AbacusSummit: A Massive Set of High-Accuracy, High-Resolution -Body Simulations
arXiv:2110.11398 · doi:10.1093/mnras/stab2484
Abstract
We present the public data release of the AbacusSummit cosmological -body simulation suite, produced with the -body code on the Summit supercomputer of the Oak Ridge Leadership Computing Facility. achieves median fractional force error at superlative speeds, calculating 70M particle updates per second per node at early times, and 45M particle updates per second per node at late times. The simulation suite totals roughly 60 trillion particles, the core of which is a set of 139 simulations with particle mass in box size . The suite spans 97 cosmological models, including Planck 2018, previous flagship simulation cosmologies, and a linear derivative and cosmic emulator grid. A sub-suite of 1883 boxes of size is available for covariance estimation. AbacusSummit data products span 33 epochs from to and include lightcones, full particle snapshots, halo catalogs, and particle subsets sampled consistently across redshift. AbacusSummit is the largest high-accuracy cosmological -body data set produced to date.
30 pages, 10 figures, 6 tables. Published in MNRAS. Data available at https://abacusnbody.org and https://abacussummit.readthedocs.io/en/latest/data-access.html (DOI: 10.13139/OLCF/1811689)
References in corpus (7)
- The NumPy array: a structure for efficient numerical computation
- Transients from Initial Conditions in Cosmological Simulations
- The Cosmic Linear Anisotropy Solving System (CLASS) I: Overview
- Corrfunc --- A Suite of Blazing Fast Correlation Functions on the CPU
- Prime Focus Spectrograph (PFS) for the Subaru Telescope: Overview, recent progress, and future perspectives
- Dark Sky Simulations: Early Data Release
- Checkpointing with cp: the POSIX Shared Memory System
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