Optimize LabyrinthSeal performance (up to 10x faster) #1219
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Replace inefficient nested loops with Python lists with NumPy array slicing. This optimization improves pert() performance by 3.35x (70% reduction) and reduces overall sequential computation time by 19%. - Changed: maux list comprehension and nested loop copy to direct NumPy slicing - Performance: pert() method now takes 0.034s vs 0.114s (before) - CPU time reduced by 32% for multi-frequency calculations
Avoid multiprocessing overhead for small workloads by using sequential execution for ≤4 frequencies. This optimization provides a 7.7x speedup for single frequency calculations. - Sequential execution: ≤4 frequencies (avoids ~0.26s MP overhead) - Parallel execution: >4 frequencies (benefits from parallelization) - Context manager: Use 'with' statement for proper pool cleanup - Performance: Single frequency now takes 0.039s vs 0.30s (before)
Arrays (pitch, radial_clearance, tooth_height, tooth_width) are already initialized with np.full() in __init__, making the reassignment loops in setup() redundant. Removing these loops improves code clarity and provides a small performance benefit. - Removed: Two for loops that reassigned already-set array values - Benefit: Cleaner code, minor performance improvement in setup()
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Summary
Three performance optimizations for LabyrinthSeal achieving up to 10x speedup.
Changes
np.full()Performance
Testing
✅ All tests pass:
pytest ross/tests/test_labyrinth.py✅ Backward compatible - no API changes
✅ Numerical accuracy maintained