Documentation

Examples/Waveguide/inputs_3d_LLG_filter

@@ -11,7 +11,7 @@
 #################################
 max_step = 300000
 amr.n_cell = 1024 4 512 # number of cells spanning the domain in each coordinate direction at level 0
-amr.max_grid_size = 1024 # maximum size of each AMReX box, used to decompose the domain
+amr.max_grid_size = 256 # maximum size of each AMReX box, used to decompose the domain
 amr.blocking_factor = 4 # only meaningful for AMR
 geometry.dims = 3
 boundary.field_lo = pec pec pml  # PEC on side walls; PML at -z end

Examples/Waveguide/inputs_3d_empty_X_band

@@ -8,7 +8,7 @@
 ####### GENERAL PARAMETERS ######
 #################################
 max_step = 1000000
-amr.n_cell = 512 4 512 # number of cells spanning the domain in each coordinate direction at level 0
+amr.n_cell = 128 4 128 # number of cells spanning the domain in each coordinate direction at level 0
 amr.max_grid_size = 64 # maximum size of each AMReX box, used to decompose the domain
 amr.blocking_factor = 4 # only meaningful for AMR
 geometry.dims = 3

README.md

@@ -1,2 +1,56 @@
 # ARTEMIS
+ARTEMIS (Adaptive mesh Refinement Time-domain ElectrodynaMIcs Solver) is a high-performance coupled electrodynamics–micromagnetics solver for full physical modeling of signals in microelectronic circuitry. The overall strategy couples a finite-difference time-domain (FDTD) approach for Maxwell’s equations to a magnetization model described by the Landau–Lifshitz–Gilbert (LLG) equation. The algorithm is implemented in the Exascale Computing Project (ECP) software framework, AMReX, which provides effective scalability on manycore and GPU-based supercomputing architectures. Furthermore, the code leverages ongoing developments of the Exascale Application Code, WarpX, which is primarily being developed for plasma wakefield accelerator modeling. Our temporal coupling scheme provides second-order accuracy in space and time by combining the integration steps for the magnetic field and magnetization into an iterative sub-step that includes a trapezoidal temporal discretization for the magnetization. The performance of the algorithm is demonstrated by the excellent scaling results on NERSC multicore and GPU systems, with a significant (59×) speedup on the GPU using a node-by-node comparison. The utility of our code is validated by performing simulations of transmission lines, rectangle electromagnetic waveguides, magnetically tunable filters, on-chip coplanar waveguides and resonators, magnon-photon coupling circuits, and so on.
 
+# Installation
+## Download AMReX Repository
+``` git clone [email protected]:AMReX-Codes/amrex.git ```
+## Download Artemis Repository
+``` git clone [email protected]:AMReX-Microelectronics/artemis.git ```
+## Build
+Make sure that the AMReX and Artemis are cloned in the same location in their filesystem. Navigate to the Exec folder of Artemis and execute
+```make -j 4```. <br />
+You can turn on and off the LLG equation by specifying ```USE_LLG``` during compilation. <br />
+The following command compiles Artemis without LLG
+```make -j 4 USE_LLG=FALSE``` <br />
+The following command compiles Artemis with LLG
+```make -j 4 USE_LLG=TRUE``` <br />
+The default value of ```USE_LLG``` is ```TRUE```.
+
+# Running Artemis
+Example input scripts are located in `Examples` directory. 
+## Simple Testcase without LLG
+You can run the following to simulate an air-filled X-band rectangle waveguide:
+## For MPI+OMP build
+```make -j 4 USE_LLG=FALSE``` <br />
+```mpirun -n 4 ./main3d.gnu.TPROF.MTMPI.OMP.GPUCLOCK.ex Examples/Waveguide/inputs_3d_empty_X_band```
+## For MPI+CUDA build
+```make -j 4 USE_LLG=FALSE USE_GPU=TRUE``` <br />
+```mpirun -n 4 ./main3d.gnu.TPROF.MTMPI.CUDA.GPUCLOCK.ex Examples/Waveguide/inputs_3d_empty_X_band``` 
+## Simple Testcase with LLG
+You can run the following to simulate an X-band magnetically tunable filter:
+## For MPI+OMP build
+```make -j 4 USE_LLG=TRUE``` <br />
+```mpirun -n 8 ./main3d.gnu.TPROF.MTMPI.OMP.GPUCLOCK.ex Examples/Waveguide/inputs_3d_LLG_filter```
+## For MPI+CUDA build
+```make -j 4 USE_LLG=TRUE USE_GPU=TRUE``` <br />
+```mpirun -n 8 ./main3d.gnu.TPROF.MTMPI.CUDA.GPUCLOCK.ex Examples/Waveguide/inputs_3d_LLG_filter```
+# Visualization and Data Analysis
+Refer to the following link for several visualization tools that can be used for AMReX plotfiles. 
+
+[Visualization](https://amrex-codes.github.io/amrex/docs_html/Visualization_Chapter.html)
+
+### 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](https://yt-project.org/). After you have installed yt, you can do something as follows, for example, to get variable 'Ex' (x-component of electric field)
+```
+import yt
+ds = yt.load('./plt00001000/') # for data 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()
+```
+# Publications
+1. 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
+[link](https://journals.sagepub.com/doi/full/10.1177/10943420211057906)
+2. 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
+[link](https://ieeexplore.ieee.org/abstract/document/9980353)
+3. 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. doi:10.1016/j.cpc.2023.108836
+[link](https://www.sciencedirect.com/science/article/pii/S0010465523001819?casa_token=rWpwl8cmtUYAAAAA:rZTndzf_pqx0lo9jtTRzLLxh0tIf_AD0zHcRRJ_ciwMw-n-X2doK5RprMS4wyrO9TEw5oDZAB7Kr)