I think scientific computing in Rust is getting too much attention without contribution lately. We don't have many essential language features stabilized. SIMD,

I think scientific computing in Rust is getting too much attention without contribution lately. We don't have many essential language features stabilized. SIMD, generic const exprs, intrinsics, function optimization overrides, and reasonable floating point overrides related to fast math are a long way from being stabilized. In order to get better perf, the code is full of these informal compiler hints to guide it towards an optimization like autovectorization or branch elision. The semantics around strict floating point standards are stifling and intrinsics have become less accessible than they used to be. Separately, is Julia hitting a different LA backend? Rust's ndarray with a blas-src on Accelerate is pretty fast, but the Rust implementation is little slower on my macbook. This is a benchmark of a dot product. ``` const M10: usize = 10_000_000; #[divan::bench] fn ndarray_dot32(b: Bencher) { b.with_inputs(|| (Array::from_vec(vec![0f32; M10]), Array::from_vec(vec![0f32; M10]))) .bench_values(|(a, b)| { a.dot(&b) }); } #[divan::bench] fn chunks_dot32(b: Bencher) { b.with_inputs(|| (vec![0f32; M10], vec![0f32; M10])) .bench_values(|(a, b)| { a.chunks_exact(32) .zip(b.chunks_exact(32)) .map(|(a, b)| a.iter().zip(b.iter()).map(|(a, b)| a * b).sum::<f32>()) .sum::<f32>() }); } #[divan::bench] fn iter_dot32(b: Bencher) { b.with_inputs(|| (vec![0f32; M100], vec![0f32; M100])) .bench_values(|(a, b)| { a.iter().zip(b.iter()).map(|(a, b)| a * b).sum::<f32>() }); } ---- Rust ---- Timer precision: 41 ns (100 samples) flops fast │ slow │ median │ mean ├─ chunks_dot32 3.903 ms│ 9.96 ms │ 4.366 ms│ 4.411 ms ├─ chunks_dot64 4.697 ms│ 16.29 ms│ 5.472 ms│ 5.516 ms ├─ iter_dot32 10.37 ms│ 11.36 ms│ 10.93 ms│ 10.86 ms ├─ iter_dot64 11.68 ms│ 13.07 ms│ 12.43 ms│ 12.4 ms ├─ ndarray_dot32 1.984 ms│ 2.91 ms │ 2.44 ms │ 2.381 ms ╰─ ndarray_dot64 4.021 ms│ 5.718 ms│ 5.141 ms│ 4.965 ms ---- Julia ---- native_dot32: Median: 1.623 ms, Mean: 1.633 ms ± 341.705 μs Range: 1.275 ms - 12.242 ms native_dot64: Median: 5.286 ms, Mean: 5.179 ms ± 230.997 μs Range: 4.736 ms - 5.617 ms simd_dot32: Median: 1.818 ms, Mean: 1.830 ms ± 142.826 μs Range: 1.558 ms - 2.169 ms simd_dot64: Median: 3.564 ms, Mean: 3.567 ms ± 586.002 μs Range: 3.123 ms - 22.887 ms iter_dot32: Median: 9.566 ms, Mean: 9.549 ms ± 144.503 μs Range: 9.302 ms - 10.941 ms iter_dot64: Median: 9.666 ms, Mean: 9.640 ms ± 84.481 μs Range: 9.310 ms - 9.867 ms All: 0 bytes, 0 allocs ``` https://github.com/trueb2/flops-bench https://github.com/trueb2/flops-bench