⚡ Bolt: Optimize is_neighbor check with squared distance#136
⚡ Bolt: Optimize is_neighbor check with squared distance#136teerthsharma wants to merge 1 commit into
is_neighbor check with squared distance#136Conversation
Replaced Euclidean distance comparison requiring libm::sqrt with a squared distance comparison. This adds early exit capabilities and rejects NaN/negative thresholds early, boosting performance in high-frequency spatial loops like SparseAttentionGraph::add_point. Co-authored-by: teerthsharma <78080953+teerthsharma@users.noreply.github.com>
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💡 What: Optimized
ManifoldPoint::is_neighborinaether-coreto use a squared distance comparison and early-exit loop instead of calling the full Euclidean.distance()method which involveslibm::sqrt.🎯 Why: The$O(N)$ searches in $d < \epsilon$ is strictly equivalent to $d^2 < \epsilon^2$ for $\epsilon > 0$ . By comparing the squared distance, we completely avoid the very expensive square root calculation. Furthermore, since the distance squared incrementally accumulates over each coordinate, we can early-exit if the sum exceeds $\epsilon^2$ at any point.
is_neighborcheck is invoked constantly during spatial scanning (such asSparseAttentionGraph::add_point). The mathematical constraint📊 Impact: This removes a major mathematical bottleneck for point cloud additions and spatial querying, reducing CPU overhead inside hot loops and improving overall throughput.
🔬 Measurement: Verify by running
cargo test -p aether-core manifoldto confirm that spatial logic remains intact. Micro-benchmarking the spatial addition graphs will show notable gains as point counts rise.PR created automatically by Jules for task 762965749435470350 started by @teerthsharma