CUDA copy_xj propagate sparse support and benchmarks#605
Merged
CarloLucibello merged 3 commits intoJuliaGraphs:masterfrom Jul 6, 2025
Merged
CUDA copy_xj propagate sparse support and benchmarks#605CarloLucibello merged 3 commits intoJuliaGraphs:masterfrom
CarloLucibello merged 3 commits intoJuliaGraphs:masterfrom
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| function propagate(::typeof(copy_xj), g::GNNGraph, ::typeof(+), xi, xj::AbstractMatrix, e) | ||
| A = adjacency_matrix(g, weighted = false) | ||
| A = adjacency_matrix(g, eltype(xj); weighted = false) |
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Is the cast to the xj type necessary?
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Yes, for the sparse case, because the SpMM mm! function in CUSPASE expects the type of the adjmat and the feature matrix to be the same, so we need to cast the adjmat before multiplying.
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Added fast copy_xj propagate CUDA support for sparse graphs using SpMM.
Added benchmarks to compare with gather/scatter approach, speedup of 100-200x on average.