Seeking advice for setting up solver for finite element

Environment information:

OS: Windows 11
CUDA runtime: CUDA 12.4.
AMGX version or commit hash: 2.5.0
NVIDIA driver: 551.78
NVIDIA GPU: NVIDIA RTX 3060 Ti

AMGX solver configuration: This is one of AMG configuration that I tried to use but failed to converged.
{
"config_version": 2,
"solver": {
"matrix_coloring_scheme": "MIN_MAX",
"max_uncolored_percentage": 0.15,
"algorithm": "AGGREGATION",
"obtain_timings": 1,
"solver": "AMG",
"smoother": "MULTICOLOR_DILU",
"print_solve_stats": 1,
"presweeps": 1,
"selector": "SIZE_2",
"coarsest_sweeps": 2,
"max_iters": 1000,
"monitor_residual": 1,
"scope": "main",
"max_levels": 1000,
"postsweeps": 1,
"tolerance": 0.1,
"print_grid_stats": 1,
"norm": "L1",
"cycle": "V"
}
}

Matrix is attached

Reproduction steps

I ran this with the command line as in the quick start document :"amgx_capi -m identity_system.mtx -c AGGREGATION_DILU.json

It got diverged. Samte thing with AMG_CLASSICAL_CG.json. Using AMG_AGGRREGATION_CG it manage to converge but very slow

If I use PCG_AGGREGATION_JACOBI i got the best convergence speed.

Can you please tell me which option should I choose for the type of matrix that I attached? This is basically a 10 nodes tet mesh generated from a 3D solid dishes.

Thanks,

Kien

coarse.zip

coarse.zip

Start by reproducing the command-line run with identity_system.mtx and the referenced AGGREGATION_DILU.json, AMG_CLASSICAL_CG.json, and AMG_AGGRREGATION_CG configurations. Review the attached coarse.zip matrix and compare the reported convergence behavior, including PCG_AGGREGATION_JACOBI; done means identifying and documenting a suitable configuration for this finite-element matrix.