Open-source Flux Transport (OFT). I. HipFT -- High-performance Flux Transport

TL;DR

The paper introduces HipFT, a high-performance, open-source Surface Flux Transport model designed for solar magnetic field mapping, featuring modular design, parallel computing, and extensibility for improved solar physics research.

Contribution

It presents HipFT, an open-source, high-performance SFT code with advanced numerical methods, parallelism, and user-extensible features, filling a gap in existing solar magnetic field modeling tools.

Findings

HipFT achieves efficient multi-CPU and GPU parallelism.

Validated numerical methods ensure accurate flux transport simulations.

Enables ensemble modeling for uncertainty quantification.

Abstract

Global solar photospheric magnetic maps play a critical role in solar and heliospheric physics research. Routine magnetograph measurements of the field occur only along the Sun-Earth line, leaving the far-side of the Sun unobserved. Surface Flux Transport (SFT) models attempt to mitigate this by modeling the surface evolution of the field. While such models have long been established in the community (with several releasing public full-Sun maps), none are open source. The Open Source Flux Transport (OFT) model seeks to fill this gap by providing an open and user-extensible SFT model that also builds on the knowledge of previous models with updated numerical and data acquisition/assimilation methods along with additional user-defined features. In this first of a series of papers on OFT, we introduce its computational core: the High-performance Flux Transport (HipFT) code (github.com/predsci/hipft). HipFT implements advection, diffusion, and data assimilation in a modular design that supports a variety of flow models and options. It can compute multiple realizations in a single run across model parameters to create ensembles of maps for uncertainty quantification and is high-performance through the use of multi-CPU and multi-GPU parallelism. HipFT is designed to enable users to easily write extensions, enhancing its flexibility and adaptability. We describe HipFT's model features, validations of its numerical methods, performance of its parallel and GPU-accelerated code implementation, analysis/post-processing options, and example use cases.