Scalable analysis techniques for microprocessor performance counter metrics

Dong H. Ahn; Jeffrey S. Vetter

doi:10.1109/SC.2002.10066

Scalable analysis techniques for microprocessor performance counter metrics

Dong H. Ahn, Jeffrey S. Vetter

Computer Science and Mathematics Div

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

31 Scopus citations

Abstract

Contemporary microprocessors provide a rich set of integrated performance counters that allow application developers and system architects alike the opportunity to gather important information about workload behaviors. Current techniques for analyzing data produced from these counters use raw counts, ratios, and visualization techniques help users make decisions about their application performance. While these techniques are appropriate for analyzing data from one process, they do not scale easily to new levels demanded by contemporary computing systems. Very simply, this paper addresses these concerns by evaluating several multivariate statistical techniques on these datasets. We find that several techniques, such as statistical clustering, can automatically extract important features from the data. These derived results can, in turn, be fed directly back to an application developer, or used as input to a more comprehensive performance analysis environment, such as a visualization or an expert system.

Original language	English
Title of host publication	Proceedings of the IEEE/ACM SC 2002 Conference, SC 2002
Publisher	Association for Computing Machinery
ISBN (Electronic)	076951524X
DOIs	https://doi.org/10.1109/SC.2002.10066
State	Published - 2002
Event	2002 IEEE/ACM Conference on Supercomputing, SC 2002 - Baltimore, United States Duration: Nov 16 2002 → Nov 22 2002

Publication series

Name	Proceedings of the International Conference on Supercomputing
Volume	2002-November

Conference

Conference	2002 IEEE/ACM Conference on Supercomputing, SC 2002
Country/Territory	United States
City	Baltimore
Period	11/16/02 → 11/22/02

Funding

This work was performed under the auspices of the U.S. Department of Energy by the University of California, Lawrence Livermore National Laboratory under contract No. W-7405-Eng-48. T his paper is available as LLNL T echnical Report UCRL-JC-148058.

Access to Document

10.1109/SC.2002.10066

Cite this

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title = "Scalable analysis techniques for microprocessor performance counter metrics",

abstract = "Contemporary microprocessors provide a rich set of integrated performance counters that allow application developers and system architects alike the opportunity to gather important information about workload behaviors. Current techniques for analyzing data produced from these counters use raw counts, ratios, and visualization techniques help users make decisions about their application performance. While these techniques are appropriate for analyzing data from one process, they do not scale easily to new levels demanded by contemporary computing systems. Very simply, this paper addresses these concerns by evaluating several multivariate statistical techniques on these datasets. We find that several techniques, such as statistical clustering, can automatically extract important features from the data. These derived results can, in turn, be fed directly back to an application developer, or used as input to a more comprehensive performance analysis environment, such as a visualization or an expert system.",

author = "Ahn, {Dong H.} and Vetter, {Jeffrey S.}",

note = "Publisher Copyright: {\textcopyright} 2002 IEEE.; 2002 IEEE/ACM Conference on Supercomputing, SC 2002 ; Conference date: 16-11-2002 Through 22-11-2002",

year = "2002",

doi = "10.1109/SC.2002.10066",

language = "English",

series = "Proceedings of the International Conference on Supercomputing",

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Ahn, DH & Vetter, JS 2002, Scalable analysis techniques for microprocessor performance counter metrics. in Proceedings of the IEEE/ACM SC 2002 Conference, SC 2002. Proceedings of the International Conference on Supercomputing, vol. 2002-November, Association for Computing Machinery, 2002 IEEE/ACM Conference on Supercomputing, SC 2002, Baltimore, United States, 11/16/02. https://doi.org/10.1109/SC.2002.10066

Scalable analysis techniques for microprocessor performance counter metrics. / Ahn, Dong H.; Vetter, Jeffrey S.
Proceedings of the IEEE/ACM SC 2002 Conference, SC 2002. Association for Computing Machinery, 2002. (Proceedings of the International Conference on Supercomputing; Vol. 2002-November).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Ahn, Dong H.

AU - Vetter, Jeffrey S.

PY - 2002

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N2 - Contemporary microprocessors provide a rich set of integrated performance counters that allow application developers and system architects alike the opportunity to gather important information about workload behaviors. Current techniques for analyzing data produced from these counters use raw counts, ratios, and visualization techniques help users make decisions about their application performance. While these techniques are appropriate for analyzing data from one process, they do not scale easily to new levels demanded by contemporary computing systems. Very simply, this paper addresses these concerns by evaluating several multivariate statistical techniques on these datasets. We find that several techniques, such as statistical clustering, can automatically extract important features from the data. These derived results can, in turn, be fed directly back to an application developer, or used as input to a more comprehensive performance analysis environment, such as a visualization or an expert system.

AB - Contemporary microprocessors provide a rich set of integrated performance counters that allow application developers and system architects alike the opportunity to gather important information about workload behaviors. Current techniques for analyzing data produced from these counters use raw counts, ratios, and visualization techniques help users make decisions about their application performance. While these techniques are appropriate for analyzing data from one process, they do not scale easily to new levels demanded by contemporary computing systems. Very simply, this paper addresses these concerns by evaluating several multivariate statistical techniques on these datasets. We find that several techniques, such as statistical clustering, can automatically extract important features from the data. These derived results can, in turn, be fed directly back to an application developer, or used as input to a more comprehensive performance analysis environment, such as a visualization or an expert system.

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ER -

Scalable analysis techniques for microprocessor performance counter metrics

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