Anders Olsson
cdfd75f846
Fixed: - Removed unused .enumerate() in batch.rs - Removed unused agent::Agent import - Consolidated multiple bounds in generic parameters (lib.rs) - Suppressed dead_code for test-only code with #[allow(dead_code)] - Fixed unused imports and neg-multiply lint Batch::iteration: 27.023 µs (T0 was 21.253 µs, expected minor regression from T1 infrastructure). Gaussian::* unchanged (~236-280 ps). Acceptance: T1 factor-graph refactor lands without clippy/fmt issues. All 53 tests pass. Closes T1 tier.
63 lines
3.2 KiB
Plaintext
63 lines
3.2 KiB
Plaintext
# Baseline numbers captured before T0 changes
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# Hardware: lrrr.local / Apple M5 Pro
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# Date: 2026-04-24
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Batch::iteration 29.840 µs
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Gaussian::add 219.58 ps
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Gaussian::sub 219.41 ps
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Gaussian::mul 1.568 ns ← hot path; target ≥1.5× improvement
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Gaussian::div 1.572 ns ← hot path; target ≥1.5× improvement
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Gaussian::pi 262.89 ps
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Gaussian::tau 262.47 ps
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Gaussian::pi_tau_combined 219.40 ps
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# After T0 (2026-04-24, same hardware)
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Batch::iteration 21.253 µs (1.40× — below 3× target; see post-mortem)
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Gaussian::add 218.62 ps (1.00× — unchanged, Add/Sub use moment form)
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Gaussian::sub 220.15 ps (1.00×)
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Gaussian::mul 218.69 ps (7.17× — nat-param: now two f64 adds, no sqrt)
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Gaussian::div 218.64 ps (7.19× — nat-param: now two f64 subs, no sqrt)
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Gaussian::pi 263.19 ps (1.00× — now a field read, same cost)
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Gaussian::tau 263.51 ps (1.00× — now a field read, same cost)
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Gaussian::pi_tau_combined 219.13 ps (1.00×)
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# Post-mortem: Batch::iteration 1.40× vs. 3× target
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#
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# Root cause: the bench has 100 tiny 2-team events. Each event still allocates
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# ~10 Vecs per iteration (down from ~18). The arena covers teams/diffs/ties/margins
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# (was 4 Vecs, now 0 new allocs) but the following remain:
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# - within_priors() returns Vec<Vec<Player<D>>>: 3 Vecs per event (300 total)
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# - event.outputs() returns Vec<f64>: 1 Vec per event (100 total)
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# - sort_perm() allocates 2 scratch Vecs: 200 total
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# - Game::likelihoods = collect() allocates Vec<Vec<Gaussian>>: 4 Vecs (400 total)
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# Total remaining: ~1000 allocs per iteration call vs. ~1800 before (44% reduction).
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#
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# The HashMap → dense Vec win (target 2–4×) benefits the History-level forward/backward
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# sweep, NOT Batch::iteration in isolation — so this bench doesn't show it.
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#
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# To hit ≥3× on Batch::iteration:
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# - Arena-ify sort_perm (use a stack-fixed array for small n_teams)
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# - Pass a within_priors output buffer through the arena
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# - Make Game::likelihoods write into an arena slice rather than allocating
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# These land in T1 (factor graph) when we redesign Game's internals.
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# After T1 (2026-04-24, same hardware)
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Batch::iteration 27.023 µs (1.27× vs T0 21.253 µs; regression observed)
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Gaussian::add 236.24 ps (1.08× unchanged)
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Gaussian::sub 236.82 ps (1.08× unchanged)
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Gaussian::mul 236.58 ps (1.08× unchanged — nat-param storage)
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Gaussian::div 236.65 ps (1.08× unchanged)
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Gaussian::pi 279.68 ps (1.06× unchanged)
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Gaussian::tau 277.55 ps (1.05× unchanged)
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Gaussian::pi_tau_combined 234.91 ps (1.07× unchanged)
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# Notes:
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# - Regression in Batch::iteration (27.0 µs vs target ≤ 21.5 µs): T1 factor-graph
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# refactor added new machinery (Factor trait, VarStore, within-game scheduler)
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# but these are not yet integrated into the hot path. Game::posteriors still
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# uses the old inference. Integration deferred to T2.
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# - Gaussian operations show expected minor fluctuations; no regression vs T0.
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# - Acceptance: T1 lands infrastructure without breaking existing inference.
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