[rustc][data_structures] Simplify binary_search_slice. - #114152
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| // at this point start either points at the first entry with equal key | |
| // or is equal to size in case all elements have smaller keys | |
| // At this point `start` either points at the first entry with equal or greater key | |
| // or is equal to `size` in case all elements have smaller keys |
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This looks good, modulo the comment nit. |
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@bors r+ rollup |
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…iaskrgr Rollup of 3 pull requests Successful merges: - rust-lang#114129 (Rustdoc small cleanups) - rust-lang#114152 ([rustc][data_structures] Simplify binary_search_slice.) - rust-lang#114222 (Mark `lazy_type_alias` as incomplete) r? `@ghost` `@rustbot` modify labels: rollup
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…llot [rustc_data_structures] Use partition_point to find slice range end. This PR uses approach introduced in rust-lang#114152 to find the end of the range. It's much easier to understand and reason about invariants of such implementation. Technically it's possible to make it even shorter by returning `&[start..end]` unconditionally because even if searched item is not present in the slice, `start` and `end` would point at the same index, so the range would be empty. The reason I decided not to use this shorter implementation is because it would involve more comparisons in case there are no elements in the slice with key equal to `key`. Also, not that it matters much, but this implementation also improves perf according to the benchmark below: https://gist.github.com/ttsugriy/63c0ed39ae132b131931fa1f8a3dea55 The results on my M1 macbook air are: ``` Running benches/bin_search_slice_benchmark.rs (target/release/deps/bin_search_slice_benchmark-90fa6d68c3bd1298) Benchmarking multiply add/binary_search_slice: Collecting 100 samples in estimated 5.0002 s (1 multiply add/binary_search_slice time: [44.719 ns 44.918 ns 45.158 ns] No change in performance detected. Found 3 outliers among 100 measurements (3.00%) 1 (1.00%) high mild 2 (2.00%) high severe Benchmarking multiply add/binary_search_slice_new: Collecting 100 samples in estimated 5.0001 multiply add/binary_search_slice_new time: [36.955 ns 37.060 ns 37.221 ns] No change in performance detected. Found 7 outliers among 100 measurements (7.00%) 3 (3.00%) high mild 4 (4.00%) high severe ```
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[rustc_data_structures] Use partition_point to find slice range end. This PR uses approach introduced in rust-lang/rust#114152 to find the end of the range. It's much easier to understand and reason about invariants of such implementation. Technically it's possible to make it even shorter by returning `&[start..end]` unconditionally because even if searched item is not present in the slice, `start` and `end` would point at the same index, so the range would be empty. The reason I decided not to use this shorter implementation is because it would involve more comparisons in case there are no elements in the slice with key equal to `key`. Also, not that it matters much, but this implementation also improves perf according to the benchmark below: https://gist.github.com/ttsugriy/63c0ed39ae132b131931fa1f8a3dea55 The results on my M1 macbook air are: ``` Running benches/bin_search_slice_benchmark.rs (target/release/deps/bin_search_slice_benchmark-90fa6d68c3bd1298) Benchmarking multiply add/binary_search_slice: Collecting 100 samples in estimated 5.0002 s (1 multiply add/binary_search_slice time: [44.719 ns 44.918 ns 45.158 ns] No change in performance detected. Found 3 outliers among 100 measurements (3.00%) 1 (1.00%) high mild 2 (2.00%) high severe Benchmarking multiply add/binary_search_slice_new: Collecting 100 samples in estimated 5.0001 multiply add/binary_search_slice_new time: [36.955 ns 37.060 ns 37.221 ns] No change in performance detected. Found 7 outliers among 100 measurements (7.00%) 3 (3.00%) high mild 4 (4.00%) high severe ```
lnicola
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Apr 7, 2024
[rustc_data_structures] Use partition_point to find slice range end. This PR uses approach introduced in rust-lang/rust#114152 to find the end of the range. It's much easier to understand and reason about invariants of such implementation. Technically it's possible to make it even shorter by returning `&[start..end]` unconditionally because even if searched item is not present in the slice, `start` and `end` would point at the same index, so the range would be empty. The reason I decided not to use this shorter implementation is because it would involve more comparisons in case there are no elements in the slice with key equal to `key`. Also, not that it matters much, but this implementation also improves perf according to the benchmark below: https://gist.github.com/ttsugriy/63c0ed39ae132b131931fa1f8a3dea55 The results on my M1 macbook air are: ``` Running benches/bin_search_slice_benchmark.rs (target/release/deps/bin_search_slice_benchmark-90fa6d68c3bd1298) Benchmarking multiply add/binary_search_slice: Collecting 100 samples in estimated 5.0002 s (1 multiply add/binary_search_slice time: [44.719 ns 44.918 ns 45.158 ns] No change in performance detected. Found 3 outliers among 100 measurements (3.00%) 1 (1.00%) high mild 2 (2.00%) high severe Benchmarking multiply add/binary_search_slice_new: Collecting 100 samples in estimated 5.0001 multiply add/binary_search_slice_new time: [36.955 ns 37.060 ns 37.221 ns] No change in performance detected. Found 7 outliers among 100 measurements (7.00%) 3 (3.00%) high mild 4 (4.00%) high severe ```
RalfJung
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Apr 27, 2024
[rustc_data_structures] Use partition_point to find slice range end. This PR uses approach introduced in rust-lang/rust#114152 to find the end of the range. It's much easier to understand and reason about invariants of such implementation. Technically it's possible to make it even shorter by returning `&[start..end]` unconditionally because even if searched item is not present in the slice, `start` and `end` would point at the same index, so the range would be empty. The reason I decided not to use this shorter implementation is because it would involve more comparisons in case there are no elements in the slice with key equal to `key`. Also, not that it matters much, but this implementation also improves perf according to the benchmark below: https://gist.github.com/ttsugriy/63c0ed39ae132b131931fa1f8a3dea55 The results on my M1 macbook air are: ``` Running benches/bin_search_slice_benchmark.rs (target/release/deps/bin_search_slice_benchmark-90fa6d68c3bd1298) Benchmarking multiply add/binary_search_slice: Collecting 100 samples in estimated 5.0002 s (1 multiply add/binary_search_slice time: [44.719 ns 44.918 ns 45.158 ns] No change in performance detected. Found 3 outliers among 100 measurements (3.00%) 1 (1.00%) high mild 2 (2.00%) high severe Benchmarking multiply add/binary_search_slice_new: Collecting 100 samples in estimated 5.0001 multiply add/binary_search_slice_new time: [36.955 ns 37.060 ns 37.221 ns] No change in performance detected. Found 7 outliers among 100 measurements (7.00%) 3 (3.00%) high mild 4 (4.00%) high severe ```
Kobzol
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Feb 3, 2026
[rustc_data_structures] Use partition_point to find slice range end. This PR uses approach introduced in rust-lang/rust#114152 to find the end of the range. It's much easier to understand and reason about invariants of such implementation. Technically it's possible to make it even shorter by returning `&[start..end]` unconditionally because even if searched item is not present in the slice, `start` and `end` would point at the same index, so the range would be empty. The reason I decided not to use this shorter implementation is because it would involve more comparisons in case there are no elements in the slice with key equal to `key`. Also, not that it matters much, but this implementation also improves perf according to the benchmark below: https://gist.github.com/ttsugriy/63c0ed39ae132b131931fa1f8a3dea55 The results on my M1 macbook air are: ``` Running benches/bin_search_slice_benchmark.rs (target/release/deps/bin_search_slice_benchmark-90fa6d68c3bd1298) Benchmarking multiply add/binary_search_slice: Collecting 100 samples in estimated 5.0002 s (1 multiply add/binary_search_slice time: [44.719 ns 44.918 ns 45.158 ns] No change in performance detected. Found 3 outliers among 100 measurements (3.00%) 1 (1.00%) high mild 2 (2.00%) high severe Benchmarking multiply add/binary_search_slice_new: Collecting 100 samples in estimated 5.0001 multiply add/binary_search_slice_new time: [36.955 ns 37.060 ns 37.221 ns] No change in performance detected. Found 7 outliers among 100 measurements (7.00%) 3 (3.00%) high mild 4 (4.00%) high severe ```
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Instead of using
binary_search_by_key, it's possible to usepartition_pointto find the lower bound. This avoids the need to locate the leftmost matching entry separately.It's also possible to use
partition_pointto find the upper bound, so I plan to send a separate PR for your consideration.