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Title: Knowledge Support and Automation for Performance Analysis with PerfExplorer 2.0

Abstract

The integration of scalable performance analysis in parallel development tools is difficult. The potential size of data sets and the need to compare results from multiple experiments presents a challenge to manage and process the information. Simply to characterize the performance of parallel applications running on potentially hundreds of thousands of processor cores requires new scalable analysis techniques. Furthermore, many exploratory analysis processes are repeatable and could be automated, but are now implemented as manual procedures. In this paper, we will discuss the current version of PerfExplorer, a performance analysis framework which provides dimension reduction, clustering and correlation analysis of individual trails of large dimensions, and can perform relative performance analysis between multiple application executions. PerfExplorer analysis processes can be captured in the form of Python scripts, automating what would otherwise be time-consuming tasks. We will give examples of large-scale analysis results, and discuss the future development of the framework, including the encoding and processing of expert performance rules, and the increasing use of performance metadata.

Authors:
 [1];  [1];  [1];  [1]
  1. Performance Research Laboratory, Computer and Information Science Department, University of Oregon, Eugene, OR 97403, USA
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1197990
Grant/Contract Number:  
FG02-07ER25826; FG02-05ER25680
Resource Type:
Published Article
Journal Name:
Scientific Programming
Additional Journal Information:
Journal Name: Scientific Programming Journal Volume: 16 Journal Issue: 2-3; Journal ID: ISSN 1058-9244
Publisher:
Hindawi Publishing Corporation
Country of Publication:
Egypt
Language:
English

Citation Formats

Huck, Kevin A., Malony, Allen D., Shende, Sameer, and Morris, Alan. Knowledge Support and Automation for Performance Analysis with PerfExplorer 2.0. Egypt: N. p., 2008. Web. doi:10.1155/2008/985194.
Huck, Kevin A., Malony, Allen D., Shende, Sameer, & Morris, Alan. Knowledge Support and Automation for Performance Analysis with PerfExplorer 2.0. Egypt. doi:10.1155/2008/985194.
Huck, Kevin A., Malony, Allen D., Shende, Sameer, and Morris, Alan. Tue . "Knowledge Support and Automation for Performance Analysis with PerfExplorer 2.0". Egypt. doi:10.1155/2008/985194.
@article{osti_1197990,
title = {Knowledge Support and Automation for Performance Analysis with PerfExplorer 2.0},
author = {Huck, Kevin A. and Malony, Allen D. and Shende, Sameer and Morris, Alan},
abstractNote = {The integration of scalable performance analysis in parallel development tools is difficult. The potential size of data sets and the need to compare results from multiple experiments presents a challenge to manage and process the information. Simply to characterize the performance of parallel applications running on potentially hundreds of thousands of processor cores requires new scalable analysis techniques. Furthermore, many exploratory analysis processes are repeatable and could be automated, but are now implemented as manual procedures. In this paper, we will discuss the current version of PerfExplorer, a performance analysis framework which provides dimension reduction, clustering and correlation analysis of individual trails of large dimensions, and can perform relative performance analysis between multiple application executions. PerfExplorer analysis processes can be captured in the form of Python scripts, automating what would otherwise be time-consuming tasks. We will give examples of large-scale analysis results, and discuss the future development of the framework, including the encoding and processing of expert performance rules, and the increasing use of performance metadata.},
doi = {10.1155/2008/985194},
journal = {Scientific Programming},
number = 2-3,
volume = 16,
place = {Egypt},
year = {2008},
month = {1}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1155/2008/985194

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