebenyi ZWolf FWylie BPinfold W(2009)Space-efficient time-series call-path profiling of parallel applicationsProceedings of the Conference on High Performance Computing Networking, Storage and Analysis10.1145/1654059.1654097(1-12)Online publication date: 14-Nov-2009
https://dl.acm.org/doi/10.1145/1654059.1654097
Reiss SAvritzer AWeyuker EWoodside M(2008)Controlled dynamic performance analysisProceedings of the 7th international workshop on Software and performance10.1145/1383559.1383566(43-54)Online publication date: 23-Jun-2008
https://dl.acm.org/doi/10.1145/1383559.1383566
Wu YLiu LMao JYang GZheng WKahaner D(2007)An analytical model for performance evaluation in a computational gridProceedings of the 2007 Asian technology information program's (ATIP's) 3rd workshop on High performance computing in China: solution approaches to impediments for high performance computing10.1145/1375783.1375813(145-151)Online publication date: 11-Nov-2007
https://dl.acm.org/doi/10.1145/1375783.1375813
Cesar EMoreno ASorribes JLuque E(2006)Modeling master/worker applications for automatic performance tuningParallel Computing10.1016/j.parco.2006.06.00532:7(568-589)Online publication date: 1-Sep-2006
https://dl.acm.org/doi/10.1016/j.parco.2006.06.005
Metz ELencevicius RGonzalez T(2005)Performance data collection using a hybrid approachACM SIGSOFT Software Engineering Notes10.1145/1095430.108172930:5(126-135)Online publication date: 1-Sep-2005
https://dl.acm.org/doi/10.1145/1095430.1081729
Metz ELencevicius RGonzalez TWermelinger MGall H(2005)Performance data collection using a hybrid approachProceedings of the 10th European software engineering conference held jointly with 13th ACM SIGSOFT international symposium on Foundations of software engineering10.1145/1081706.1081729(126-135)Online publication date: 5-Sep-2005
https://dl.acm.org/doi/10.1145/1081706.1081729
Mendes CReed D(2004)Monitoring Large Systems Via Statistical SamplingInternational Journal of High Performance Computing Applications10.1177/109434200403895818:2(267-277)Online publication date: 1-May-2004
https://dl.acm.org/doi/10.1177/1094342004038958
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