An Integrated Science Environment for Astrophysical Simulations

Turk Matthew J, La Jolla CA

Investigators

Abstract

Astrophysical simulation codes, as well as astrophysical data, have grown increasingly complex: simulations are now able to probe the formation of stars, galaxies, black holes and many other disparate phenomena. However, as simulations become larger, and the physical models governing those simulations become more complex, the inherent difficulties in developing simulation codes and analyzing the data output from these simulations grow commensurately. Many different, often competing, groups utilize independent simulation platforms, which prevents substantial collaboration as a result of technical incompatibilities. To mitigate this fragmentation, this research involves the creation of an Integrated Science Environment for both astrophysical computation and visualization. This Integrated Science Environment is designed to work equally well for new users as well as for petascale simulations on platforms such as the NSF-funded Blue Waters. The Integrated Science Environment produced by this research is constructed out of three primary components: a simulation platform interface, an initial conditions generator, and an analysis and visualization engine. The simulation platform interface abstracts the internal data structures and unit-handling of individual simulation platforms into physically-relevant quantities, providing a compatibility layer enabling microphysical solvers (such as chemistry, radiative cooling, and hydrodynamics) to be applied to multiple platforms unmodified. The initial conditions generator creates the starting points for astrophysical simulations, enabling both intuitive initial conditions generation and straightforward cross-code comparison and validation of results. Finally, the analysis engine produces high-quality quantitative results and visualizations, including planetarium-quality visualization tools. The Integrated Science Environment is fully MPI-parallelized and is written primarily in Python with APIs for in situ or concurrent analysis and visualization to be conducted during the course of a simulation, scaling up to hundreds of thousands of processors.