Improve Symbolic Cholesky performance #1758
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ginkgo-bot
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reg:testing
Constexpr evaluation complexity too high
yhmtsai
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Dec 27, 2024
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| GKO_REGISTER_OPERATION(forest_from_factor, cholesky::forest_from_factor); | ||
| GKO_REGISTER_OPERATION(from_factor, elimination_forest::from_factor); |
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previous name seems to be more descriptive.
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elimination_forest::forest_from_factor seemed unnecessarily verbose
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| GKO_REGISTER_OPERATION(forest_from_factor, cholesky::forest_from_factor); | ||
| GKO_REGISTER_OPERATION(from_factor, elimination_forest::from_factor); |
| { | ||
| constexpr auto size = 8; | ||
| using tree = gko::detail::cartesian_tree<size>; | ||
| int values[size]{}; |
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Suggested change
| int values[size]{}; | |
| int values[size]; |
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That's a technicality about constructing vs. assigning to uninitialized elements that I wanted to avoid. This kind of initialization is required in constexpr code, which is why I used it here as well.
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The OpenMP part is not yet parallelized, and the skeleton tree computation is not enabled by default. |
…ssing) see TODOs for problematic points
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This improves the symbolic Cholesky performance by preprocessing the matrix on the GPU with a Minimum Spanning Tree algorithm.
Example rgg_22 from SuiteSparse with METIS nested dissection on H100:
The performance improvements are split between device-host transfer (transferring a spanning tree instead of the full matrix) and the elimination tree computation (operating on a sparser graph)