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

ORAL

Abstract

We present MagneX, an open-source micromagnetics modeling tool that leverages massively parallel and GPU-enabled DOE software frameworks and ML workflows to allow for detailed investigations of multiphysics coupling in spintronic devices. We leverage the AMReX framework for multicore and GPU scalability, the SUNDIALS library for high-order multirate time integration, and python-based workflows for data-driven acceleration of computational kernels. MagneX 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 performance and scalability of the code and rigorously validate MagneX's functionality using the mumag standard problems and widely-accepted DMI benchmarks. 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.

Presenters

  • Andy J Nonaka

    Lawrence Berkeley National Laboratory

Authors

  • Andy J Nonaka

    Lawrence Berkeley National Laboratory

  • Prabhat Kumar

    Lawrence Berkeley National Laboratory

  • Julian LePelch

    University of California, Santa Cruz

  • Yingheng Tang

    Lawrence Berkeley National Lab, Lawrence Berkeley National Laboratory

  • Jorge A Munoz

    University of Texas at El Paso

  • Christian A Fernandez

    University of Texas at El Paso

  • Cesar Diaz

    University of Texas at El Paso

  • David J Gardner

    Lawrence Livermore National Laboratory

  • Zhi (Jackie) Yao

    Lawrence Berkeley National Laboratory