%0 Conference Paper %1 frey2016auto %A Frey, Steffen %A Ertl, Thomas %B Proceedings of the New York Scientific Data Summit (NYSDS) %D 2016 %I IEEE %K 2016 A02 from:leonkokkoliadis sfbtrr161 visus visus:ertl visus:freysn %P 1-10 %R 10.1109/NYSDS.2016.7747807 %T Auto-Tuning Intermediate Representations for In Situ Visualization %U https://ieeexplore.ieee.org/document/7747807 %X Advances in high-accuracy measurement techniques and parallel computing systems for simulations lead to a widening gapbetween the rate at which data is generated and the rate at which it can be transferred and stored. In situ visualization directly tackles thisissue by processing—and with this reducing—data as soon as it is generated. This allows to create, transmit and store visualizations at amuch higher resolution than what would be possible otherwise with traditional approaches. So-called hybrid in situ visualization is apopular variant that transforms data into an intermediate visualization representation of reduced size. These intermediate representationscondense the original data by applying visualization techniques, but in contrast to the traditional result of a rendered image, they stillpreserve some degrees of freedom for live and a posteriori exploration and analysis. However, the configuration of the involvedprocessing steps requires careful configuration under the consideration of achieved quality and preserved degrees of freedom againstbandwidth and storage resources.To optimize the generation of intermediate representations for hybrid in situ visualization, we present our approach to (1) analyze andquantify the impact of input parameters, and (2) to auto-tune them on this basis under the consideration of different constraints. Wedemonstrate its application and evaluate respective results at the example of Volumetric Depth Images (VDIs), a view-dependentrepresentation for volumetric data. VDIs can quickly and flexibly be generated via a modified volume raycasting procedure that partitionsand partially composits samples along view rays. In particular, we study the impact of respective input parameters on this process w.r.t.the involved quality-space trade-off. We quantify rendering quality via image quality metrics and space requirements via the compressedsize of the intermediate representation. On this basis, we then automatically determine the parameter settings that yield the best qualityunder different constraints. We demonstrate the utility of our approach by means of a variety of different data sets, and show that weoptimize the achieved results without having to rely on tedious and time-consuming manual tweaking.

@inproceedings{frey2016auto, abstract = {Advances in high-accuracy measurement techniques and parallel computing systems for simulations lead to a widening gapbetween the rate at which data is generated and the rate at which it can be transferred and stored. In situ visualization directly tackles thisissue by processing—and with this reducing—data as soon as it is generated. This allows to create, transmit and store visualizations at amuch higher resolution than what would be possible otherwise with traditional approaches. So-called hybrid in situ visualization is apopular variant that transforms data into an intermediate visualization representation of reduced size. These intermediate representationscondense the original data by applying visualization techniques, but in contrast to the traditional result of a rendered image, they stillpreserve some degrees of freedom for live and a posteriori exploration and analysis. However, the configuration of the involvedprocessing steps requires careful configuration under the consideration of achieved quality and preserved degrees of freedom againstbandwidth and storage resources.To optimize the generation of intermediate representations for hybrid in situ visualization, we present our approach to (1) analyze andquantify the impact of input parameters, and (2) to auto-tune them on this basis under the consideration of different constraints. Wedemonstrate its application and evaluate respective results at the example of Volumetric Depth Images (VDIs), a view-dependentrepresentation for volumetric data. VDIs can quickly and flexibly be generated via a modified volume raycasting procedure that partitionsand partially composits samples along view rays. In particular, we study the impact of respective input parameters on this process w.r.t.the involved quality-space trade-off. We quantify rendering quality via image quality metrics and space requirements via the compressedsize of the intermediate representation. On this basis, we then automatically determine the parameter settings that yield the best qualityunder different constraints. We demonstrate the utility of our approach by means of a variety of different data sets, and show that weoptimize the achieved results without having to rely on tedious and time-consuming manual tweaking.}, added-at = {2020-01-15T16:32:57.000+0100}, author = {Frey, Steffen and Ertl, Thomas}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/292bd71ab8fa9e54e6e6a6b6358c3a5a3/sfbtrr161}, booktitle = {Proceedings of the New York Scientific Data Summit (NYSDS)}, doi = {10.1109/NYSDS.2016.7747807}, interhash = {15920ce57aeb023d6cf21a1cf730d29b}, intrahash = {92bd71ab8fa9e54e6e6a6b6358c3a5a3}, keywords = {2016 A02 from:leonkokkoliadis sfbtrr161 visus visus:ertl visus:freysn}, pages = {1-10}, publisher = {IEEE}, timestamp = {2020-10-05T11:55:29.000+0200}, title = {Auto-Tuning Intermediate Representations for In Situ Visualization}, url = {https://ieeexplore.ieee.org/document/7747807}, year = 2016 }