Fast Flow-based Distance Quantification and Interpolation for High-Resolution Density Distributions

Abstract

We present a GPU-targeted algorithm for the efficient direct computation of distances and interpolates between high-resolution density distributions without requiring any kind of intermediate representation like features. It is based on a previously published multi-core approach, and substantially improves its performance already on the same CPU hardware due to algorithmic improvements. As we explicitly target a manycore-friendly algorithm design, we further achieve significant speedups by running on a GPU. This paper quickly reviews the previous approach, and explicitly discusses the analysis of algorithmic characteristics as well as hardware architectural considerations on which our redesign was based. We demonstrate the performance and results of our technique by means of several transitions between volume data sets.