Rust 零成本抽象：for 循环与迭代器的性能

读Zero-cost abstractions，看Introduction to rust: a low-level language with high-level abstractions，我试图比较两种计算向量点积的方法:一种使用for循环，另一种使用迭代器.

#![feature(test)]

extern crate rand;
extern crate test;

use std::cmp::min;

fn dot_product_1(x: &[f64], y: &[f64]) -> f64 {
    let mut result: f64 = 0.0;
    for i in 0..min(x.len(), y.len()) {
        result += x[i] * y[i];
    }
    return result;
}

fn dot_product_2(x: &[f64], y: &[f64]) -> f64 {
    x.iter().zip(y).map(|(&a, &b)| a * b).sum::<f64>()
}

#[cfg(test)]
mod bench {
    use test::Bencher;
    use rand::{Rng,thread_rng};
    use super::*;

    const LEN: usize = 30;

    #[test]
    fn test_1() {
        let x = [1.0, 2.0, 3.0];
        let y = [2.0, 4.0, 6.0];
        let result = dot_product_1(&x, &y);
        assert_eq!(result, 28.0);
    }

    #[test]
    fn test_2() {
        let x = [1.0, 2.0, 3.0];
        let y = [2.0, 4.0, 6.0];
        let result = dot_product_2(&x, &y);
        assert_eq!(result, 28.0);
    }

    fn rand_array(cnt: u32) -> Vec<f64> {
        let mut rng = thread_rng();
        (0..cnt).map(|_| rng.gen::<f64>()).collect()

    }

    #[bench]
    fn bench_small_1(b: &mut Bencher) {
        let samples = rand_array(2*LEN as u32);
        b.iter(|| {
            dot_product_1(&samples[0..LEN], &samples[LEN..2*LEN])
        })
    }

    #[bench]
    fn bench_small_2(b: &mut Bencher) {
        let samples = rand_array(2*LEN as u32);
        b.iter(|| {
            dot_product_2(&samples[0..LEN], &samples[LEN..2*LEN])
        })
    }
}

上面后面的链接声称，带有迭代器的版本应该具有类似的性能，"而且实际上要快一点".然而，在对两者进行基准测试时，我得到了非常不同的结果:

running 2 tests
test bench::bench_small_loop ... bench:          20 ns/iter (+/- 1)
test bench::bench_small_iter ... bench:          24 ns/iter (+/- 2)

test result: ok. 0 passed; 0 failed; 0 ignored; 2 measured; 0 filtered out

那么，"零成本抽象"go 了哪里？

Update:添加@wimh提供的foldr个示例，并使用split_at代替切片，得到以下结果.

running 3 tests
test bench::bench_small_fold ... bench:          18 ns/iter (+/- 1)
test bench::bench_small_iter ... bench:          21 ns/iter (+/- 1)
test bench::bench_small_loop ... bench:          24 ns/iter (+/- 1)

test result: ok. 0 passed; 0 failed; 0 ignored; 3 measured; 0 filtered out

因此，额外的时间似乎直接或间接地来自于在测量代码中构造切片.为了验证情况是否属实，我try 了以下两种方法，得到了相同的结果(这里显示的是foldr个 case ，使用map+sum):

#[bench]
fn bench_small_iter(b: &mut Bencher) {
    let samples = rand_array(2 * LEN);
    let s0 = &samples[0..LEN];
    let s1 = &samples[LEN..2 * LEN];
    b.iter(|| dot_product_iter(s0, s1))
}

#[bench]
fn bench_small_fold(b: &mut Bencher) {
    let samples = rand_array(2 * LEN);
    let (s0, s1) = samples.split_at(LEN);
    b.iter(|| dot_product_fold(s0, s1))
}