Scalable Isosurface Visualization of Massive Datasets on COTS Clusters

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Scalable Isosurface Visualization of Massive Datasets on COTS Clusters (2001)

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by Xiaoyu Zhang , Chandrajit Bajaj , William Blanke

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23 - 4 self

BibTeX

@MISC{Zhang01scalableisosurface,
    author = {Xiaoyu Zhang and Chandrajit Bajaj and William Blanke},
    title = {Scalable Isosurface Visualization of Massive Datasets on COTS Clusters},
    year = {2001}
}

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Abstract

Our scalable isosurface visualization solution on a commodity offthe -shelf cluster is an end-to-end parallel and progressive platform, from the initial data access to the final display. In this paper we focus on the back end scalability by introducing a fully parallel and out-of-core isosurface extraction algorithm. It partitions the volume data according to its workload spectrum for load balancing and creates an I/O-optimal external interval tree to minimize the number of I/O operations of loading large data from disk. It achieves scalability by using both parallel processing and parallel disks. Interactive browsing of extracted isosurfaces is made possible by using parallel isosurface extraction and rendering in conjunction with a new specialized piece of image compositing hardware called the Metabuffer. We also describe an isosurface compression scheme that is efficient for isosurface processing.

Keyphrases

cot cluster scalable isosurface visualization massive datasets out-of-core isosurface extraction algorithm isosurface processing parallel processing extracted isosurfaces load balancing volume data final display large data back end scalability parallel disk commodity offthe shelf cluster progressive platform scalable isosurface visualization solution o-optimal external interval tree isosurface compression scheme initial data access interactive browsing workload spectrum end-to-end parallel parallel isosurface extraction new specialized piece

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