Files
2026-08-01 16:11:49 +03:00

142 lines
3.0 KiB
Rust

#![feature(test)]
extern crate test;
use rand::prelude::*;
use test::Bencher;
/// Exposes wyhash's RNG through the rand traits
struct WyRng(u64);
impl rand::TryRng for WyRng {
type Error = std::convert::Infallible;
fn try_next_u32(&mut self) -> Result<u32, Self::Error> {
Ok(wyhash::wyrng(&mut self.0) as u32)
}
fn try_next_u64(&mut self) -> Result<u64, Self::Error> {
Ok(wyhash::wyrng(&mut self.0))
}
fn try_fill_bytes(&mut self, dst: &mut [u8]) -> Result<(), Self::Error> {
for chunk in dst.chunks_mut(8) {
let bytes = wyhash::wyrng(&mut self.0).to_le_bytes();
chunk.copy_from_slice(&bytes[..chunk.len()]);
}
Ok(())
}
}
fn wyrng() -> WyRng {
WyRng(rand::random())
}
#[bench]
fn shuffle_wyhash(b: &mut Bencher) {
let mut rng = wyrng();
let mut x = (0..100).collect::<Vec<usize>>();
b.iter(|| {
x.shuffle(&mut rng);
x[0]
})
}
#[bench]
fn shuffle_fastrand(b: &mut Bencher) {
let mut rng = fastrand::Rng::new();
let mut x = (0..100).collect::<Vec<usize>>();
b.iter(|| {
rng.shuffle(&mut x);
x[0]
})
}
#[bench]
fn u8_wyhash(b: &mut Bencher) {
let mut rng = wyrng();
b.iter(|| {
let mut sum = 0u8;
for _ in 0..10_000 {
sum = sum.wrapping_add(rng.random::<u8>());
}
sum
})
}
#[bench]
fn u8_fastrand(b: &mut Bencher) {
let mut rng = fastrand::Rng::new();
b.iter(|| {
let mut sum = 0u8;
for _ in 0..10_000 {
sum = sum.wrapping_add(rng.u8(..));
}
sum
})
}
#[bench]
fn u32_wyhash(b: &mut Bencher) {
let mut rng = wyrng();
b.iter(|| {
let mut sum = 0u32;
for _ in 0..10_000 {
sum = sum.wrapping_add(rng.random::<u32>());
}
sum
})
}
#[bench]
fn u32_fastrand(b: &mut Bencher) {
let mut rng = fastrand::Rng::new();
b.iter(|| {
let mut sum = 0u32;
for _ in 0..10_000 {
sum = sum.wrapping_add(rng.u32(..));
}
sum
})
}
#[bench]
fn f32_fastrand(b: &mut Bencher) {
let mut rng = fastrand::Rng::new();
b.iter(|| {
// f32 sum unrolled 2x to hide f32-add latency.
//
// Optimal amount of unrolling is somewhat sensitive to CPU and algorithm.
// Variously could be 2x, 3x, or 4x unrolling. On AArch64 and x86-64 on the
// current algorithm, 2x seems to be optimal on this benchmark.
let mut sum = 0.0;
for _ in 0..5_000 {
sum += rng.f32() + rng.f32();
}
sum
})
}
#[bench]
fn fill(b: &mut Bencher) {
let mut rng = fastrand::Rng::new();
b.iter(|| {
// Pick a size that isn't divisible by 8.
let mut bytes = [0u8; 367];
rng.fill(&mut bytes);
bytes
})
}
#[bench]
fn fill_naive(b: &mut Bencher) {
let mut rng = fastrand::Rng::new();
b.iter(|| {
let mut bytes = [0u8; 367];
for item in &mut bytes {
*item = rng.u8(..);
}
bytes
})
}