Boris2

Boris Computational Spintronics.

This is the single-GPU version. There is a multi-GPU version here: https://github.com/SerbanL/BORIS

Development of the single-GPU version is largely finished, all functionality is included in the multi-GPU upgraded codebase.

C++17 used. The codebase is currently contained in 918 files (.h, .cpp, .cu, .cuh, .py), ~164k non-trivial loc, and can be compiled on Windows or Linux-based OS with MSVC compiler or g++ compiler respectively.

Download

Latest compiled version with installer, including source code with makefile for Linux-based OS, found here : https://boris-spintronics.uk/download

Manual

Latest manual rolled in with installer, also found here in the Manual directory together with examples.

External Dependencies

CUDA 9.2 or newer : https://developer.nvidia.com/cuda-92-download-archive Python3 development version : https://www.python.org/downloads/ FFTW3 : http://www.fftw.org/download.html

OS

The full code can be compiled on Windows 7 or Windows 10 using the MSVC compiler. The code has also been ported to Linux (I've tested on Ubuntu 20.04) and compiled with g++, but with restrictions:

1. The graphical interface was originally written using DirectX11 so when compiling on Linux the GRAPHICS 0 flag needs to be set (see below). In the near future I plan to re-write the graphical interface in SFML.

Building From Source

Windows:

1. Clone the project.
2. Open the Visual Studio solution file (I use Visual Studio 2017).
3. Make sure all external dependencies are updated - see above.
4. Configure the compilation as needed - see CompileFlags.h, BorisLib_Config.h, and cuBLib_Flags.h, should be self explanatory.
5. Compile!

Linux (tested on Ubuntu 20.04):

Extract the archive. On Linux-based OS the program needs to be compiled from source using the provided makefile in the extracted BorisLin directory. Make sure you have all the required updates and dependencies:

Step 0: Updates.

1. Get latest g++ compiler: $ sudo apt install build-essential
2. Get OpenMP: $ sudo apt-get install libomp-dev
3. Get LibTBB: $ sudo apt install libtbb-dev
4. Get latest CUDA Toolkit (see manual for further details)
5. Get and install FFTW3: Instructions at http://www.fftw.org/fftw2_doc/fftw_6.html
6. Get Python3 development version, required for running Python scripts in embedded mode. To get Python3 development version: $ sudo apt-get install python-dev

Open terminal and go to extracted BorisLin directory.

Step 1: Configuration.

$ make configure (arch=xx) (sprec=0/1) (python=x.x) (cuda=x.x) (conda-env-path=/../..)

Before compiling you need to set the correct CUDA architecture for your NVidia GPU.

For a list of architectures and more details see: https://en.wikipedia.org/wiki/CUDA.

Possible values for arch are:

• arch=50 is required for Maxwell architecture; translates to -arch=sm_50 in nvcc compilation.

• arch=60 is required for Pascal architecture; translates to -arch=sm_60 in nvcc compilation.

• arch=70 is required for Volta (and Turing) architecture; translates to -arch=sm_70 in nvcc compilation.

• arch=80 is required for Ampere architecture; translates to -arch=sm_80 in nvcc compilation.

• arch=90 is required for Ada (and Hopper) architecture; translates to -arch=sm_90 in nvcc compilation.

sprec sets either single precision (1) or double precision (0) for CUDA computations.

python is the Python version installed, e.g. 3.8

if conda-env-path is not set the system installed python will be used.

if you would like to use conda python distribution use conda-env-path variable.

for base environment set the conda installation path (e.g. /opt/conda or /home/USERNAME/miniconda3)

for specific environment set specify the environment path (e.g. /opt/conda/envs/your_desired_env or /home/USERNAME/miniconda3/envs/your_desired_env)

cuda is the CUDA Toolkit version installed, e.g. 12.0.

Example: $ make configure arch=80 sprec=1 python=3.8 cuda=12.0

Step 2: Compilation.

$ make compile -j N

(replace N with the number of logical cores on your CPU for multi-processor compilation)

Example: $ make compile -j 16

Step 3: Installation.

$ make install

Step4: Run.

$ ./BorisLin

Step5: python package NetSocks

For proper use of python bindings you will need a NetSocks binding. You can find it in the src folder. It is already packaged so you can install with:

$ pip install .

Publications

There are a number of articles which cover various parts of the software.

General (if using Boris for published works please use this as a reference)

• S. Lepadatu, “Boris computational spintronics — High performance multi-mesh magnetic and spin transport modeling software”, Journal of Applied Physics 128, 243902 (2020)

Differential equation solvers

• S. Lepadatu “Speeding Up Explicit Numerical Evaluation Methods for Micromagnetic Simulations Using Demagnetizing Field Polynomial Extrapolation” IEEE Transactions on Magnetics 58, 1 (2022)

Multilayered convolution

• S. Lepadatu, “Efficient computation of demagnetizing fields for magnetic multilayers using multilayered convolution” Journal of Applied Physics 126, 103903 (2019)

Parallel Monte Carlo algorithm

• S. Lepadatu, G. Mckenzie, T. Mercer, C.R. MacKinnon, P.R. Bissell, “Computation of magnetization, exchange stiffness, anisotropy, and susceptibilities in large-scale systems using GPU-accelerated atomistic parallel Monte Carlo algorithms” Journal of Magnetism and Magnetic Materials 540, 168460 (2021)

Micromagnetic Monte Carlo algorithm (with demagnetizing field parallelization)

• S. Lepadatu “Micromagnetic Monte Carlo method with variable magnetization length based on the Landau–Lifshitz–Bloch equation for computation of large-scale thermodynamic equilibrium states” Journal of Applied Physics 130, 163902 (2021)

Roughness effective field

• S. Lepadatu, “Effective field model of roughness in magnetic nano-structures” Journal of Applied Physics 118, 243908 (2015)

Heat flow solver, LLB and 2-sublattice LLB

• S. Lepadatu, “Interaction of Magnetization and Heat Dynamics for Pulsed Domain Wall Movement with Joule Heating” Journal of Applied Physics 120, 163908 (2016)

• S. Lepadatu “Emergence of transient domain wall skyrmions after ultrafast demagnetization” Physical Review B 102, 094402 (2020)

Spin transport solver

• S. Lepadatu, “Unified treatment of spin torques using a coupled magnetisation dynamics and three-dimensional spin current solver” Scientific Reports 7, 12937 (2017)

• S. Lepadatu, “Effect of inter-layer spin diffusion on skyrmion motion in magnetic multilayers” Scientific Reports 9, 9592 (2019)

• C.R. MacKinnon, S. Lepadatu, T. Mercer, and P.R. Bissell “Role of an additional interfacial spin-transfer torque for current-driven skyrmion dynamics in chiral magnetic layers” Physical Review B 102, 214408 (2020)

• C.R. MacKinnon, K. Zeissler, S. Finizio, J. Raabe, C.H. Marrows, T. Mercer, P.R. Bissell, and S. Lepadatu, “Collective skyrmion motion under the influence of an additional interfacial spin transfer torque” Scientific Reports 12, 10786 (2022)

• S. Lepadatu and A. Dobrynin, “Self-consistent computation of spin torques and magneto-resistance in tunnel junctions and magnetic read-heads with metallic pinhole defects” Journal of Physics: Condensed Matter 35, 115801 (2023)

Elastodynamics solver with thermoelastic effect and magnetostriction

• S. Lepadatu, “All-Optical Magnetothermoelastic Skyrmion Motion” Physical Review Applied 19, 044036 (2023)