State-of-the-art in large-scale volume visualization beyond structured data

Abstract

Volume data these days is usually massive in terms of its topology, multiple fields, or temporal component. With the gap between compute and memory performance widening, the memory subsystem becomes the primary bottleneck for scientific volume visualization. Simple, structured, regular representations are often infeasible because the buses and interconnects involved need to accommodate the data required for interactive rendering. In this state-of-the-art report, we review works focusing on large-scale volume rendering beyond those typical structured and regular grid representations. We focus primarily on hierarchical and adaptive mesh refinement representations, unstructured meshes, and compressed representations that gained recent popularity. We review works that approach this kind of data using strategies such as out-of-core rendering, massive parallelism, and other strategies to cope with the sheer size of the ever-increasing volume of data produced by today's supercomputers and acquisition devices. We emphasize the data management side of large-scale volume rendering systems and also include a review of tools that support the various volume data types discussed.