AMGX
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NVIDIA
Distributed multigrid linear solver library on GPU
Some minor CMake changes to try to fix some issues with building on systems without MPI and OpenMP CXX.
Hi, I am interested in the amgx_mpi_possion5pt tutorial. I compared the solve time and total time with that with a solver in PETSc. Here is the output from the amgx...
After initial hiccups with building components, I am now facing problems with running icoFoam (OpenFOAM) with execution dumping core with the following Error message.I am running this on Laptop having...
```shell $ python test_amgx.py Traceback (most recent call last): File "/data/guyer/fipy/test_amgx.py", line 5, in import pyamgx ImportError: /toolbox/guyer/AMGX/build/libamgxsh.so: undefined symbol: omp_get_num_threads ``` It works with AMGX v2.3.0 `test_amgx.py` is [pyamgx...
My sysadmins assure me that CUDA Toolkit is installed (and the AMGX examples seen to work when I comment out the following lines (only `examples/CMakeLists.txt` on HEAD; `eigen_examples/CMakeLists.txt` as well...
The AMGX becomes very slow at the stage "Parsing configuration string: selector=AGGRESSIVE_PMIS " after I increased the matrix size (matrix B). The dimension of matrix are as follows: A: NumGlobalRows...
Managing runtime selection of the supported types in AmgX makes the code more complex but doesn't seem to benefit users significantly from our discussions so far. This is a draft...
To be more precise: Does it add to values on other processors?
Dose AMGx provide a mechanism to exclude all the zero in the input matrix.
Supposing I used PCG(in double precision) as solver, and AMG(in single precision) as preconditioner, I would expect to have good performance for large scale matrix?
