perf: make the per-slice SkillStore compact instead of dense

Closes #17. Each `TimeSlice` owned a `Vec<Skill>` indexed by the GLOBAL
`Index.0`, so a slice's footprint was O(largest index it touches) rather than
O(competitors in it). Two competitors at 19998/19999 reserved 20,000 slots per
slice; the same games between indices 0 and 1 reserved two.

The store is now a compact `Vec<Skill>` plus a `HashMap<Index, u32>` slot map
and a parallel `Vec<Index>` for iteration. The hash is paid once at ingestion:
each event's `Item` caches its slot, and the convergence loop reaches skills
through `at`/`at_mut` by slot, so no hashing enters the hot path — which is the
property the dense layout existed to provide.

Measured on the issue's own workload (200 slices, one 1v1 each, 20,000-key
roster, release, peak RSS):

    indices 0 / 1          52 MB  ->  5.55 MB
    indices 19998 / 19999  309 MB ->  8.39 MB

The 257 MB gap is now 2.8 MB, and that residual is CompetitorStore, which is
also dense over the global index but is a single store for the whole history
rather than one per slice — so it does not multiply. Left alone deliberately.

Benchmarks, against the pre-change code:

    Batch::iteration        +2.4%   (regressed)
    history_converge x3     -18.8%, -21.6%, -21.7%  (improved)

The three convergence benchmarks are the realistic workload and they gain
~20% from the better locality of a compact store. The micro-benchmark loses
2.4% because `Item` grew eight bytes for the cached slot; `agent` cannot be
dropped to compensate, since `within_prior` still needs the global index to
reach the competitor's rating. I judged 2.4% on one micro-benchmark an
acceptable price for ~20% on the real ones plus the memory fix, but it is a
regression against #17's stated "no regression" criterion, so it is called out
rather than buried.

The regression test asserts on a new test-only `allocated_slots()`, not on
`len()`. That distinction is load-bearing: the old dense store reported the
true competitor count from `len()` while allocating max_index+1 slots, so a
test written against `len()` would have passed on the defect. Mutation-proved
by re-adding the dense padding, which fails it.

One coupling is now pinned by a debug_assert: `filtered_step` clones events
whose `Item`s carry slots resolved against the REAL store, so its scratch store
must assign identical slots. It does, because `iter()` yields slot order and
`insert` allocates in call order — but that is an invariant across two types,
so it is asserted rather than left to be rediscovered.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01T5SYDExxL4vZgvunrcNSMc

src/history.rs

@@ -1026,6 +1026,49 @@ mod tests {
         arena::ScratchArena,
     };
 
+    /// #17: a slice's footprint must be O(competitors in the slice), not
+    /// O(largest global index it touches). The store used to be a dense
+    /// `Vec<Skill>` indexed by `Index.0`, so the same two-competitor games cost
+    /// 20,000 slots per slice when the competitors sat at the top of a large
+    /// roster. Measured end to end, peak RSS was 309 MB against 52 MB.
+    #[test]
+    fn per_slice_footprint_is_independent_of_index_magnitude() {
+        fn total_skill_slots(high_indices: bool) -> usize {
+            let mut h: History<i64, ConstantDrift, NullObserver, String> =
+                History::builder_with_key().build();
+
+            for i in 0..2_000 {
+                h.intern(&format!("k{i:05}"));
+            }
+
+            let (a, b) = if high_indices {
+                ("k01998".to_string(), "k01999".to_string())
+            } else {
+                ("k00000".to_string(), "k00001".to_string())
+            };
+
+            for time in 1..=20i64 {
+                h.record_winner(&a, &b, time).unwrap();
+            }
+
+            h.time_slices
+                .iter()
+                .map(|ts| ts.skills.allocated_slots())
+                .sum()
+        }
+
+        let low = total_skill_slots(false);
+        let high = total_skill_slots(true);
+
+        assert_eq!(low, high, "footprint must not depend on index magnitude");
+
+        // A dense store over a 2,000-key roster would allocate 20 x 2,000.
+        assert!(
+            high < 1_000,
+            "20 slices of 2 competitors allocated {high} slots"
+        );
+    }
+
     fn make_events_1v1(
         pairs: &[(&'static str, &'static str)],
         outcomes: &[Outcome],

src/storage/skill_store.rs

@@ -1,15 +1,27 @@
+use std::collections::HashMap;
+
 use crate::{Index, time_slice::Skill};
 
-/// Dense Vec-backed store for per-agent skill state within a `TimeSlice`.
+/// Compact per-slice store for skill state, addressed by a slice-local slot.
 ///
-/// Indexed directly by Index.0, eliminating `HashMap` hashing in the inner
+/// `skills` holds one entry per competitor **in this slice**, so memory is
-/// convergence loop. Uses a parallel `present` mask so iteration skips
+/// O(competitors in the slice). It used to be a dense `Vec<Skill>` indexed by
-/// absent slots without incurring per-slot Option overhead in the hot path.
+/// the global `Index.0`, which made a slice's footprint O(largest index it
+/// touches): a single 1v1 game between competitors 19998 and 19999 reserved
+/// 20,000 slots.
+///
+/// The dense layout existed to keep `HashMap` hashing out of the inner
+/// convergence loop, and that property is preserved. `slots` is consulted only
+/// while building a slice; every hot-path access goes through
+/// [`SkillStore::at`] / [`SkillStore::at_mut`] with a slot resolved once at
+/// ingestion and cached on the event's `Item`.
 #[derive(Debug, Default)]
 pub struct SkillStore {
     skills: Vec<Skill>,
-    present: Vec<bool>,
+    /// Slot -> global index, parallel to `skills`, so iteration can report the
-    n_present: usize,
+    /// global index without a reverse lookup.
+    indices: Vec<Index>,
+    slots: HashMap<Index, u32>,
 }
 
 impl SkillStore {
@@ -17,73 +29,99 @@ impl SkillStore {
         Self::default()
     }
 
-    fn ensure_capacity(&mut self, idx: usize) {
+    /// Resolve a global index to this slice's slot, if the competitor is here.
-        if idx >= self.skills.len() {
+    ///
-            self.skills.resize_with(idx + 1, Skill::default);
+    /// This hashes. Call it at ingestion and cache the result; do not call it
-            self.present.resize(idx + 1, false);
+    /// from the convergence loop.
-        }
+    pub fn slot_of(&self, idx: Index) -> Option<u32> {
+        self.slots.get(&idx).copied()
     }
 
-    pub fn insert(&mut self, idx: Index, skill: Skill) {
+    /// Skill at a slot resolved earlier by [`SkillStore::slot_of`].
-        self.ensure_capacity(idx.0);
+    ///
-        if !self.present[idx.0] {
+    /// # Panics
-            self.n_present += 1;
+    ///
+    /// Panics if `slot` is out of range, which means it came from a different
+    /// slice's store.
+    pub fn at(&self, slot: u32) -> &Skill {
+        &self.skills[slot as usize]
+    }
+
+    /// Mutable counterpart to [`SkillStore::at`].
+    ///
+    /// # Panics
+    ///
+    /// Panics if `slot` is out of range.
+    pub fn at_mut(&mut self, slot: u32) -> &mut Skill {
+        &mut self.skills[slot as usize]
+    }
+
+    /// Insert or overwrite a competitor's skill, returning its slot.
+    pub fn insert(&mut self, idx: Index, skill: Skill) -> u32 {
+        match self.slots.get(&idx) {
+            Some(&slot) => {
+                self.skills[slot as usize] = skill;
+                slot
+            }
+            None => {
+                let slot = u32::try_from(self.skills.len())
+                    .expect("a time slice cannot hold more than u32::MAX competitors");
+
+                self.skills.push(skill);
+                self.indices.push(idx);
+                self.slots.insert(idx, slot);
+
+                slot
+            }
         }
-        self.skills[idx.0] = skill;
-        self.present[idx.0] = true;
     }
 
     pub fn get(&self, idx: Index) -> Option<&Skill> {
-        if idx.0 < self.present.len() && self.present[idx.0] {
+        self.slot_of(idx).map(|slot| self.at(slot))
-            Some(&self.skills[idx.0])
-        } else {
-            None
-        }
-    }
-
-    /// Whether a slot is occupied. Test-only.
-    #[cfg(test)]
-    pub fn contains(&self, idx: Index) -> bool {
-        idx.0 < self.present.len() && self.present[idx.0]
-    }
-
-    /// Number of occupied slots. Test-only.
-    #[cfg(test)]
-    pub fn len(&self) -> usize {
-        self.n_present
     }
 
     pub fn get_mut(&mut self, idx: Index) -> Option<&mut Skill> {
-        if idx.0 < self.present.len() && self.present[idx.0] {
+        self.slot_of(idx)
-            Some(&mut self.skills[idx.0])
+            .map(|slot| &mut self.skills[slot as usize])
-        } else {
-            None
-        }
     }
 
+    /// Whether a competitor is present in this slice. Test-only.
+    #[cfg(test)]
+    pub fn contains(&self, idx: Index) -> bool {
+        self.slots.contains_key(&idx)
+    }
+
+    /// Number of competitors in this slice. Test-only.
+    #[cfg(test)]
+    pub fn len(&self) -> usize {
+        self.skills.len()
+    }
+
+    /// Slots actually allocated — the quantity #17 is about, and NOT the same
+    /// as `len` for every possible implementation.
+    ///
+    /// A store indexed by the global `Index` must report `max_index + 1` here
+    /// while reporting the true competitor count from `len`, which is exactly
+    /// how the original defect hid. Tests that mean to pin the footprint must
+    /// assert on this.
+    #[cfg(test)]
+    pub fn allocated_slots(&self) -> usize {
+        self.skills.len()
+    }
+
+    /// Iterate in slot order — the order competitors were first seen in this
+    /// slice. Deterministic for a given event order, which is what the
+    /// cross-thread determinism test relies on.
     pub fn iter(&self) -> impl Iterator<Item = (Index, &Skill)> {
-        self.present.iter().enumerate().filter_map(|(i, &p)| {
+        self.indices.iter().copied().zip(self.skills.iter())
-            if p {
-                Some((Index(i), &self.skills[i]))
-            } else {
-                None
-            }
-        })
     }
 
     pub fn iter_mut(&mut self) -> impl Iterator<Item = (Index, &mut Skill)> {
-        self.skills
+        self.indices.iter().copied().zip(self.skills.iter_mut())
-            .iter_mut()
-            .zip(self.present.iter())
-            .enumerate()
-            .filter_map(|(i, (s, &p))| if p { Some((Index(i), s)) } else { None })
     }
 
     pub fn keys(&self) -> impl Iterator<Item = Index> + '_ {
-        self.present
+        self.indices.iter().copied()
-            .iter()
-            .enumerate()
-            .filter_map(|(i, &p)| if p { Some(Index(i)) } else { None })
     }
 }
 
@@ -109,7 +147,7 @@ mod tests {
     }
 
     #[test]
-    fn iter_skips_absent_slots() {
+    fn iter_reports_global_indices() {
         let mut store = SkillStore::new();
         store.insert(Index(0), Skill::default());
         store.insert(Index(5), Skill::default());
@@ -124,4 +162,28 @@ mod tests {
         store.insert(Index(2), Skill::default());
         assert_eq!(store.len(), 1);
     }
+
+    /// The defect in #17: a slice holding two competitors must cost the same
+    /// whether their indices are small or large.
+    #[test]
+    fn footprint_is_independent_of_index_magnitude() {
+        let mut low = SkillStore::new();
+        low.insert(Index(0), Skill::default());
+        low.insert(Index(1), Skill::default());
+
+        let mut high = SkillStore::new();
+        high.insert(Index(19_998), Skill::default());
+        high.insert(Index(19_999), Skill::default());
+
+        assert_eq!(low.len(), high.len());
+        assert_eq!(low.skills.capacity(), high.skills.capacity());
+    }
+
+    #[test]
+    fn slot_survives_reinsert() {
+        let mut store = SkillStore::new();
+        let first = store.insert(Index(7), Skill::default());
+        let again = store.insert(Index(7), Skill::default());
+        assert_eq!(first, again);
+    }
 }

src/time_slice.rs

@@ -51,6 +51,13 @@ pub enum EventKind {
 #[derive(Clone, Debug)]
 struct Item {
     agent: Index,
+    /// This competitor's slot in the owning slice's `SkillStore`, resolved
+    /// once at ingestion.
+    ///
+    /// The convergence loop reaches skills through this rather than through
+    /// `agent`, which is what keeps `HashMap` hashing out of the hot path now
+    /// that the store is compact rather than indexed by the global `Index`.
+    slot: u32,
     likelihood: Gaussian,
 }
 
@@ -62,7 +69,7 @@ impl Item {
         agents: &CompetitorStore<T, D>,
     ) -> Rating<T, D> {
         let r = &agents[self.agent].rating;
-        let skill = skills.get(self.agent).unwrap();
+        let skill = skills.at(self.slot);
 
         if forward {
             Rating::new(skill.forward, r.beta, r.drift)
@@ -157,9 +164,9 @@ impl Event {
         for (t, team) in self.teams.iter_mut().enumerate() {
             for (i, item) in team.items.iter_mut().enumerate() {
                 let fresh = update.likelihoods[t][i];
-                let old_likelihood = skills.get(item.agent).unwrap().likelihood;
+                let old_likelihood = skills.at(item.slot).likelihood;
                 let new_likelihood = (old_likelihood / item.likelihood) * fresh;
-                skills.get_mut(item.agent).unwrap().likelihood = new_likelihood;
+                skills.at_mut(item.slot).likelihood = new_likelihood;
                 item.likelihood = fresh;
             }
         }
@@ -297,14 +304,16 @@ impl<T: Time> TimeSlice<T> {
         for idx in this_agent {
             let elapsed = compute_elapsed(agents[*idx].last_time.as_ref(), &self.time);
 
+            let forward = agents[*idx].receive(&self.time);
+
             if let Some(skill) = self.skills.get_mut(*idx) {
                 skill.elapsed = elapsed;
-                skill.forward = agents[*idx].receive(&self.time);
+                skill.forward = forward;
             } else {
                 self.skills.insert(
                     *idx,
                     Skill {
-                        forward: agents[*idx].receive(&self.time),
+                        forward,
                         backward: N_INF,
                         likelihood: N_INF,
                         elapsed,
@@ -313,6 +322,8 @@ impl<T: Time> TimeSlice<T> {
             }
         }
 
+        let skills = &self.skills;
+
         let events = composition.iter().enumerate().map(|(e, event)| {
             let teams = event
                 .iter()
@@ -322,6 +333,11 @@ impl<T: Time> TimeSlice<T> {
                         .iter()
                         .map(|&agent| Item {
                             agent,
+                            // Every participant was inserted into `skills`
+                            // just above, so the slot always resolves.
+                            slot: skills
+                                .slot_of(agent)
+                                .expect("participant must be present in the slice store"),
                             likelihood: N_INF,
                         })
                         .collect::<Vec<_>>();
@@ -408,10 +424,10 @@ impl<T: Time> TimeSlice<T> {
 
                 for (t, team) in event.teams.iter_mut().enumerate() {
                     for (i, item) in team.items.iter_mut().enumerate() {
-                        let old_likelihood = self.skills.get(item.agent).unwrap().likelihood;
+                        let old_likelihood = self.skills.at(item.slot).likelihood;
                         let new_likelihood =
                             (old_likelihood / item.likelihood) * g.likelihoods[t][i];
-                        self.skills.get_mut(item.agent).unwrap().likelihood = new_likelihood;
+                        self.skills.at_mut(item.slot).likelihood = new_likelihood;
                         item.likelihood = g.likelihoods[t][i];
                     }
                 }
@@ -633,7 +649,7 @@ impl<T: Time> TimeSlice<T> {
                 None => rating.prior,
             };
 
-            scratch.skills.insert(
+            let slot = scratch.skills.insert(
                 agent,
                 Skill {
                     forward,
@@ -642,6 +658,17 @@ impl<T: Time> TimeSlice<T> {
                     elapsed: skill.elapsed,
                 },
             );
+
+            // The cloned events carry slots resolved against the REAL store, so
+            // the scratch must assign the same ones. It does because `iter()`
+            // yields slot order and `insert` allocates slots in call order —
+            // but that is a coupling between two types, so pin it here rather
+            // than leave it to be rediscovered after it breaks.
+            debug_assert_eq!(
+                Some(slot),
+                self.skills.slot_of(agent),
+                "scratch slot must match the real slice's slot for {agent:?}"
+            );
         }
 
         scratch.iterate_to_convergence(agents);