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…-based approach Performance improvements: 1. Pre-compute row-to-column mapping for O(1) branch endpoint lookup instead of O(nnz) sparse row access via A[row,:].nzind on CSC matrix 2. Build adjacency lists upfront for efficient neighbor traversal 3. Replace recursive _new_parent/_reverse_search DFS with iterative queue-based chain peeling algorithm 4. Remove fake threading (Threads.@threads with ReentrantLock serialized all work, adding overhead without parallelism) Agent-Logs-Url: https://github.com/NREL-Sienna/PowerNetworkMatrices.jl/sessions/22fe7baf-bf90-4039-b7d3-fd6ae12cc5f0 Co-authored-by: jd-lara <16385323+jd-lara@users.noreply.github.com>
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[WIP] Improve performance of radial reduction
Optimize radial reduction: iterative queue-based peeling replaces recursive DFS
Apr 14, 2026
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Performance ResultsPrecompile Time
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jd-lara
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This is a great improvement, I added a similar piece of work for the 2-degree. Waiting for @m-bossart approval
m-bossart
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Apr 15, 2026
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calculate_radial_arcshad three performance bottlenecks:A[row_ix, :].nzindon a CSC matrix scans all columns to find nonzeros in a row. Called repeatedly during chain walking._new_parentrecursively calls itself up each chain, compounding the sparse access cost.Threads.@threadswith aReentrantLockaround the entire loop body serializes all work while adding threading overhead.Changes
_build_row_to_cols: single O(nnz) pass over CSC structure, then O(1) endpoint lookups per branch(neighbor_col, row_index)pairs for O(degree) neighbor traversal_reverse_search→_new_parentrecursion: seed queue with degree-1 buses, peel leaves, enqueue parents that become chain nodes (original degree 2)Threads.@threads+ReentrantLock: eliminates threading overhead on what was effectively serial executionWarning
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