In-situ Feature-based Objects Tracking for Large-Scale Scientific Simulations
Emerging scientific simulations on leadership class systems are generating huge amounts of data. However, the increasing gap between computation and disk IO speeds makes traditional data analytics pipelines based on post-processing cost prohibitive and often infeasible. In this paper, the authors investigate an alternate approach that aims to bring the analytics closer to the data using data staging and the in-situ execution of data analysis operations. Specifically, they present the design, implementation and evaluation of a framework that can support in-situ feature based object tracking on distributed scientific datasets.