guppy i: a code for reducing the storage requirements of cosmological simulations

Author:

Mansfield Philip123ORCID,Abel Tom123

Affiliation:

1. Kavli Institute for Particle Astrophysics and Cosmology, Stanford University , 452 Lomita Mall, Stanford, CA 94305 , USA

2. Department of Physics, Stanford University , 382 Via Pueblo Mall, Stanford, CA 94305 , USA

3. SLAC National Accelerator Laboratory , 2575 Sand Hill Road, Menlo Park, CA 94025 , USA

Abstract

ABSTRACT As cosmological simulations have grown in size, the permanent storage requirements of their particle data have also grown. Even modest simulations present a major logistical challenge for the groups which run these boxes and researchers without access to high performance computing facilities often need to restrict their analysis to lower quality data. In this paper, we present guppy, a compression algorithm and code base tailored to reduce the sizes of dark matter-only cosmological simulations by approximately an order of magnitude. guppy is a ‘lossy’ algorithm, meaning that it injects a small amount of controlled and uncorrelated noise into particle properties. We perform extensive tests on the impact that this noise has on the internal structure of dark matter haloes, and identify conservative accuracy limits which ensure that compression has no practical impact on single-snapshot halo properties, profiles, and abundances. We also release functional prototype libraries in C, Python, and Go for reading and creating guppy data.

Funder

U.S. Department of Energy

Publisher

Oxford University Press (OUP)

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