An algebra for cross-experiment performance analysis

Performance tuning of parallel applications usually involves multiple experiments to compare the effects of different optimization strategies. This article describes an algebra that can be used to compare, integrate, and summarize performance data from multiple sources. The algebra consists of a dat...

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Bibliographic Details
Published inInternational Conference on Parallel Processing, 2004. ICPP 2004 pp. 63 - 72 vol.1
Main Authors Song, F., Wolf, F., Bhatia, N., Dongarra, J., Moore, S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2004
Subjects
Algebra
Analytical models
Arithmetic
Data models
Data visualization
Displays
Hardware
Lifting equipment
Optimization
Performance analysis
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ISBN9780769521978
0769521975
ISSN0190-3918
DOI10.1109/ICPP.2004.1327905

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Summary:Performance tuning of parallel applications usually involves multiple experiments to compare the effects of different optimization strategies. This article describes an algebra that can be used to compare, integrate, and summarize performance data from multiple sources. The algebra consists of a data model to represent the data in a platform-independent fashion plus arithmetic operations to merge, subtract, and average the data from different experiments. A distinctive feature of this approach is its closure property, which allows processing and viewing all instances of the data model in the same way - regardless of whether they represent original or derived data - in addition to an arbitrary and easy composition of operations.
ISBN:9780769521978
0769521975
ISSN:0190-3918
DOI:10.1109/ICPP.2004.1327905