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Nbodykit: an open-source, massively parallel toolkit for large-scale structure

Research output: Contribution to journalArticle

Standard

Nbodykit : an open-source, massively parallel toolkit for large-scale structure. / Hand, Nick; Feng, Yu; Beutler, Florian; Li, Yin; Modi, Chirag; Seljak, Uroš; Slepian, Zachary.

In: Astronomical Journal, Vol. 156, No. 4, 160, 18.09.2018.

Research output: Contribution to journalArticle

Harvard

Hand, N, Feng, Y, Beutler, F, Li, Y, Modi, C, Seljak, U & Slepian, Z 2018, 'Nbodykit: an open-source, massively parallel toolkit for large-scale structure' Astronomical Journal, vol. 156, no. 4, 160. https://doi.org/10.3847/1538-3881/aadae0

APA

Hand, N., Feng, Y., Beutler, F., Li, Y., Modi, C., Seljak, U., & Slepian, Z. (2018). Nbodykit: an open-source, massively parallel toolkit for large-scale structure. Astronomical Journal, 156(4), [160]. https://doi.org/10.3847/1538-3881/aadae0

Vancouver

Hand N, Feng Y, Beutler F, Li Y, Modi C, Seljak U et al. Nbodykit: an open-source, massively parallel toolkit for large-scale structure. Astronomical Journal. 2018 Sep 18;156(4). 160. https://doi.org/10.3847/1538-3881/aadae0

Author

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. In: Astronomical Journal. 2018 ; Vol. 156, No. 4.

Bibtex

@article{2e2365ca84484fa99b56d672c9c7f04a,
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, we hope nbodykit provides a common framework for the community to use and develop in confronting the analysis challenges of future LSS surveys.",
keywords = "large-scale structure of universe, methods: data analysis, methods: numerical, RCUK, STFC, ST/P004210/1",
author = "Nick Hand and Yu Feng and Florian Beutler and Yin Li and Chirag Modi and Uroš Seljak and Zachary Slepian",
year = "2018",
month = "9",
day = "18",
doi = "10.3847/1538-3881/aadae0",
language = "English",
volume = "156",
journal = "The Astronomical Journal",
issn = "0004-6256",
publisher = "IOP Publishing Ltd.",
number = "4",

}

RIS

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T1 - Nbodykit

T2 - The Astronomical Journal

AU - Hand, Nick

AU - Feng, Yu

AU - Beutler, Florian

AU - Li, Yin

AU - Modi, Chirag

AU - Seljak, Uroš

AU - Slepian, Zachary

PY - 2018/9/18

Y1 - 2018/9/18

N2 - 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.

AB - 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.

KW - large-scale structure of universe

KW - methods: data analysis

KW - methods: numerical

KW - RCUK

KW - STFC

KW - ST/P004210/1

UR - http://www.scopus.com/inward/record.url?scp=85054803264&partnerID=8YFLogxK

U2 - 10.3847/1538-3881/aadae0

DO - 10.3847/1538-3881/aadae0

M3 - Article

VL - 156

JO - The Astronomical Journal

JF - The Astronomical Journal

SN - 0004-6256

IS - 4

M1 - 160

ER -

ID: 12320842