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MagneX

MagneX is a massively parallel, 3D micromagnetics solver for modeling magnetic materials. MagneX solves the Landau-Lifshitz-Gilbert (LLG) equations, including exchange, anisotropy, demagnetization, and Dzyaloshinskii-Moriya interaction (DMI) coupling. The algorithm is implemented using Exascale Computing Project software framework, AMReX, which provides effective scalability on manycore and GPU-based supercomputing architectures.

Documentation and Getting Help

More extensive documentation is available HERE. Our community is here to help. Please report installation problems or general questions about the code in the github Issues tab above.

Installation

Here are instructions for a basic, pure-MPI (no GPU) installation. More detailed instructions for GPU systems are in the full documentation.

Download AMReX and MagneX Repositories

Make sure that AMReX and MagneX are cloned at the same root location.
>> git clone https://github.com/AMReX-Codes/amrex.git
>> git clone https://AMReX-Microelectronics/MagneX.git

Dependencies

Beyond a standard Ubuntu22 installation, the Ubuntu packages libfftw3-dev, libfftw3-mpi-dev, and cmake are required.
SUNDIALS is optional and enables Runge-Kutta, implicit, and multirate integrators (more detailed instructions in the full documentation).
heFFTe is a required dependency. At the same level that AMReX and MagneX are cloned, run:
>> git clone https://github.com/icl-utk-edu/heffte.git
>> cd heffte
>> mkdir build
>> cd build
>> cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_CXX_STANDARD=17 -DBUILD_SHARED_LIBS=OFF -DCMAKE_INSTALL_PREFIX=. -DHeffte_ENABLE_FFTW=ON -DHeffte_ENABLE_CUDA=OFF ..
>> make -j4
>> make install

Build

Navigate to MagneX/Exec/ and run:
>> make -j4

Running MagneX

You can run the following to simulate muMAG Standard Problem 4 dynamics:
>> ./main3d.gnu.MPI.ex standard_problem_inputs/inputs_std4

Visualization and Data Analysis

Refer to the following link for several visualization tools that can be used for AMReX plotfiles.

Visualization

Data Analysis in Python using yt

You can extract the data in numpy array format using yt (you can refer to this for installation and usage of yt. After you have installed yt, you can do something as follows, for example, to get variable 'Mx' (x-component of magnetization)

import yt
ds = yt.load('./plt00010000/') # for data at time step 10000
ad0 = ds.covering_grid(level=0, left_edge=ds.domain_left_edge, dims=ds.domain_dimensions)
Mx_array = ad0['Mx'].to_ndarray()

Publications

  1. Z. Yao, P. Kumar, J. C. LePelch, and A. Nonaka, MagneX: An Exascale-Enabled Micromagnetics Solver for Spintronic Systems, in preparation.