As the computation power of supercomputers increases, so does simulation size, which in turn produces orders-of-magnitude more data. Because generated data often exceed the simulation's disk quota, many simulations would stand to benefit from data-reduction techniques to reduce storage requirements. Such techniques include autoencoders, data compression algorithms, and sampling. Lossy compression techniques can significantly reduce data size, but such techniques come at the expense of losing information that could result in incorrect post hoc analysis results. To help scientists determine the best compression they can get while keeping their analyses accurate, we have developed Foresight, an analysis framework that enables users to evaluate how different data-reduction techniques will impact their analyses. We use particle data from a cosmology simulation, turbulence data from Direct Numerical Simulation, and asteroid impact data from xRage to demonstrate how Foresight can help scientists determine the best data-reduction technique for their simulations.

Abstract We describe the parallel performance of the pure Java CartaBlanca code on heat transfer and multiphase fluid flow problems. CartaBlanca is designed for parallel computations on partitioned unstructured meshes. It uses Java's thread facility to manage computations on each of the mesh partitions. Inter‐partition communications are handled by two compact objects for node‐by‐node communication along partition boundaries and for global reduction calculations across the entire mesh. For distributed calculations, the JavaParty package from the University of Karlsruhe is demonstrated to work with CartaBlanca. Copyright © 2004 John Wiley & Sons, Ltd.