Adaptive Mesh Refinement for Astrophysics Applications with ParalleX

TL;DR

This paper demonstrates how the ParalleX execution model enhances adaptive mesh refinement in astrophysics simulations by improving scalability and simplifying implementation, enabling efficient handling of large data tables and complex physical models.

Contribution

It introduces the application of the ParalleX execution model to astrophysics, showcasing improved scalability and simplified implementation for adaptive mesh refinement techniques.

Findings

Enhanced strong scaling performance in astrophysics simulations

Seamless overlapping of computation and remote data access

Simplified implementation of large data table management

Abstract

Several applications in astrophysics require adequately resolving many physical and temporal scales which vary over several orders of magnitude. Adaptive mesh refinement techniques address this problem effectively but often result in constrained strong scaling performance. The ParalleX execution model is an experimental execution model that aims to expose new forms of program parallelism and eliminate any global barriers present in a scaling-impaired application such as adaptive mesh refinement. We present two astrophysics applications using the ParalleX execution model: a tabulated equation of state component for neutron star evolutions and a cosmology model evolution. Performance and strong scaling results from both simulations are presented. The tabulated equation of state data are distributed with transparent access over the nodes of the cluster. This allows seamless overlapping of computation with the latencies introduced by the remote access to the table. Because of the expected size increases to the equation of state table, this type of table partitioning for neutron star simulations is essential while the implementation is greatly simplified by ParalleX semantics.