Histogram-based I/O optimization for visualizing large-scale data

Authors:
Yuan Hong;Tom Peterka;Han-Wei Shen
Affiliations:
The Ohio State University, Columbus, OH;Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL;The Ohio State University, Columbus, OH
Venue:
Proceedings of the 2009 Workshop on Ultrascale Visualization
Year:
2009

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Abstract

We present an I/O optimization method for parallel volume rendering based on visibility and spatial locality. The combined metric is used to organize the file layout of the dataset on a parallel file system. This reduces the number of small, noncontiguous I/O operations and improves load balance among I/O servers. The net result is reduced I/O time. Since large-scale visualization is data-intensive, overall visualization performance improves using this method. This paper explains the preprocessing of data blocks to compute feature vectors and the storage organization based on them. Run-time performance is analyzed with a variety of transfer functions, view directions, system scales, and datasets. Our results show significant performance gains over file layouts based on space-filling curves.