DEUS full observable universe simulations: Numerical challenge and outlooks. (August 2015)

• Record Type: Journal Article
• Authors: Reverdy, Vincent; Alimi, Jean-Michel; Bouillot, Vincent; Rasera, Yann; Corasaniti, Pier-Stefano; Balmès, Irène; Requena, Stéphane; Delaruelle, Xavier; Richet, Jean-Noel
• Is Part Of: International journal of high performance computing applications. Volume 29:Number 3(2015:Autumn)
• Page Start: 249
• Page End: 260
• Publication Date: 2015-08
• Subjects: Supercomputing -- cosmological simulation -- N-body simulation -- dark energy -- dark matter -- exascale; High performance computing -- Periodicals; Supercomputers -- Periodicals; 004.1105
• DOI: 10.1177/1094342015576845
• Languages: English
• ISSNs: 1094-3420

Abstract

We report the realization of the first cosmological simulations on the scale of the whole observable universe. These simulations have been carried out on 4752 nodes of the Curie supercomputer as a part of the Dark Energy Universe Simulation: Full Universe Runs (DEUS-FUR) project which aims at establishing new probes to put constraints on the nature of dark energy by comparing the growth of large-scale structures, the characteristics of extreme statistical events and the matter distribution in redshift space. The numerical challenge of the first DEUS-FUR simulation associated with the concordance ΛCDM (Λ Cold Dark Matter) model was already presented during the 2012 supercomputing conference (Alimi et al., 2012, in The international conference for high performance computing, networking, storage and analysis ). Here we first focus on the numerical aspects of the two new simulations. In practice, each one of these simulations has evolved 550 billion dark matter particles in an adaptive mesh refinement grid, and one of the new simulations has pushed back the total number of grid points from 2000 billion for the ΛCDM model to 2200 billion due to the formation of a larger number of structures. We highlight the optimizations and adjustments required to run such a set of simulations and we then summarize some important lessons learnt for future exascale computing projects.