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JCR Impact Factor: 0.595
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Issues per year: 4
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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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ABC Algorithm based Fuzzy Modeling of Optical Glucose Detection, SARACOGLU, O. G., BAGIS, A., KONAR, M., TABARU, T. E.
Issue 3/2016

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LATEST NEWS

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Apr-04
We have the confirmation Advances in Electrical and Computer Engineering will be included in the EBSCO database.

2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

2017-Jan-30
We have the confirmation Advances in Electrical and Computer Engineering will be included in the Gale database.

2016-Dec-17
IoT is a new emerging technology domain which will be used to connect all objects through the Internet for remote sensing and control. IoT uses a combination of WSN (Wireless Sensor Network), M2M (Machine to Machine), robotics, wireless networking, Internet technologies, and Smart Devices. We dedicate a special section of Issue 2/2017 to IoT. Prospective authors are asked to make the submissions for this section no later than the 31st of March 2017, placing "IoT - " before the paper title in OpenConf.

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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
 
Click to see author's profile on See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (361 KB) | Citation | Downloads: 810 | Views: 3,286

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

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[CrossRef] [Web of Science Times Cited 316] [SCOPUS Times Cited 467]


[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]


[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

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[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 50] [SCOPUS Times Cited 102]


[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 27] [SCOPUS Times Cited 34]


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[CrossRef] [Web of Science Times Cited 3] [SCOPUS Times Cited 10]


[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

[14] J. Weinberg, M. McCracken, A. Snavely, and E. Strohmair, "Quantifying locality in the memory access patterns of HPC applications", Supercomputing, 2005
[CrossRef] [SCOPUS Times Cited 32]


[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 11]


[16] F. Darema-Rogers, G. Pfister, and K. So, "Memory access patterns of parallel scientific programs", ACM SIGMETRICS Conference on Measurement and Modeling of Computer Systems, pages 46-58. ACM Press, 1987
[CrossRef]


[17] L. Carrington, A. Snavely, X. Gao, and N. Wolter, "A Performance prediction framework for scientic applications", Lecture Notes in Computer Science, 2659, pages 926-935. Springer, January 2003
[CrossRef]


[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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[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 23]


[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 86]


[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 22]


References Weight

Web of Science® Citations for all references: 499 TCR
SCOPUS® Citations for all references: 701 TCR

Web of Science® Average Citations per reference: 23 ACR
SCOPUS® Average Citations per reference: 32 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 2017-11-14 05:35 in 82 seconds.




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


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