MagneX: A High-Performance, GPU-Enabled, Data-Driven Micromagnetics Solver for Spintronics

TL;DR

MagneX is an open-source, GPU-accelerated micromagnetics solver that integrates multiple magnetic interactions, demonstrates high performance and scalability, and incorporates machine learning to accelerate complex computations in spintronics modeling.

Contribution

The paper introduces MagneX, a novel GPU-enabled micromagnetics tool that combines advanced physics modeling with machine learning acceleration, validated against standard benchmarks.

Findings

MagneX achieves high GPU performance and scalability.

The tool accurately models key magnetic interactions.

Machine learning accelerates demagnetization computations.

Abstract

In order to comprehensively investigate the multiphysics coupling in spintronic devices, it is essential to parallelize and utilize GPU-acceleration to address the spatial and temporal disparities inherent in the relevant physics. Additionally, the use of cutting-edge time integration libraries as well as machine learning (ML) approaches to replace and potentially accelerate expensive computational routines are attractive capabilities to enhance modeling capabilities moving forward. Leveraging the Exascale Computing Project software framework AMReX, as well as SUNDIALS time-integration libraries and python-based ML workflows, we have developed an open-source micromagnetics modeling tool called MagneX. This tool incorporates various crucial magnetic coupling mechanisms, including Zeeman coupling, demagnetization coupling, crystalline anisotropy interaction, exchange coupling, and Dzyaloshinskii-Moriya interaction (DMI) coupling. We demonstrate the GPU performance and scalability of the code and rigorously validate MagneX's functionality using the mumag standard problems and widely-accepted DMI benchmarks. Furthermore, we demonstrate the data-driven capability of MagneX by replacing the computationally-expensive demagnetization physics with neural network libraries trained from our simulation data. With the capacity to explore complete physical interactions, this innovative approach offers a promising pathway to better understand and develop fully integrated spintronic and electronic systems.