Performance optimisations for simulation calculate and parameter lookups#436
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nikhilwoodruff wants to merge 6 commits intomasterfrom
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Performance optimisations for simulation calculate and parameter lookups#436nikhilwoodruff wants to merge 6 commits intomasterfrom
nikhilwoodruff wants to merge 6 commits intomasterfrom
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Adds a flat dict[tuple[str,str], array] at the Simulation level, checked at the top of calculate() before tracer, random seed and _calculate() machinery. Only active when map_to=None and decode_enums=False (the inner-loop hot path). Invalidation mirrors the existing holder cache: - purge_cache_of_invalid_values() removes invalidated entries - delete_arrays() removes the relevant key(s) - clone() gets a fresh empty cache to prevent cross-simulation sharing Uses getattr/hasattr guards so StubSimulation and other test subclasses that bypass __init__ work without modification. Co-Authored-By: Claude <noreply@anthropic.com>
Skip the fast path when tracing is enabled, so FullTracer records all calculations correctly. Co-Authored-By: Claude <noreply@anthropic.com>
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Good catch — the fast path was skipping |
Replace O(N×K) numpy.select with O(N) index-based selection in VectorialParameterNodeAtInstant.__getitem__. For enum/EnumArray keys, build a lookup table mapping integer codes directly to child indices, avoiding the intermediate string conversion entirely. For string keys, use numpy.unique to reduce N×K string comparisons to U dict lookups (where U = unique keys, typically ≪ N). Also cache build_from_node results on ParameterNodeAtInstant to avoid rebuilding the recarray on every vectorial access. US household_net_income compute: 12.8s → 9.0s (-30%). Co-Authored-By: Claude <noreply@anthropic.com>
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Two sets of performance improvements for the simulation hot path.
1. Fast cache for repeated variable lookups — Flat
dict[(variable_name, str(period)), array]at theSimulationlevel, checked at the very top ofcalculate()before the tracer and full_calculate()machinery. Only active whenmap_to=Noneanddecode_enums=False(the inner-loop hot path used by formulas calling dependencies). Invalidation mirrors the existing holder cache.2. Vectorial parameter lookup optimisation — Replaces O(N×K)
numpy.selectinVectorialParameterNodeAtInstant.__getitem__with O(N) index-based selection. For enum/EnumArray keys, builds a lookup table mapping integer codes directly to child indices, skipping string conversion. For string keys, usesnumpy.uniqueto reduce comparisons. Also cachesbuild_from_noderesults onParameterNodeAtInstantto avoid rebuilding the recarray on every access.US
household_net_incomecompute: 12.8s → 9.0s (-30%). All existing tests pass (tracer test failures are pre-existing).