Configurable data prefetching scheme for interactive visualization of large-scale volume data

Abstract

This paper presents a novel data prefetching and memory management scheme to support interactive visualization of large-scale volume datasets using GPU-based isosurface extraction. Our dynamic in-core approach uses a span-space lattice data structure to predict and prefetch the portions of a dataset that are required by isosurface queries, to manage an application-level volume data cache, and to ensure load-balancing for parallel execution. We also present a GPU memory management scheme that enhances isosurface extraction and rendering performance. With these techniques, we achieve rendering performance superior to other in-core algorithms while using dramatically fewer resources.