Anders Olsson
d3cfee53a1
Batch::iteration: 29.840 µs → 21.253 µs (1.40×) Gaussian::mul: 1.568 ns → 218.69 ps (7.17×) Gaussian::div: 1.572 ns → 218.64 ps (7.19×) Gaussian arithmetic hit target (7×+ vs 1.5–2× expected). Batch::iteration reached 1.40× vs the 3× target. Post-mortem: the bench exercises 100 tiny 2-team events and the dominant cost is still Vec allocation in within_priors, sort_perm, and Game::likelihoods. The HashMap→Vec win shows at the History level (forward/backward sweep) which this bench doesn't exercise. Remediation plan documented in benches/baseline.txt: arena-ify sort_perm, within_priors, and Game::likelihoods in T1 when Game's internals are redesigned around the new factor graph. 38/38 tests passing. Closes T0 tier.
44 lines
2.2 KiB
Plaintext
44 lines
2.2 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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