ARTEMIS (Adaptive mesh Refinement Time-domain ElectrodynaMIcs Solver)

ARTEMIS is a high-performance coupled electrodynamics–micromagnetics solver for fully physical modeling of signals in microelectronic circuitry. Its primary features include:

Finite-Difference Time-Domain (FDTD) approach for Maxwell’s equations.
Landau–Lifshitz–Gilbert (LLG) equation modeling for micromagnetics.
Adaptive Mesh Refinement implemented via the AMReX framework.
GPU acceleration and scalable parallel performance on modern manycore architectures.

The code couples magnetization physics with electromagnetic fields in a temporally second-order accurate manner, using a trapezoidal scheme in time for the LLG equation and straightforward explicit FDTD updates for the electromagnetic fields. In practice, ARTEMIS has shown excellent scaling results on NERSC multicore and GPU systems, delivering up to a 59× speedup on GPU relative to a single CPU node.

Installation

Clone AMReX (dependency):

git clone git@github.com:AMReX-Codes/amrex.git

Clone ARTEMIS in the same directory as AMReX:
```
git clone git@github.com:AMReX-Microelectronics/artemis.git
```
Make sure amrex/ and artemis/ are placed alongside each other in your filesystem.
Build ARTEMIS:
1. Navigate to the Exec/ folder inside artemis/.
2. Build with make -j 4, for example:
```
cd artemis/Exec/
make -j 4
```
By default, LLG is enabled (USE_LLG = TRUE). You can explicitly switch it on/off:
- Without LLG:
```
make -j 4 USE_LLG=FALSE
```
- With LLG:
```
make -j 4 USE_LLG=TRUE
```
To enable GPU acceleration (e.g., CUDA), set USE_GPU=TRUE in the make command. Check the GNUmakefile or other build scripts for additional optional flags like MPI, OpenMP, etc.

Visualization and Data Analysis

ARTEMIS uses the AMReX I/O format for storing simulation results. You can use tools such as VisIt, ParaView, or other readers compatible with AMReX plotfiles.

Additionally, yt can be used in Python to load the data for advanced post-processing:

import yt
ds = yt.load('./plt00001000/')  # load plotfile at time step 1000
ad0 = ds.covering_grid(level=0, left_edge=ds.domain_left_edge, dims=ds.domain_dimensions)
E_array = ad0['Ex'].to_ndarray()  # Retrieve Ex (x-component of E-field)

Publications

Z. Yao, R. Jambunathan, Y. Zeng, and A. Nonaka, A massively parallel time-domain coupled electrodynamics–micromagnetics solver. The International Journal of High Performance Computing Applications, 2022;36(2):167-181. doi:10.1177/10943420211057906
S. S. Sawant, Z. Yao, R. Jambunathan, and A. Nonaka, Characterization of transmission lines in microelectronic circuits using the ARTEMIS solver, IEEE Journal on Multiscale and Multiphysics Computational Techniques, vol. 8, pp. 31-39, 2023, doi:10.1109/JMMCT.2022.3228281
R. Jambunathan, Z. Yao, R. Lombardini, A. Rodriguez, and A. Nonaka, Two-fluid physical modeling of superconducting resonators in the ARTEMIS framework, Computer Physics Communications, 291, p.108836, 2023. doi:10.1016/j.cpc.2023.108836