A preview and exploratory technique for large-scale scientific simulations

Successful in-situ and remote visualization solutions must have minimal storage requirements and account for only a small percentage of supercomputing time. One solution that meets these requirements is to store a compact intermediate representation of the data, instead of a 3D volume itself. Recent work explores the use of attenuation functions as a data representation that summarizes the distribution of attenuation along the rays. This representation goes beyond conventional static images and allows users to dynamically explore their data, for example, to change color and opacity parameters, without accessing the original 3D data. The computation and storage costs of this method may still be prohibitively expensive for large and time-varying data sets, thus limiting its applicability in the real-world scenarios. In this paper, we present an efficient algorithm for computing attenuation functions in parallel. We exploit the fact that the distribution of attenuation can be constructed recursively from a hierarchy of blocks or intervals of the data, which is a highly parallelizeable process. We have developed a library of routines that can be used in a distance visualization scenario or can be called directly from a simulation code to generate explorable images in-situ. Through a number of examples, we demonstrate the application of this work to large-scale scientific simulations in a real-world parallel environment with thousands of processors. We also explore various compression methods for reducing the size of the RAF. Finally, we present a method for computing an alternative RAF representation, which more closely encodes the actual distribution of samples along a ray, using kernel density estimation.