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

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.
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  1. Performance Research Laboratory, Computer and Information Science Department, University of Oregon, Eugene, OR 97403, USA
Publication Date:
Grant/Contract Number:
FG02-07ER25826; FG02-05ER25680
Published Article
Journal Name:
Scientific Programming
Additional Journal Information:
Journal Volume: 16; Journal Issue: 2-3; Related Information: CHORUS Timestamp: 2016-08-23 12:17:06; Journal ID: ISSN 1058-9244
Hindawi Publishing Corporation
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