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perf: array-based BFS in computeStress() — eliminates HashMap/boxing overhead - #124
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The stress computation previously allocated V HashMap<String,Integer> objects (one per source vertex via GraphUtils.bfsDistances), each with V entries containing boxed Integer values. For a 1000-node graph this created ~1M boxed integers and ~1000 HashMaps that were used once and immediately garbage-collected. Replace with array-based BFS using reusable int[] arrays: - Single int[n] distance array reset with Arrays.fill per source - int[n] queue array reused across all BFS iterations - int[][] adjacency list built once (same pattern as NodeCentralityAnalyzer) - Pre-extracted positions into flat double[][] for cache-friendly access - Eliminates all String-based HashMap lookups in the inner loop This reduces GC pressure and improves cache locality, particularly noticeable on graphs with 500+ vertices where computeStress() is the dominant cost in getQualityReport().
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Closing: superseded or conflicting with newer changes already on main/master. |
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What
Replaces HashMap-based all-pairs BFS in \ForceDirectedLayout.computeStress()\ with array-based BFS using reusable \int[]\ arrays.
Why
The previous implementation allocated V \HashMap<String,Integer>\ objects (one per source via \GraphUtils.bfsDistances), each containing V entries with boxed Integer values. For a 1000-node graph: ~1M boxed integers + ~1000 HashMaps, all immediately GC'd.
How
Impact
Reduces GC pressure and improves cache locality. Most noticeable on graphs with 500+ vertices where \computeStress()\ dominates \getQualityReport()\ cost.
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