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, production-ready 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 browser-based, post-hoc, interactive visualization of the summary for decision-making. A study of their weak-scaling on a distributed multi-GPU system is included.
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Thompson, D., Jourdain, S., Bauer, A., Geveci, B., Maynard, R., Vatsavai, R. R., & O’Leary, P. (2017). In situ summarization with VTK-m. In Proceedings of ISAV 2017: In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization - Held in conjunction with SC 2017: The International Conference for High Performance Computing, Networking, Storage and Analysis (pp. 32–36). Association for Computing Machinery, Inc. https://doi.org/10.1145/3144769.3144777
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