Replace O(N*degree^2) CPU dedup with GPU warp-ballot kernel - #2437
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Replace O(N*degree^2) CPU dedup with GPU warp-ballot kernel#2437jamxia155 wants to merge 1 commit into
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The graph shrink step in GNND::build() copied the NN-descent output while removing duplicate and self-referencing neighbor IDs using a nested scan: for each of the N nodes, each of the `node_degree` candidates was checked against all already-placed entries, giving O(N*degree^2) CPU work that scales poorly with graph degree and dataset size. Replace with a GPU kernel (dedup_graph_kernel) that runs one warp per node. The warp scans InternalID_t neighbors in original order, using __ballot_sync for O(warp-width) duplicate detection, and fills any remaining slots with xorshift64 random nodes. Original neighbor order is preserved, avoiding the recall regression seen with sort-based approaches. The H2D/D2H transfers are O(N*degree) -- the same order as a single pass over the graph -- while the replaced CPU work is O(N*degree^2), so the transfers are dominated by the savings at any practical degree.
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The graph shrink step in
GNND::build()copied the NN-descent output while removing duplicate and self-referencing neighbor IDs using a nested scan: for each of the N nodes, each of thenode_degreecandidates was checked against all already-placed entries, givingO(N*degree^2)CPU work that scales poorly with graph degree and dataset size.Replace with a GPU
dedup_graph_kernelthat runs one warp per node. The warp scans neighbors in original order, using__ballot_syncforO(warp-width)duplicate detection, and fills any remaining slots withxorshift64random nodes. The H2D/D2H transfers areO(N*degree)-- the same order as a single pass over the graph -- while the replaced CPU work isO(N*degree^2), so the transfers are dominated by the savings at any practical degree.Tested on 32-core AMD + H100 with
n_rows=2.5Mandgraph_degree=56, this change reduces runtime by about 6 seconds.