NIFTy 3-Numerical Information Field Theory: A Python Framework for Multicomponent Signal Inference on HPC Clusters

Steininger, Theo; Dixit, Jait; Frank, Philipp; Greiner, Maksim; Hutschenreuter, Sebastian; Knollmüller, Jakob; Leike, Reimar; Porqueres, Natalia; Pumpe, Daniel; Reinecke, Martin; Sraml, Matevz; Varady, Csongor und Ensslin, Torsten (2019): NIFTy 3-Numerical Information Field Theory: A Python Framework for Multicomponent Signal Inference on HPC Clusters. In: Annalen der Physik, Bd. 531, Nr. 3, 1800290

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 handle non-scalar fields, such as vector or tensor fields. The functionality and performance of the software package is demonstrated with example code, which implements a mock inference inspired by a real-world 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 .

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Physik
• Themengebiete: 500 Naturwissenschaften und Mathematik > 530 Physik
• ISSN: 0003-3804
• Sprache: Englisch