Research map / Learning theory and algorithms / Clustering
Differential Performance Debugging With Discriminant Regression Trees
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Cluster traces into functional performance groups before learning a decision tree over auxiliary features | Differential performance debugging · Synthetic microbenchmarks | R²: For R_1600, R² is 0.99 for DPDebugger and 0.73 for GUIDE | Helped |
| Use K-linear clustering instead of spectral clustering (ablation) | DPDebugger · Synthetic benchmarks with linear clusters | running time: R_7: 31.8 -> 210.1 seconds | Helped |
| Use spectral clustering with the alignment kernel instead of RBF or nearest-neighborhood kernels (ablation) | DPDebugger clustering · Linear-cluster data | clustering quality | Helped |
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