FunM^2C: A Filter for Uncertainty Visualization of Multivariate Data on Multi-Core Devices

Fast forward

Abstract

Uncertainty visualization is an emerging research topic in data vi- sualization because neglecting uncertainty in visualization can lead to inaccurate assessments. In this short paper, we study the prop- agation of multivariate data uncertainty in visualization. Although there have been a few advancements in probabilistic uncertainty vi- sualization of multivariate data, three critical challenges remain to be addressed. First, state-of-the-art probabilistic uncertainty visual- ization framework is limited to bivariate data (two variables). Sec- ond, the existing uncertainty visualization algorithms use compu- tationally intensive techniques and lack support for cross-platform portability. Third, as a consequence of the computational expense, integration into interactive production visualization tools is imprac- tical. In this work, we address all three issues and make a threefold contribution. First, we generalize the state-of-the-art probabilis- tic framework for bivariate data to multivariate data with a arbi- trary number of variables. Second, through utilization of VTK-m’s shared-memory parallelism and cross-platform compatibility fea- tures, we demonstrate acceleration of multivariate uncertainty visu- alization on different many-core architectures, including OpenMP and AMD GPUs. Third, we demonstrate the integration of our al- gorithms with the ParaView software. We demonstrate utility of our algorithms through experiments on multivariate simulation data.