Beyond software performance visualization
M. Abrams, T. J. Lee, H. T. Cadiz, K. Ganugapati · Concurrency Practice and Experience · 1995
Abstract Performance visualization tools of the past decade have yielded new insights into the behavior of sequential, parallel and distributed programs. However, they have three inherent limitations: (1) they only display what happened in one execution of a program (this is dangerous when analyzing concurrent applications, which are prone to non‐deterministic behavior); (2) a human uses one or more bandwidth‐limited senses with a visualization tool (this limits the scalability of a visualization tool); (3) the relationship of ‘interesting’ program events is often separated in time by other events; thus discerning time‐dependent behavior often hinges on finding the ‘right’ visualization—a possibly time‐consuming activity. CHITRA93 complements visualization systems, while alleviating these limitations, and analyzes a set (or ensemble) of traces by combining the visualization of a few traces with a statistical analysis of the entire ensemble (overcoming (1)). It reduces the ensemble to empirical models that capture the time‐dependent relationships of ‘interesting’ program events through application, programming language and computer architecture independent analysis techniques (addressing (2) and (3)). It also incorporates the following transforms, such as aggregation, that simplify the ensemble and reduce the state‐space size of the models generated; a user interface that allows certain transforms to be selected by editing the visualization with a mouse; homogeneity tests that allow partitioning of an ensemble; an efficient semi‐Markov model generation algorithm whose computation time is linear in the sum of the lengths of the traces comprising the ensemble; and a CHAID‐based model that can fathom non‐Markovian relationships among transitions in the traces. The use of CHITRA93 is demonstrated by partitioning ten parallel database traces with nearly 8,000 states into two homogeneous subsets, each modeled by an irreducible, periodic and hierarchical stochastic process with as few as four states.