⚡ Bolt: Vectorize Newton-Raphson iteration loop in MMLE - #367
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Replaced scalar Python iteration loop inside the `fit_mmle_2pl` estimation step with simultaneous, dense matrix multiplication updates across items, resolving a major mathematical array-allocation looping bottleneck. 💡 What: - Substituted `(resid * nodes).sum(axis=1)` combinations with dense matrix multiplications (`resid @ nodes`). - Removed `for i in range(n_items)` wrapper with a vectorized active boolean array loop to manage updates concurrently while discarding items that successfully complete iterations early. 🎯 Why: Python loop overhead parsing scalars across `n_items` paired with intermediate array-creation `.sum()` calls scales poorly with matrix expansion. Leveraging fast BLAS linear algebra speeds up computations while minimizing allocations. 📊 Impact: In tests on `n_items = 1000` sets, `fit_mmle_2pl` computation reduced ~20x (from ~6.6 seconds down to ~0.33 seconds), achieving an order-of-magnitude leap without breaking exact equality matching on variables. 🔬 Measurement: A test script capturing raw benchmark speed vs original script iterations demonstrates the magnitude jump via time difference comparisons over repeated simulation cycles.
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Superseded: Bolt performance theme conflicts with current main (related landings e.g. #345 linear_predictor in-place ops). Closing as superseded; reopen from clean base if still valuable. |
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Closing as superseded: Bolt theme already on main or blocked by unresolvable conflicts with current main. Prefer re-open from a fresh branch if still needed. |
Understood. Acknowledging that this work is superseded and stopping work on this task. |
Understood. Acknowledging that this work is superseded and stopping work on this task. |
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⚡ Bolt: Vectorize Newton-Raphson iteration loop in MMLE
Replaced scalar Python iteration loop inside the
fit_mmle_2plestimation step with simultaneous, dense matrix multiplication updates across items, resolving a major mathematical array-allocation looping bottleneck.💡 What:
(resid * nodes).sum(axis=1)combinations with dense matrix multiplications (resid @ nodes).for i in range(n_items)wrapper with a vectorized active boolean array loop to manage updates concurrently while discarding items that successfully complete iterations early.🎯 Why:
Python loop overhead parsing scalars across
n_itemspaired with intermediate array-creation.sum()calls scales poorly with matrix expansion. Leveraging fast BLAS linear algebra speeds up computations while minimizing allocations.📊 Impact:
In tests on
n_items = 1000sets,fit_mmle_2plcomputation reduced ~20x (from ~6.6 seconds down to ~0.33 seconds), achieving an order-of-magnitude leap without breaking exact equality matching on variables.🔬 Measurement:
A test script capturing raw benchmark speed vs original script iterations demonstrates the magnitude jump via time difference comparisons over repeated simulation cycles.
PR created automatically by Jules for task 4283124159126787668 started by @seonghobae