ArgMinMax

Efficient argmin & argmax (in 1 function) with SIMD (SSE, AVX(2), AVX512, NEON) for f16, f32, f64, i8, i16, i32, i64, u8, u16, u32, u64 on ndarray::ArrayView1

🚀 The function is generic over the type of the array, so it can be used on an ndarray::ArrayView1<T> where T can be f16*, f32, f64, i8, i16, i32, i64, u8, u16, u32, u64.

⚡ Runtime CPU feature detection is used to select the most efficient implementation for the current CPU. This means that the same binary can be used on different CPUs without recompilation.

👀 The SIMD implementation contains no if checks, ensuring that the runtime of the function is independent of the input data its order (best-case = worst-case = average-case).

🪄 Efficient support for f16 and uints: through (bijective aka symmetric) bitwise operations, f16 (optional) and uints are converted to ordered integers, allowing to use integer SIMD instructions.

*for f16 you should enable the 'half' feature.

Installing

Add the following to your Cargo.toml:

toml [dependencies] argminmax = "0.3"

Example usage

```rust use argminmax::ArgMinMax; // extension trait for ndarray::ArrayView1 use ndarray::Array1;

let arr: Vec = (0..200_000).collect(); let arr: Array1 = Array1::from(arr);

let (min, max) = arr.view().argminmax(); // apply extension

println!("min: {}, max: {}", min, max); println!("arr[min]: {}, arr[max]: {}", arr[min], arr[max]); ```

Benchmarks

Benchmarks on my laptop (AMD Ryzen 7 4800U, 1.8 GHz, 16GB RAM) using criterion show that the function is 3-20x faster than the scalar implementation (depending of data type).

See /benches/results.

Run the benchmarks yourself with the following command: bash cargo bench --quiet --message-format=short --features half | grep "time:"

Tests

To run the tests use the following command: bash cargo test --message-format=short --features half

Limitations

❗ Does not support NaNs.

Acknowledgements

Some parts of this library are inspired by the great work of minimalrust's argmm project.