# Boris2
**Repository Path**: wang-tx_1_0/Boris2
## Basic Information
- **Project Name**: Boris2
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: GPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-10-28
- **Last Updated**: 2025-12-18
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# 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)