Tsai, Karen C.Bujack, RoxanaGeveci, BerkAyachit, UtkarshAhrens, JamesFrey, Steffen and Huang, Jian and Sadlo, Filip2020-05-242020-05-242020978-3-03868-107-61727-348Xhttps://doi.org/10.2312/pgv.20201075https://diglib.eg.org:443/handle/10.2312/pgv20201075Feature-driven in situ data reduction can overcome the I/O bottleneck that large simulations face in modern supercomputer architectures in a semantically meaningful way. In this work, we make use of pattern detection as a black box detector of arbitrary feature templates of interest. In particular, we use moment invariants because they allow pattern detection independent of the specific orientation of a feature. We provide two open source implementations of a rotation invariant pattern detection algorithm for high performance computing (HPC) clusters with a distributed memory environment. The first one is a straightforward integration approach. The second one makes use of the Fourier transform and the Cross-Correlation Theorem. In this paper, we will compare the two approaches with respect to performance and flexibility and showcase results of the in situ integration with real world simulation code.Attribution 4.0 International LicenseHuman centered computingVisualizationTheory of computationParallel algorithmsPattern matching10.2312/pgv.2020107557-68