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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  3/2009 - 18

Workload Characterization an Essential Step in Computer Systems Performance Analysis - Methodology and Tools

CHEVERESAN, R.T. See more information about CHEVERESAN, R.T. on SCOPUS See more information about CHEVERESAN, R.T. on IEEExplore See more information about CHEVERESAN, R.T. on Web of Science, HOLBAN., S. See more information about HOLBAN., S. on SCOPUS See more information about HOLBAN., S. on SCOPUS See more information about HOLBAN., S. on Web of Science
 
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Download PDF pdficon (361 KB) | Citation | Downloads: 1,261 | Views: 5,005

Author keywords
performance analysis, workload characterization, instruction traces, instruction decomposition, data locality

References keywords
performance(9), modeling(5), memory(5), applications(5), systems(4), supercomputing(4), parallel(4), architecture(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2009-10-26
Volume 9, Issue 3, Year 2009, On page(s): 100 - 106
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2009.03018
Web of Science Accession Number: 000271872000018
SCOPUS ID: 77954752048

Abstract
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Computer system performance is a very complex process in which the hardware and software manufacturers invest important human and financial resources. Workload characterization represents an essential component of performance analysis. This paper presents a trace based methodology for software applications evaluation. It introduces a new analysis concept designed to significantly ease this process and it presents a set of experimental data collected using the new analysis structure on a representative set of scientific and commercial applications. Several important conclusions are drawn regarding workload characteristics, classifications and runtime behavior. This type of data is used by the computer architects in their efforts to maximize the performance of the hardware platforms these applications are going to execute on.


References | Cited By  «-- Click to see who has cited this paper

[1] D. Bailey, J. Barton, T. Lasinski, and H. Simon, "The NAS parallel benchmarks", International Journal of Supercomputing Applications, 27(2):63-73, 1991
[CrossRef] [Web of Science Times Cited 591] [SCOPUS Times Cited 777]


[2] I. Sharapov, R. Kroeger, G. Delamarter, R. Cheveresan, and M. Ramsay. "A case study in top-down performance estimation for a large-scale parallel application", ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, March 2006
[CrossRef] [SCOPUS Times Cited 14]


[3] R. Brown and I. Sharapov, "Parallelization of a molecular modeling application: Programmability comparison between OpenMP and MPI", Workshop on Productivity and Performance in High-End Computing, February 2006

[4] J. Dean, J. Hicks, C. Waldspruger, W. Weihl, and G. Chrysos, "Profileme: Hardware support for instruction-level profiling on out-of-order processors", Proceedings of Annual International Symposium on Microarchitecture, 1997

[5] Sun studio performance analyzer: developers.sun.com/prodtech/cc/analyzer/index.html

[6] Intel vtune performance analyzer: www.intel.com/cd/software/products/asmo-na/eng/vtune/index.htm

[7] M. Martonosi, A. Gupta, and T. Anderson, "Memspy: Analyzing memory system bottlenecks in programs", Measurement and Modeling of Computer Systems, pages 1-12, 1992

[8] A. Lebeck and D. Wood, "Cache profiling and the spec benchmarks: A case study", IEEE Computer, 27(10):15-26, October 1994
[CrossRef] [Web of Science Times Cited 58] [SCOPUS Times Cited 124]


[9] S. Graham, P. Kessler, and M. McKusick, "gprof: a call graph execution profile", SIGPLAN: Symposium on Compiler Construction, 1982
[CrossRef] [Web of Science Times Cited 57] [SCOPUS Times Cited 62]


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[CrossRef] [Web of Science Times Cited 3] [SCOPUS Times Cited 11]


[12] J. Mauro and R. McDougall, "Solaris Internals - Core Kernel Architecture", Sun Microsystems Press, 2005

[13] J. Hennessy and D. Patterson, "Computer Architecture: A Quantitative Approach", Morgan Kaufmann Publishers, 2007

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[CrossRef] [SCOPUS Times Cited 3]


[15] K. Rupnow, A. Rodrigues, K. Underwood, and K. Compton, "Scientific applications vs. spec-fp: A comparison of program behavior", ICS'06: Proceedings of the 20th ACM International Conference on Supercomputing, Cairns, Australia, 2006
[CrossRef] [SCOPUS Times Cited 12]


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[CrossRef] [SCOPUS Times Cited 26]


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[CrossRef] [SCOPUS Times Cited 32]


[18] R. Bunt and C. Williamson, "Temporal and spatial locality: A time and place for everything", International Symposium in Honour of Professor Guenter Haring's 60th Birthday, 2003

[19] P. Trancoso, J.-L. Larriba-Pey, Z. Zhang, J. Torrellas, "The memory performance of DSS commercial workloads in shared-memory multiprocessors", Proc. of the 3rd IEEE Symp.on High-Performance Computer Architecture (HPCA-3), 1997
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[20] E. Strohmaier and H. Shan, "Architecture independent performance characterization and benchmarking for scientific applications", International Symposium on Modeling, Analysis and Simulation of Computer Telecommunications Systems, 2004
[CrossRef] [Web of Science Times Cited 9] [SCOPUS Times Cited 29]


[21] J. Anderson, L. Berc, J. Dean, S. Ghemawat, M. Henzinger, S. Leung, R. Sites, M. Vandevoorde, C. Waldspruger, and W. Weihl, "Continuous profiling: Where have all the cycles gone?", Proceedings of the 16th ACM Symposium of Operating Systems Principles, October 1997
[CrossRef] [Web of Science Times Cited 105] [SCOPUS Times Cited 174]


[22] R. Cheveresan, M. Ramsay, C. Feucht, I. Sharapov, "Characteristics of Workloads Used in High Performance and Technical Computing", 21st ACM International Conference on Supercomputing (ICS '07), Seattle, WA, June 2007
[CrossRef] [SCOPUS Times Cited 31]


References Weight

Web of Science® Citations for all references: 840 TCR
SCOPUS® Citations for all references: 1,295 TCR

Web of Science® Average Citations per reference: 38 ACR
SCOPUS® Average Citations per reference: 59 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-03-28 20:20 in 87 seconds.




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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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