Comments (3)
learning-chip
commented on May 29, 2024
The AMG solve phase (just a few SpMVs) is much easier to port to GPU than the AMG setup phase (contains coarse node selection algorithms). This strategy is adopted by AMGCL.
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navidcy
commented on May 29, 2024
I might try to add GPU functionality to AlgebraicMultigrid.jl. @learning-chip, could you elaborate a bit more on what you are suggestion? If I understand correctly you are saying that we only need to make sure that solve methods work on CUDAs. All the other setup phases can be done on CPU and converted to the GPU relatively easy?
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ma-sadeghi
commented on May 29, 2024
Just chiming in to see if anyone has made any progress on this one. Would greatly appreciate it, thanks!
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Related Issues (20)
- Support custom interpolations
- Multithreading not working when using OpenBLAS HOT 9
- Creating multigrid hierarchy (e.g., `ruge_stuben`) allocates too much memory
- Increase test coverage with MatrixDepot.jl
- Info about upcoming removal of packages in the General registry HOT 4
- Info about upcoming removal of packages in the General registry HOT 1
- Dependency cleanup HOT 4
- verbose HOT 1
- Hessian vector products HOT 7
- TagBot trigger issue HOT 6
- Got unsupported keyword arguments "strength", "max_levels", "max_coarse" HOT 1
- User-defined near null space HOT 3
- Move from Travis to Github Actions HOT 3
- Optimization-Based Algebraic Multigrid Coarsening Using Reinforcement Learning (NeurIPS 2021)
- Avoid printing numerical values of Coarse Solver when summarizing MultiLevel solver HOT 2
- Printed Grid Complexity is wrong (just copies Operator Complexity) HOT 2
- Use sparse LU as a faster coarse solver than Pinv HOT 4
- Default ruge_stuben solver diverges for a Poisson problem, due to problematic interpolation HOT 3
- Inf on preconditioner \ b HOT 2
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