Parallel and Out-of-core View-dependent Isocontour Visualization Using Random Data Distribution

Abstract

In this paper we describe a parallel and out-of-core view-dependent isocontour visualization algorithm that efficiently extracts and renders the visible portions of an isosurface from large datasets. The algorithm first creates an occlusion map using ray-casting and nearest neighbors. With the occlusion map constructed, the visible portion of the isosurface is extracted and rendered. All steps are in a single pass with minimal communication overhead. The overall workload is well balanced among parallel processors using random data distribution. Volumetric datasets are statically partitioned onto the local disks of each processor and loaded only when necessary. This out-of-core feature allows it to handle scalably large datasets. We additionally demonstrate significant speedup of the view-dependent isocontour visualization on a commodity off-the-shelf PC cluster.