In Situ Summarization with VTK-m

Abstract

Summarization and compression at current and future scales requires a framework for developing and benchmarking algorithms. We present a framework created by integrating existing, productionready projects and provide timings of two particular algorithms that serve as exemplars for summarization: a wavelet-based data reduction filter and a generator for creating image-like databases of extracted features (isocontours in this case). Both support browserbased, post-hoc, interactive visualization of the summary for decisionmaking. A study of their weak-scaling on a distributed multi-GPU system is included.