Load-Balanced Isosurfacing on Multi-GPU Clusters

Abstract

Isosurface extraction is a common technique applied in scientific visualization. Increasing sizes of volumes over which isosurfacing is to be applied combined with increasingly hierarchical parallel architectures present challenges for efficiently distributing isosurfacing work loads. We propose a technique that, with a modest amount of preprocessing, efficiently distributes isosurfacing load to GPU compute resources within a cluster. Load uniformity is maximized over a set of user-defined isovalues, enabling improved scalability over naive, non-data-centric, work distribution approaches.