NIFTy 3 - Numerical Information Field Theory - A Python framework for multicomponent signal inference on HPC clusters

TL;DR

NIFTy 3 is an advanced Python framework that simplifies the development of field inference algorithms, supporting high-dimensional, multi-component, and parallel computations for real-world scientific problems.

Contribution

It introduces NIFTy 3, enabling scalable, parallel inference on HPC clusters with support for various geometries and non-scalar fields, improving flexibility and performance.

Findings

Supports n-dimensional Cartesian and spherical spaces

Enables parallel inference on HPC clusters

Handles non-scalar fields efficiently

Abstract

NIFTy, "Numerical Information Field Theory", is a software framework designed to ease the development and implementation of field inference algorithms. Field equations are formulated independently of the underlying spatial geometry allowing the user to focus on the algorithmic design. Under the hood, NIFTy ensures that the discretization of the implemented equations is consistent. This enables the user to prototype an algorithm rapidly in 1D and then apply it to high-dimensional real-world problems. This paper introduces NIFTy 3, a major upgrade to the original NIFTy framework. NIFTy 3 allows the user to run inference algorithms on massively parallel high performance computing clusters without changing the implementation of the field equations. It supports n-dimensional Cartesian spaces, spherical spaces, power spaces, and product spaces as well as transforms to their harmonic counterparts. Furthermore, NIFTy 3 is able to treat non-scalar fields. The functionality and performance of the software package is demonstrated with example code, which implements a real inference algorithm from the realm of information field theory. NIFTy 3 is open-source software available under the GNU General Public License v3 (GPL-3) at https://gitlab.mpcdf.mpg.de/ift/NIFTy/