Eurographics Symposium on Parallel Graphics and Visualization (2011), pp. 1–11 T. Kuhlen, R. Pajarola, and K. Zhou (Editors) A Preview and Exploratory Technique for Large-Scale Scientific Simulations Anna Tikhonova 1 and Hongfeng Yu 2 and Carlos D. Correa 3 and Jacqueline H. Chen 2 and Kwan-Liu Ma 1 1 University of California, Davis 2 Sandia National Laboratories 3 Lawrence Livermore National Laboratory Abstract 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. In addition to compression techniques, it is necessary to develop new intermediate data representations that exploit the manner in which samples are composited to generate an image. Recent work proposes 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 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 in the data, thus making the process highly parallelizeable. 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-worldparallel environment with thousands of processors. We also explore various compression methods for reducing the size of the RAF and propose the use of kernel density estimation to compute an alternative RAF representation, which more closely represents the actual distribution of samples along a ray. Categories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics]: Three-Dimensional Graphics and Realism—Color, shading, shadowing, and texture 1. Introduction The growing power of parallel supercomputers has en- abled scientists to model increasingly complex physical phe- nomena. Scientific simulations generate output that is usu- ally volumetric, large-scale, multi-dimensional, and time- varying. Visualizing such data sets may be a time-consuming and a resource-intensive task. For example, the data may be unstructured or the rendering might need to be performed at high-resolution or it might employ an expensive lighting model, such as global illumination. This problem is exacer- bated when the data is visualized remotely or when the ren- dering or interaction with the data is performed on low-end devices. A large body of work is dedicated to reducing the amount of data that needs to be generated, stored, transferred over the network, and accessed to visualize a data set. Some of the recent and practical methods include in-situ and distance vi- sualization. When the data is visualized in-situ, it is rendered while the simulation is running. In this case, only an image of the data is stored and not the 3D data itself, which results in significant space savings. When the visualization is per- formed remotely, a powerful cluster performs the expensive rendering computations. Then, only the result of these com- putations - an image - is served to the user. In both of these approaches, the user is presented with a static image. The lack of explicit 3D information in a static image prevents the user from changing the properties of the data depicted in it. submitted to Eurographics Symposium on Parallel Graphics and Visualization (2011)