GALARIO: a GPU Accelerated Library for Analysing Radio Interferometer Observations

TL;DR

GALARIO is a GPU-accelerated library that significantly speeds up the analysis of radio interferometer data, enabling faster modeling and fitting of astronomical observations with broad applicability.

Contribution

It introduces a highly modular, GPU-accelerated library for rapid computation of synthetic visibilities from radio interferometry data, compatible with multiple programming languages.

Findings

GALARIO is 150 times faster than Python implementations.

It is 10 times faster than serial C++ on CPU.

The library supports both GPU and CPU computations with identical interfaces.

Abstract

We present GALARIO, a computational library that exploits the power of modern graphical processing units (GPUs) to accelerate the analysis of observations from radio interferometers like ALMA or the VLA. GALARIO speeds up the computation of synthetic visibilities from a generic 2D model image or a radial brightness profile (for axisymmetric sources). On a GPU, GALARIO is 150 faster than standard Python and 10 times faster than serial C++ code on a CPU. Highly modular, easy to use and to adopt in existing code, GALARIO comes as two compiled libraries, one for Nvidia GPUs and one for multicore CPUs, where both have the same functions with identical interfaces. GALARIO comes with Python bindings but can also be directly used in C or C++. The versatility and the speed of GALARIO open new analysis pathways that otherwise would be prohibitively time consuming, e.g. fitting high resolution observations of large number of objects, or entire spectral cubes of molecular gas emission. It is a general tool that can be applied to any field that uses radio interferometer observations. The source code is available online at https://github.com/mtazzari/galario under the open source GNU Lesser General Public License v3.