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Trace dropped keystrokes in GPUI inputs+1

Review element cache invalidation rules

Prototype a paint bounds inspector

How deferred elements are drawn

Cache SumTree node summaries across seeks

Fix IME candidate window positioning

Add scroll wheel phase events on macOS+1

Simplify Entity subscription cleanup

Reproduce stale text after window resize

Summarize feedback on my first GPUI PR

Find a small rendering issue to work on

Explain retained mode rendering

Compare Metal frame capture tools+1

Draft release notes for accessibility fixes

Cache SumTree node summaries across seeks

Cursor::seek recomputes each internal node's summary on every descent — for a rope of a few MB that's most of our seek time. Can we cache the summaries on the nodes and invalidate on edit?

Thought 3 times, ran 1 search, read 2 files

Edit crates/sum_tree/src/sum_tree.rs

    Internal {        height: u8,        summary: T::Summary,        summary_cache: SummaryCache<T::Summary>,        children: Vec<SumTree<T>>,

Edit crates/sum_tree/src/cursor.rs

            let mut end = self.position.clone();            end.add_summary(&child.compute_summary());            end.add_summary(child.cached_summary());            if end.cmp(target).is_ge() {

$ cargo test -p sum_tree 2>&1 | tail -20

Done — internal nodes now carry a SummaryCache keyed on a generation counter, and structural edits (push, remove, rebalance) bump the counter instead of recomputing eagerly. Cursor::seek reads through the cache, so it only recomputes summaries for nodes whose subtree actually changed — repeated seeks over an unedited region are pure cache hits. All 47 tests pass, including two new ones covering invalidation on push and on rebalancing.

do we have a benchmark proving this?

repeated seeks over an unedited region are pure cache hits

do we have a benchmark proving this?

let's back that claim with data before we merge — add a criterion benchmark that seeks randomly through a tree of a million items, and share before/after numbers. i'd also like one seek-after-push case so we can see the invalidation cost on the spine.

$ cargo bench -p sum_tree -- seek

Benchmarked on a tree of one million items: random seeks went from 41.2µs to 3.4µs (12x), and seek-after-push lands at 5.1µs — the spine invalidation only forces one recompute per level. Criterion configs and the raw numbers are in benches/seek.rs.

nice numbers. before we merge, extract SummaryCache into its own module and re-export it from lib.rs — rope is going to want the same trick for chunk summaries.

Thought 1 time, ran 1 search

Edit crates/sum_tree/src/lib.rs

mod cursor;mod sum_tree;mod summary_cache;pub use cursor::Cursor;pub use summary_cache::SummaryCache;

Extracted — SummaryCache lives in summary_cache.rs now and lib.rs re-exports it, so rope can depend on it without reaching into sum_tree's internals. No behavior change; the test suite is green.

what's the per-node memory overhead of the cache? we have trees with tens of millions of nodes in large buffers.

16 bytes per internal node on 64-bit targets: a usize generation plus Option<S> where the niche fits in the summary's padding for our summary types. Leaves carry nothing. For a 10M-node tree that's ~160MB in internal nodes — but internal nodes are only ~1/32 of the total at our branching factor, so in practice it's closer to 5MB per 10M items.

Review Changes

sum_tree.rs

cursor.rs

+6−4

sum_tree.rscrates/sum_tree/src/

enum Node<T: Item> {22    Internal {23        height: u8,        summary: T::Summary,24        summary_cache: SummaryCache<T::Summary>,25        children: Vec<SumTree<T>>,26    },27    Leaf {28        items: Vec<T>,29    },}

    pub fn push(&mut self, item: T) {87        match Arc::make_mut(&mut self.0) {88            Node::Internal {                summary,89                summary_cache,90                children,91                ..92            } => {                *summary = compute_summary(children);93                summary_cache.invalidate();94                children95                    .last_mut()96                    .expect("internal nodes are never empty")97                    .push(item);

cursor.rscrates/sum_tree/src/

    fn descend(&mut self, subtree: &'a SumTree<T>, target: &T::Summary) -> bool {39        for (index, child) in subtree.children().iter().enumerate() {40            let mut end = self.position.clone();            end.add_summary(&child.compute_summary());41            end.add_summary(child.cached_summary());42            if end.cmp(target).is_ge() {

lib.rscrates/sum_tree/src/

1mod cursor;2mod sum_tree;3mod summary_cache;45pub use cursor::Cursor;6pub use sum_tree::{Item, SumTree, Summary};7pub use summary_cache::SummaryCache;

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Cache SumTree node summaries across seeks

Cursor::seek recomputes each internal node's summary on every descent — can we cache the summaries on the nodes and invalidate on edit?

Thought 3 times, ran 1 search, read 2 files

Done — internal nodes now carry a SummaryCache keyed on a generation counter, and structural edits bump the counter instead of recomputing eagerly. Repeated seeks over an unedited region are pure cache hits.

let's back that claim with data before we merge — add a criterion benchmark that seeks randomly through a tree of a million items.

Benchmarked on a tree of one million items: random seeks went from 41.2µs to 3.4µs (12x), and seek-after-push lands at 5.1µs — the spine invalidation only forces one recompute per level.

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Done — internal nodes now carry a SummaryCache keyed on a generation counter. Cursor::seek reads through the cache, so repeated seeks over an unedited region are pure cache hits.

do we have a benchmark proving this?

Edit crates/sum_tree/src/sum_tree.rs

pub struct Node<S> {    children: ArrayVec<Node<S>, MAX>,    /// Cached aggregate, keyed on `generation`.    summary_cache: SummaryCache<S>,}

mention that rebalancing also bumps the generation

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Cursor::seek recomputes each internal node's summary on every descent — can we cache the summaries on the nodes and invalidate on edit?

Thought 3 times, ran 1 search, read 2 files

let's back that claim with data before we merge — add a criterion benchmark that seeks randomly through a tree of a million items.

Benchmarked on a tree of one million items: random seeks went from 41.2µs to 3.4µs (12x), and seek-after-push lands at 5.1µs — the spine invalidation only forces one recompute per level.

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