Cosmological Simulations on a Grid of Computers

TL;DR

This paper presents a grid-based computational framework for efficiently exploring parameter spaces in cosmological simulations, utilizing middleware to manage resources and data, thereby optimizing the semi-analytical models in galaxy formation.

Contribution

It introduces a grid computing approach with middleware to perform large-scale simulations, enabling empirical parameter space exploration for galaxy formation models.

Findings

Successful implementation of grid middleware for cosmological simulations

Identification of key parameters influencing galaxy formation results

Efficient management of computational resources for large parameter sweeps

Abstract

The work presented in this paper aims at restricting the input parameter values of the semi-analytical model used in GALICS and MOMAF, so as to derive which parameters influence the most the results, e.g., star formation, feedback and halo recycling efficiencies, etc. Our approach is to proceed empirically: we run lots of simulations and derive the correct ranges of values. The computation time needed is so large, that we need to run on a grid of computers. Hence, we model GALICS and MOMAF execution time and output files size, and run the simulation using a grid middleware: DIET. All the complexity of accessing resources, scheduling simulations and managing data is harnessed by DIET and hidden behind a web portal accessible to the users.