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NIFTy 3 - Numerical Information Field Theory - A Python framework for multicomponent signal inference on HPC clusters | | Theo Steininger
; Jait Dixit
; Philipp Frank
; Maksim Greiner
; Sebastian Hutschenreuter
; Jakob Knollmüller
; Reimar Leike
; Natalia Porqueres
; Daniel Pumpe
; Martin Reinecke
; Matevž Šraml
; Csongor Varady
; Torsten Enßlin
; | | Date: |
3 Aug 2017 | | 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 this https URL | | Source: | arXiv, 1708.1073 | | Services: | Forum | Review | PDF | Favorites | |
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