Auto-tuning intermediate representations for in situ visualization
Steffen Frey, Thomas Ertl · 2016
Advances in high-accuracy measurement techniques and parallel computing systems for simulations lead to a widening gap between the rate at which data is generated and the rate at which it can be transferred and stored. In situ visualization directly tackles this issue by processing - and with this reducing - data as soon as it is generated. This allows to create, transmit and store visualizations at a much higher resolution than what would be possible otherwise with traditional approaches. So-called hybrid in situ visualization is a popular variant that transforms data into an intermediate visualization representation of reduced size. These intermediate representations condense the original data by applying visualization techniques, but in contrast to the traditional result of a rendered image, they still preserve some degrees of freedom for live and a posteriori exploration and analysis. However, the configuration of the involved processing steps requires careful configuration under the consideration of achieved quality and preserved degrees of freedom against bandwidth and storage resources. To optimize the generation of intermediate representations for hybrid in situ visualization, we present our approach to (1) analyze and quantify the impact of input parameters, and (2) to auto-tune them on this basis under the consideration of different constraints. We demonstrate its application and evaluate respective results at the example of Volumetric Depth Images (VDIs), a view-dependent representation for volumetric data. VDIs can quickly and flexibly be generated via a modified volume raycasting procedure that partitions and 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 compressed size of the intermediate representation. On this basis, we then automatically determine the parameter settings that yield the best quality under different constraints. We demonstrate the utility of our approach by means of a variety of different data sets, and show that we optimize the achieved results without having to rely on tedious and time-consuming manual tweaking.