⚡ Bolt: Optimize sparse attention graph distance calculations - #111
⚡ Bolt: Optimize sparse attention graph distance calculations#111teerthsharma wants to merge 1 commit into
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What: Optimized `ManifoldPoint::is_neighbor` in `crates/aether-core/src/manifold.rs` to use squared distance comparisons with an early exit condition, bypassing the expensive `libm::sqrt` function. Additionally, safely handled `NaN` coordinates and thresholds by using negated bound checks. Also fixed a pathological performance bug in `auto_k_selection` within `crates/aether-core/src/ml/clustering.rs` where the number of components was incorrectly returning the dataset size. Why: The `SparseAttentionGraph::add_point` method performs intensive O(N) spatial scans and uses `is_neighbor` heavily. Avoiding square root calculations in this hot path yields a significant performance boost. The `auto_k_selection` fix prevents K-Means from devolving to worst-case complexity by correctly determining the topological $k$. Impact: Substantially decreases CPU overhead for manifold embedding and prevents massive pathological slowdowns in topological K-Means clustering. Measurement: Verify that all unit tests pass, especially the newly fixed `test_auto_k`, and note the execution time improvements of embedding algorithms using `SparseAttentionGraph`. Co-authored-by: teerthsharma <78080953+teerthsharma@users.noreply.github.com>
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💡 What:
Optimized
ManifoldPoint::is_neighborincrates/aether-core/src/manifold.rsto use squared distance comparisons with an early exit condition, bypassing the expensivelibm::sqrtfunction. Additionally, safely handledNaNcoordinates and thresholds by using negated bound checks. Also fixed a pathological performance bug inauto_k_selectionwithincrates/aether-core/src/ml/clustering.rswhere the number of components was incorrectly returning the dataset size.🎯 Why:$k$ .
The
SparseAttentionGraph::add_pointmethod performs intensive O(N) spatial scans and usesis_neighborheavily. Avoiding square root calculations in this hot path yields a significant performance boost. Theauto_k_selectionfix prevents K-Means from devolving to worst-case complexity by correctly determining the topological📊 Impact:
Substantially decreases CPU overhead for manifold embedding and prevents massive pathological slowdowns in topological K-Means clustering.
🔬 Measurement:
Verify that all unit tests pass, especially the newly fixed
test_auto_k, and note the execution time improvements of embedding algorithms usingSparseAttentionGraph.PR created automatically by Jules for task 16112420087768161793 started by @teerthsharma