Add propagate w_mul_xj CUDA sparse support using matrix mul#610
Merged
CarloLucibello merged 1 commit intoJuliaGraphs:masterfrom Jul 16, 2025
Merged
Conversation
Member
|
Build kite Cuda issues doesn't seem related to this pr, but we have to fix it at some point. |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Added fast w_mul_xj propagate CUDA support for sparse graphs using SpMM.
Added benchmarks to compare with gather/scatter approach, speedup from 40x to 300x on my machine, huge memory allocation benefits (up to 1000x less or more depending on size of graph and sparsity level).
CUDA tests on GraphNeuralNetworks passed.