Topological Analysis and Visualization of Cyclical Behavior in Memory Reference Traces

Abstract

We demonstrate the application of topological analysis techniques to the rather unexpected domain of software visualization. We collect a memory reference trace from a running program, recasting the linear flow of trace records as a high-dimensional point cloud in a metric space. We use topological persistence to automatically detect significant circular structures in the point cloud, which represent recurrent or cyclical runtime program behaviors. We visualize such recurrences using radial plots to display their time evolution, offering multi-scale visual insights, and detecting potential candidates for memory performance optimization. We then present several case studies to demonstrate some key insights obtained using our techniques.

Keywords: automatic circular structure detection; cyclical behavior visualization; cyclical runtime program behaviors; high-dimensional point cloud; memory performance optimization; memory reference traces; metric space; multiscale visual insights; radial plots; recurrence visualization; software visualization; topological analysis; trace record linear flow recasting; data visualisation; performance evaluation; storage management; topology;

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Citation

A.N.M. Imroz Choudhury, Bei Wang, Paul Rosen, and Valerio Pascucci. Topological analysis and visualization of cyclical behavior in memory reference traces. In IEEE Pacific Visualization Symposium, PacificVis, pages 9 --16, March 2012. [ DOI ]

Bibtex


@inproceedings{Choudhury.2012.PV,
  author = {{\relax A.N.M. Imroz} Choudhury and Bei Wang and Paul Rosen 
  	and Valerio Pascucci},
  title = {Topological Analysis and Visualization of Cyclical Behavior in Memory
	Reference Traces},
  booktitle = {IEEE Pacific Visualization Symposium},
  year = {2012},
  series = {PacificVis},
  pages = {9 -16},
  month = {March},
  abstract = {We demonstrate the application of topological analysis techniques
	to the rather unexpected domain of software visualization. We collect
	a memory reference trace from a running program, recasting the linear
	flow of trace records as a high-dimensional point cloud in a metric
	space. We use topological persistence to automatically detect significant
	circular structures in the point cloud, which represent recurrent
	or cyclical runtime program behaviors. We visualize such recurrences
	using radial plots to display their time evolution, offering multi-scale
	visual insights, and detecting potential candidates for memory performance
	optimization. We then present several case studies to demonstrate
	some key insights obtained using our techniques.},
  doi = {10.1109/PacificVis.2012.6183557},
  issn = {2165-8765},
  keywords = {automatic circular structure detection; cyclical behavior visualization;
	cyclical runtime program behaviors; high-dimensional point cloud;
	memory performance optimization; memory reference traces; metric
	space; multiscale visual insights; radial plots; recurrence visualization;
	software visualization; topological analysis; trace record linear
	flow recasting; data visualisation; performance evaluation; storage
	management; topology;}
}