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Data visualization can help users decipher scientific and engineering data and better comprehend large, complex data sets. The authors present a high-level abstract model for performance visualization that relates behavior abstractions to visual representations in a structured way. This model is based on two principles: Displays of performance information are linked directly to parallel performance models, and performance visualizations are designed and applied in an integrated environment. The authors explain some advantages of adhering to these principles. They begin by establishing a context for users to clearly understand performance information, defining terms such as perspective, semantic context, and subview mapping. Next, they describe the techniques used to scale graphical views as data sets become very large. Finally, they discuss concepts such as user perception and interaction, comparisons and cross-correlations between related views or representations, and information extraction. On the basis of this conceptual foundation, the authors present examples of practical applications for the model. These case studies address topics such as concurrency and communication in data-parallel computation, access patterns for data distributions, and critical paths in parallel computation. The authors conclude by discussing the relationship between performance visualization and general scientific visualization.
Allen D. Malony, Diane T. Rover, Michael T. Heath, "The Visual Display of Parallel Performance Data", Computer, vol. 28, no. , pp. 21-28, November 1995, doi:10.1109/2.471176
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