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Title: nbodykit: An Open-source, Massively Parallel Toolkit for Large-scale Structure

Abstract

We present nbodykit, an open-source, massively parallel Python toolkit for analyzing large-scale structure (LSS) data. Using Python bindings of the Message Passing Interface, we provide parallel implementations of many commonly used algorithms in LSS. nbodykit is both an interactive and scalable piece of scientific software, performing well in a supercomputing environment while still taking advantage of the interactive tools provided by the Python ecosystem. Existing functionality includes estimators of the power spectrum, two- and three-point correlation functions, a friends-of-friends grouping algorithm, mock catalog creation via the halo occupation distribution technique, and approximate N-body simulations via the FastPM scheme. The package also provides a set of distributed data containers, insulated from the algorithms themselves, that enables nbodykit to provide a unified treatment of both simulation and observational data sets. nbodykit can be easily deployed in a high-performance computing environment, overcoming some of the traditional difficulties of using Python on supercomputers. We provide performance benchmarks illustrating the scalability of the software. The modular, component-based approach of nbodykit allows researchers to easily build complex applications using its tools. The package is extensively documented at http://nbodykit.readthedocs.io, which also includes an interactive set of example recipes for new users to explore. As open-source software, wemore » hope nbodykit provides a common framework for the community to use and develop in confronting the analysis challenges of future LSS surveys.« less

Authors:
ORCiD logo [1];  [2];  [3];  [4];  [5];  [5];  [6]
+ Show Author Affiliations
  1. Univ. of California, Berkeley, CA (United States). Astronomy Dept.; Univ. of California, Berkeley, CA (United States). Berkeley Center for Cosmological Physics
  2. Univ. of California, Berkeley, CA (United States). Berkeley Center for Cosmological Physics
  3. Univ. of Portsmouth (United Kingdom). Institute of Cosmology & Gravitation; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  4. Univ. of California, Berkeley, CA (United States). Berkeley Center for Cosmological Physics; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of California, Berkeley, CA (United States). Physics Dept. ; Univ. of Tokyo, Chiba (Japan). Kavli Institue for the Physics and Mathematics of the Universe
  5. Univ. of California, Berkeley, CA (United States). Berkeley Center for Cosmological Physics; Univ. of California, Berkeley, CA (United States). Physics Dept.
  6. Univ. of California, Berkeley, CA (United States). Berkeley Center for Cosmological Physics; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC); Univ. of California, Oakland, CA (United States); Oak Ridge Associated Univ., Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1559157
Grant/Contract Number:  
AC02-05CH11231; SC0014664
Resource Type:
Accepted Manuscript
Journal Name:
The Astronomical Journal (Online)
Additional Journal Information:
Journal Name: The Astronomical Journal (Online); Journal Volume: 156; Journal Issue: 4; Journal ID: ISSN 1538-3881
Publisher:
IOP Publishing - AAAS
Country of Publication:
United States
Language:
English
Subject:
79 ASTRONOMY AND ASTROPHYSICS; 97 MATHEMATICS AND COMPUTING; large-scale structure of universe – methods: data analysis – methods: numerical

Citation Formats

Hand, Nick, Feng, Yu, Beutler, Florian, Li, Yin, Modi, Chirag, Seljak, Uroš, and Slepian, Zachary. nbodykit: An Open-source, Massively Parallel Toolkit for Large-scale Structure. United States: N. p., 2018. Web. doi:10.3847/1538-3881/aadae0.
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Hand, Nick, Feng, Yu, Beutler, Florian, Li, Yin, Modi, Chirag, Seljak, Uroš, & Slepian, Zachary. nbodykit: An Open-source, Massively Parallel Toolkit for Large-scale Structure. United States. https://doi.org/10.3847/1538-3881/aadae0
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Hand, Nick, Feng, Yu, Beutler, Florian, Li, Yin, Modi, Chirag, Seljak, Uroš, and Slepian, Zachary. Tue . "nbodykit: An Open-source, Massively Parallel Toolkit for Large-scale Structure". United States. https://doi.org/10.3847/1538-3881/aadae0. https://www.osti.gov/servlets/purl/1559157.
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@article{osti_1559157,
title = {nbodykit: An Open-source, Massively Parallel Toolkit for Large-scale Structure},
author = {Hand, Nick and Feng, Yu and Beutler, Florian and Li, Yin and Modi, Chirag and Seljak, Uroš and Slepian, Zachary},
abstractNote = {We present nbodykit, an open-source, massively parallel Python toolkit for analyzing large-scale structure (LSS) data. Using Python bindings of the Message Passing Interface, we provide parallel implementations of many commonly used algorithms in LSS. nbodykit is both an interactive and scalable piece of scientific software, performing well in a supercomputing environment while still taking advantage of the interactive tools provided by the Python ecosystem. Existing functionality includes estimators of the power spectrum, two- and three-point correlation functions, a friends-of-friends grouping algorithm, mock catalog creation via the halo occupation distribution technique, and approximate N-body simulations via the FastPM scheme. The package also provides a set of distributed data containers, insulated from the algorithms themselves, that enables nbodykit to provide a unified treatment of both simulation and observational data sets. nbodykit can be easily deployed in a high-performance computing environment, overcoming some of the traditional difficulties of using Python on supercomputers. We provide performance benchmarks illustrating the scalability of the software. The modular, component-based approach of nbodykit allows researchers to easily build complex applications using its tools. The package is extensively documented at http://nbodykit.readthedocs.io, which also includes an interactive set of example recipes for new users to explore. As open-source software, we hope nbodykit provides a common framework for the community to use and develop in confronting the analysis challenges of future LSS surveys.},
doi = {10.3847/1538-3881/aadae0},
journal = {The Astronomical Journal (Online)},
number = 4,
volume = 156,
place = {United States},
year = {Tue Sep 18 00:00:00 EDT 2018},
month = {Tue Sep 18 00:00:00 EDT 2018}
}
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Journal Article:
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https://doi.org/10.3847/1538-3881/aadae0
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Cited by: 156 works
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Figures / Tables:

Figure 1 Figure 1: The components and interfaces of nbodykit. The main Python classes are Catalog, Mesh, and Algorithm objects, which are described in more detail in Section 2.3. Algorithm results can be consistent, where all processes hold the same data, or distributed, where data are spread out evenly across parallel processes.
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A New Method to Correct for Fiber Collisions in Galaxy Two-Point Statistics
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Mock galaxy catalogs using the quick particle mesh method
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Measuring line-of-sight dependent Fourier-space clustering using FFTs
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Accurate Estimators of Correlation Functions in Fourier Space
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Forecasts for the WFIRST High Latitude Survey using the BlueTides Simulation
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Forward Modeling of Large-Scale Structure: An open-source approach with Halotools
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Beyond the plane-parallel approximation for redshift surveys
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Stable clustering, the halo model and nonlinear cosmological power spectra
text, January 2002

Towards optimal parallel PM N-body codes: PMFAST
text, January 2004

A Measurement of the Quadrupole Power Spectrum in the Clustering of the 2dF QSO Survey
text, January 2005

Constraints onOmega from the IRAS Redshift Surveys
text, January 1994

Power Spectra for Cold Dark Matter and its Variants
text, January 1997

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    Learning to predict the cosmological structure formation
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    • Barreira, Alexandre; Nelson, Dylan; Pillepich, Annalisa
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    The Aemulus Project. I. Numerical Simulations for Precision Cosmology
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    • DeRose, Joseph; Wechsler, Risa H.; Tinker, Jeremy L.
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    Cosmological Constraints from the Redshift Dependence of the Alcock–Paczynski Effect: Fourier Space Analysis
    journal, December 2019

    A Hybrid Deep Learning Approach to Cosmological Constraints from Galaxy Redshift Surveys
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    • Ntampaka, Michelle; Eisenstein, Daniel J.; Yuan, Sihan
    • The Astrophysical Journal, Vol. 889, Issue 2
    • DOI: 10.3847/1538-4357/ab5f5e

    COLOSSUS: A Python Toolkit for Cosmology, Large-scale Structure, and Dark Matter Halos
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    Primordial features from linear to nonlinear scales
    text, January 2020

    • Beutler, Florian; Biagetti, Matteo; Green, Daniel
    • Apollo - University of Cambridge Repository
    • DOI: 10.17863/cam.48003

    First constraint on the neutrino-induced phase shift in the spectrum of baryon acoustic oscillations
    text, January 2019

    • Baumann, Daniel; Beutler, Florian; Flauger, Raphael
    • Apollo - University of Cambridge Repository
    • DOI: 10.17863/cam.36820

    Learning to Predict the Cosmological Structure Formation
    text, January 2018

    Primordial Features from Linear to Nonlinear Scales
    text, January 2019

    A Hybrid Deep Learning Approach to Cosmological Constraints From Galaxy Redshift Surveys
    text, January 2019

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