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.
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Presenters
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Andy J Nonaka
Lawrence Berkeley National Laboratory
Authors
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Andy J Nonaka
Lawrence Berkeley National Laboratory
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Prabhat Kumar
Lawrence Berkeley National Laboratory
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Julian LePelch
University of California, Santa Cruz
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Yingheng Tang
Lawrence Berkeley National Lab, Lawrence Berkeley National Laboratory
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Jorge A Munoz
University of Texas at El Paso
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Christian A Fernandez
University of Texas at El Paso
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Cesar Diaz
University of Texas at El Paso
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David J Gardner
Lawrence Livermore National Laboratory
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Zhi (Jackie) Yao
Lawrence Berkeley National Laboratory