An algebra for cross-experiment performance analysis
Fengguang Song, Felix Wolf, Nayan Sanjay Bhatia, Jack J. Dongarra, Shirley Moore · 2004
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 platformindependent 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.