bench: arithmetic operation micro-benchmarks#39
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An op-level tier alongside the whole-model builds: one benchmark per (operation, size profile), operands built outside the measured region so a run isolates a single op rather than a whole build. This attributes perf changes to a specific arithmetic path — a build benchmark says "kvl got heavier", an op benchmark says "expr+expr broadcast got heavier". - benchmarks/ops.py: op registry (OpSpec) + size profiles (small 1D×2000; large 3D×3×4×1000 — differ in element count *and* dim count; the asymmetric shape also catches dim-order bugs) + ~30 ops across scaling, var/expr arithmetic, quadratic, reductions and constraint construction. Binary labelled ops carry match/broadcast variants — the alignment-path axis where the interesting regressions live. - benchmarks/drivers/test_ops.py: parametrized driver, one benchmark per (op, profile). - conftest: add test_ops to CODSPEED_MODULES (tracked; memory advisory). 60 benchmarks, ~80s/run with memory. Signal validates: large ≈ 6× small, broadcast ≈ 5× match (the §9 cross-product).
Collapse to one 3-D profile (3×4×1000, ~12 K elements) — CodSpeed records time *and* memory per benchmark, so a second size wasn't buying a separate signal; one multi-dim profile keeps broadcast/alignment coverage with MB-scale ops above the noise floor, and halves the matrix. Benchmark ids drop the size suffix. Add three categories: absence/masking (expr.where / fillna / absence propagation — §4–§7, the semantics-heavy surface), groupby.sum, and an N-way merge (constraint-assembly cost). 35 ops, ~45 s/run with memory.
Merging this PR will not alter performance
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Op-level benchmark tier (35 ops, one 3-D profile) to measure CodSpeed cost on the fork before upstreaming (PyPSA PyPSA#805). See benchmarks/ops.py.