Scalable Parallel Feature Extraction and Tracking for Large Time-varying 3D Volume Data

Abstract

Large-scale time-varying volume data sets can take terabytes to petabytes of storage space to store and process. One promising approach is to process the data in parallel, and then extract and analyze only features of interest, reducing required memory space by several orders of magnitude for following visualization tasks. However, extracting volume features in parallel is a non-trivial task as features might span over multiple processors, and local partial features are only visible within their own processors. In this paper, we discuss how to generate and maintain connectivity information of features across different processors. Based on the connectivity information, partial features can be integrated, which makes it possible to extract and track features for large data in parallel. We demonstrate the effectiveness and scalability of our approach using two data sets with up to 16384 processors.