Visualization of Large Scale Time-Varying Particles Data from Cosmology

Time-varying data visualization is a fundamental way of understanding the formation and evolution process of the cosmology structures. In our terascale time-varying cosmology data visualization, we present a statistic based tone mapping algorithm for the data with extreme high dynamic range both in spatial and time dimensions, with which we gained novel tone-coherent visualization results. At the mean time, when reconstructing the evolution process, as the data in one time step are distributed on 2048 files in Hilbert space filling curve way, we found that quite a part of the data files are invisible in most visualization, thus we proposed a visibility culling algorithm for the interpolated data based on the nearest key frame pair. Our algorithm puts the union set of visible file IDs as the culling result, through which we dramatically reduce the computation, memory consumption and I/O operations. The culling process is efficiently done by using the Hilbert hash cell. Last, we analyze the performance and image quality.Our visualization results show that algorithms proposed here are great help for efficiently expressing forming detail of the cosmology structure, and also great help for gaining insight into the evolution of the Universe.