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Add benchmarks for k_smallest implementation variants.
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use criterion::{black_box, criterion_group, criterion_main, Bencher, BenchmarkId, Criterion}; | ||
use itertools::Itertools; | ||
use rand::{rngs::StdRng, seq::SliceRandom, SeedableRng}; | ||
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fn strict(b: &mut Bencher, (k, vals): &(usize, &Vec<usize>)) { | ||
b.iter(|| black_box(vals.iter()).k_smallest(*k)) | ||
} | ||
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fn relaxed(b: &mut Bencher, (k, vals): &(usize, &Vec<usize>)) { | ||
b.iter(|| black_box(vals.iter()).k_smallest_relaxed(*k)) | ||
} | ||
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fn ascending(n: usize) -> Vec<usize> { | ||
(0..n).collect() | ||
} | ||
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fn random(n: usize) -> Vec<usize> { | ||
let mut vals = (0..n).collect_vec(); | ||
vals.shuffle(&mut StdRng::seed_from_u64(42)); | ||
vals | ||
} | ||
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fn descending(n: usize) -> Vec<usize> { | ||
(0..n).rev().collect() | ||
} | ||
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fn k_smallest(c: &mut Criterion, order: &str, vals: fn(usize) -> Vec<usize>) { | ||
let mut g = c.benchmark_group(format!("k-smallest/{order}")); | ||
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for log_n in 20..23 { | ||
let n = 1 << log_n; | ||
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let vals = vals(n); | ||
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for log_k in 7..10 { | ||
let k = 1 << log_k; | ||
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let params = format!("{log_n}/{log_k}"); | ||
let input = (k, &vals); | ||
g.bench_with_input(BenchmarkId::new("strict", ¶ms), &input, strict); | ||
g.bench_with_input(BenchmarkId::new("relaxed", ¶ms), &input, relaxed); | ||
} | ||
} | ||
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g.finish() | ||
} | ||
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fn k_smallest_asc(c: &mut Criterion) { | ||
k_smallest(c, "asc", ascending); | ||
} | ||
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fn k_smallest_rand(c: &mut Criterion) { | ||
k_smallest(c, "rand", random); | ||
} | ||
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fn k_smallest_desc(c: &mut Criterion) { | ||
k_smallest(c, "desc", descending); | ||
} | ||
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criterion_group!(benches, k_smallest_asc, k_smallest_rand, k_smallest_desc); | ||
criterion_main!(benches); |
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@@ -114,6 +114,7 @@ where | |
return; | ||
} | ||
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assert_ne!(buf.len(), buf.capacity()); | ||
buf.push(val); | ||
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if buf.len() == 2 * k { | ||
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