nbodykit: an open-source, massively parallel toolkit for large-scale structure (1712.05834v1)

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 (MPI), 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, 2 and 3-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 enable 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.

nbodykit: an Open-Source Toolkit for Large-Scale Structure Analysis

The paper under review introduces nbodykit, a comprehensive open-source toolkit designed for analyzing large-scale structure (LSS) data. It distinguishes itself by offering a highly modular, massively parallel framework tailored for usage on both high-performance computing environments and interactive platforms. Written entirely in Python, nbodykit leverages Python bindings to the Message Passing Interface (MPI), facilitating parallel implementations of algorithms pivotal to LSS analysis, such as power spectrum estimation, correlation functions, halo identification, and mock catalog creation via halo occupation distribution techniques.

Core Features and Technical Implementation

nbodykit is structured to cater to a wide array of data commonly encountered in cosmological research, including simulation outputs and observational datasets. Its architecture encompasses distributed data containers which enable seamless interaction with diverse data formats like CSV, FITS, HDF5, and binary formats. Notably, the toolkit uses the particle mesh library PFFT for efficient fast Fourier transforms, ensuring scalability even as data sizes grow in accordance with next-generation LSS surveys.

The modular nature of nbodykit is essentially built around three primary components: Catalogs, Meshes, and Algorithms.

1. Catalogs: These are designed to hold data of discrete objects in a columnar format, accommodating datasets from various formats and supporting the generation of simulated data at runtime.
2. Meshes: Defined via the paint() function which allows interpolation of catalog data onto a mesh, thereby facilitating calculations in both configuration and Fourier space.
3. Algorithms: Implement various LSS analysis techniques ranging from the estimation of the power spectrum using FFT-based methods to pair counting in survey data.

Performance and Scalability

A detailed benchmarking paper presented in the paper underlines nbodykit's excellent scalability on high-performance computing resources, notably the Cori Cray supercomputers at NERSC. The performance benchmarks showcase significant wall-clock time reductions when increasing the number of MPI ranks, affirming its suitability for handling the large datasets anticipated in upcoming surveys like DESI.

Implications for Research

The establishment of nbodykit as a standard package for LSS analysis presents notable advantages from both a practical and theoretical standpoint. Practically, it simplifies the incorporation of advanced analysis techniques into research workflows, potentially reducing computational overhead. Theoretically, it offers a robust and adaptable framework that could expedite numerous cosmological investigations, such as probing the neutrino mass scale or deviations from General Relativity, thereby advancing our understanding of cosmic evolution.

Future Prospects

As LSS datasets expand, the need for efficient processing tools becomes critical, and nbodykit is well-positioned to adapt to these developments. Its modular and open-source nature fosters community involvement, suggesting potential avenues for enhancement driven by novel user contributions. Future iterations might incorporate additional statistical estimators or broaden its applicability to model complex galaxy-halo relations more thoroughly.

In conclusion, nbodykit serves not only as a utility for current cosmological research but as a promising precursor to the software ecosystems that will be required for future scientific inquiries based on extensive LSS data.