evidence/CB-EV-0001-game-kernel.md records the acceptance run against the CB-RES-0001 baseline. Met: AM-1 rule coverage 58/58; AM-6 throughput 1.65M events/s against a 100k target; AM-7 scaling 1.08x at 20x workload and a 100k-event replay in 4.13ms against a 5s budget; AM-8 zero divergence over 10 full runs with fmt and clippy clean; AM-10 zero foreign collection types. Not met and reported as such: AM-4 at 33 transitive crates against a <=20 target. Attribution is in the evidence file. The recommended fix is making serde_yaml optional (-5, a test-only concern), after which the remainder is sha2 and rand_chacha, which K5 and K7 require. We are not hand-rolling crypto primitives to win a dependency count. AM-12 is recorded as uncomputable: per-task token counts were never instrumented, and inventing a USD figure would defeat the metric. A measurement error was found and corrected before publication. The first benchmark reported 9.3M events/s on a flat curve. The workload had a player selecting SUPPORT while parked at Stress 4, so GR-R03 rejected it, rounds never completed, and throughput was computed for rounds that never happened. The bench now asserts the per-round event count and panics rather than measuring a stalled loop. The corrected figure is 5.6x lower. The evidence file states plainly what the boardgame.io comparison does and does not support: the ~450x command-rate ratio is cross-runtime and cross-feature-set, so it is a direction, not a verdict, per the InnerLoop parity-cap rule. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
220 lines
8 KiB
Rust
220 lines
8 KiB
Rust
//! AM-6/AM-7 benchmarks (GameKernel §4): the real GROUND aggregate under
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//! the CB-RES-0001 synthetic workload (3-player commit/reveal rounds).
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//!
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//! The baseline is boardgame.io, recorded in
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//! research/CB-RES-0001-harness/boardgame-io/results-260731.json. That
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//! harness measured moves/second *while history grew*, and its headline
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//! finding was that throughput halved as history doubled. AM-7 exists
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//! because of that finding, so the same workload sizes are run here:
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//! what matters is the shape of the curve, not only the peak number.
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use cb_events::state_hash_hex;
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use cb_game_runtime::{ScenarioGame, Setup};
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use cb_kernel::{Actor, Aggregate, PlayerId};
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use criterion::{criterion_group, criterion_main, BatchSize, Criterion, Throughput};
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use games_ground::{Action, GroundCommand, GroundMode, GroundState};
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use std::collections::BTreeMap;
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fn setup(seed: u64) -> GroundState {
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GroundState::setup(
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&Setup {
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players: 3,
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preset: "standard-3p".to_string(),
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patch: BTreeMap::new(),
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},
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seed,
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)
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.expect("standard-3p setup")
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}
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fn apply(state: &mut GroundState, actor: Actor, command: &GroundCommand) -> usize {
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match state.validate(actor, command) {
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Ok(events) => {
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for event in &events {
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state.fold(event);
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}
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events.len()
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}
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Err(_) => 0,
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}
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}
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/// One full round for three players: three Selects, Reveal, the GROUND
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/// mode choice, Resolve, End. Returns the number of events applied.
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fn play_round(state: &mut GroundState) -> usize {
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let picks = [
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(
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PlayerId(0),
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GroundCommand::SelectAction {
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action: Action::Attack,
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target: Some(PlayerId(1)),
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problem: None,
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},
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),
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(
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PlayerId(2),
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GroundCommand::SelectAction {
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action: Action::Support,
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target: Some(PlayerId(1)),
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problem: None,
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},
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),
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(
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PlayerId(1),
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GroundCommand::SelectAction {
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action: Action::Ground,
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target: None,
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problem: None,
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},
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),
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];
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let mut applied = 0;
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for (seat, command) in picks {
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applied += apply(state, Actor::Player(seat), &command);
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}
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applied += apply(state, Actor::System, &GroundCommand::Reveal);
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// GR-R05: the GROUND player picks a mode before resolution.
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applied += apply(
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state,
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Actor::Player(PlayerId(1)),
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&GroundCommand::ChooseGroundMode {
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mode: GroundMode::Gr,
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choice: None,
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},
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);
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applied += apply(state, Actor::System, &GroundCommand::Resolve);
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applied += apply(state, Actor::System, &GroundCommand::EndRound);
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applied
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}
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/// A game ends after Round 5 (GR-R09), so a long run starts a fresh game
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/// rather than idling on a finished one. Setup cost is therefore part of
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/// the measurement, at one setup per five rounds.
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fn run_rounds(rounds: usize) -> usize {
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let mut applied = 0;
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let mut state = setup(42);
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for round in 0..rounds {
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if state.outcome.is_some() {
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state = setup(42 + round as u64);
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}
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let produced = play_round(&mut state);
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// A workload whose commands get rejected still "runs", but it
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// measures nothing. An earlier version of this bench stalled on
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// the GR-R03 stress gate and reported throughput for rounds that
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// never happened, so refuse to measure that.
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assert!(
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produced == EVENTS_PER_ROUND || produced == FINAL_ROUND_EVENTS,
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"round {round} produced {produced} events, expected {EVENTS_PER_ROUND} \
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(or {FINAL_ROUND_EVENTS} on a game's last round)"
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);
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applied += produced;
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}
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applied
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}
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/// Pinned by the `bench_shape` test in the aggregate crate.
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const EVENTS_PER_ROUND: usize = 13;
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/// GR-R09: a game's fifth round emits GameEnded instead of RoundEnded
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/// plus StepAdvanced, so it is one event shorter.
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const FINAL_ROUND_EVENTS: usize = 12;
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/// Mean events per round over a 5-round game, scaled by 5 to stay in
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/// integers: (4 x 13 + 12) = 64.
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const EVENTS_PER_5_ROUNDS: usize = 64;
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fn bench_synthetic(c: &mut Criterion) {
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// Events per round is fixed by the workload, so throughput can be
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// reported in events/second — the AM-6 unit.
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assert_eq!(play_round(&mut setup(1)), EVENTS_PER_ROUND);
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let mut group = c.benchmark_group("synthetic-ground-3p");
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// AM-6 headline throughput and AM-7 scaling, at the sizes the
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// boardgame.io harness used so the curves line up.
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for &rounds in &[5_000usize, 10_000, 20_000, 40_000, 100_000] {
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group.throughput(Throughput::Elements(
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(rounds * EVENTS_PER_5_ROUNDS / 5) as u64,
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));
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group.bench_function(format!("rounds-{rounds}"), |b| {
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b.iter_batched(|| rounds, run_rounds, BatchSize::SmallInput)
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});
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}
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group.finish();
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// AM-7 replay, and the honest analogue of the boardgame.io finding:
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// a *single* growing event log folded back into state. The round
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// benches above restart the game every 5 rounds (GR-R09), so their
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// flat curve is partly by construction — this one is not, because
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// the log here grows without bound.
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let mut replay = c.benchmark_group("replay-ground-3p");
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for &events in &[10_000usize, 100_000] {
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replay.throughput(Throughput::Elements(events as u64));
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replay.bench_function(format!("fold-{events}-events"), |b| {
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// Build one log of `events` events, then measure folding it.
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let mut source = setup(42);
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let mut log = Vec::with_capacity(events);
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while log.len() < events {
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if source.outcome.is_some() {
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source = setup(43);
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}
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let picks = [
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(
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PlayerId(0),
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GroundCommand::SelectAction {
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action: Action::Attack,
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target: Some(PlayerId(1)),
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problem: None,
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},
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),
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(
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PlayerId(1),
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GroundCommand::SelectAction {
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action: Action::Support,
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target: Some(PlayerId(2)),
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problem: None,
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},
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),
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];
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for (seat, cmd) in picks {
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if let Ok(produced) = source.validate(Actor::Player(seat), &cmd) {
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for e in &produced {
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source.fold(e);
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log.push(e.clone());
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}
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}
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}
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if let Ok(produced) = source.validate(Actor::System, &GroundCommand::Reveal) {
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for e in &produced {
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source.fold(e);
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log.push(e.clone());
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}
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}
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if let Ok(produced) = source.validate(Actor::System, &GroundCommand::EndRound) {
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for e in &produced {
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source.fold(e);
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log.push(e.clone());
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}
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}
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}
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b.iter(|| {
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let mut state = setup(42);
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for event in &log {
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state.fold(event);
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}
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state_hash_hex(&state)
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})
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});
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}
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replay.finish();
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// AM-7: hashing the full aggregate, the per-round determinism cost.
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let mut hashing = c.benchmark_group("state-hash-ground-3p");
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hashing.throughput(Throughput::Elements(1));
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hashing.bench_function("hash-one-state", |b| {
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let state = setup(42);
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b.iter(|| state_hash_hex(&state))
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});
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hashing.finish();
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}
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criterion_group!(benches, bench_synthetic);
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criterion_main!(benches);
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