Scalable isosurface visualization of massive datasets on COTS clusters

Abstract

Our scalable isosurface visualization solution on a commodity off-the-shelf (COTS) cluster is an end-to-end parallel and progressive platform, from the initial data access to the final display. 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 specialized piece of image compositing hardware called the Metabuffer. We also describe an isosurface compression scheme that is efficient for isosurface processing.