Performance

This is a list of tips for writing performant noiz code. Always benchmark your noise functions. Small changes can have surprising butterfly affects!

Configuration

Make sure Cargo.toml is configured for speed. See this guide for how to do this effectively.

Noiz is so fast because of strongly worded letters to the compiler (#[inline]) and rust’s incredible zero-cost abstractions. Seriously, noiz is driven by traits and types and it’s just as fast as traditional megalithic functions! Anyway, expect much worse performance when cargo has not been configured to make use of the inlining, etc.

Only Pay for What You Use

Sometimes there’s easy ways to make noise algorithms faster:

• Ensure no derivatives are being calculated but those that are needed,
• Consider turning on Voronoi’s approximation flag,
• Switch Vec3A for Vec3 or visa versa,
• Change “libm” backend for “std” or visa versa,
• Try PerCellPointDistances<Voronoi, EuclideanLength, WorleyDifference> instead of DistanceToEdge<Voronoi>, which give similar results,
• Try applying UNormToSNorm and similar modifiers after LayeredNoise instead of inside it,
• Consider trading RandomElements for MixCellValuesForDomain,
• Consider using SelfMasked instead of Masked,
• Consider using Linear instead of Smoothstep,
• Consider using WorleyNearestSmoothMin instead of WorleySmoothMin.
• Use QuickGradients instead of higher quality but slower generators,
• Use RawNoise instead of Noise to skip scaling,
• Use SampleableFor and sample_for instead of their dynamic counterparts when in tight loops for better inlining,

In general, value noise is faster than perlin noise is faster than simplex noise. Sometimes it makes sense to calculate high octaves (that contribute a lot) with simplex or perlin noise, and then calculate details with value or perlin noise. For example:

use noiz::prelude::*;
use bevy_math::prelude::*;
let noise = Noise::<LayeredNoise<
    Normed<f32>,
    Persistence,
    (
        FractalLayers<Octave<MixCellGradients<
            OrthoGrid,
            Smoothstep,
            QuickGradients,
        >>>,
        FractalLayers<Octave<MixCellValues<
            OrthoGrid,
            Smoothstep,
            Random<SNorm, f32>,
        >>>,
    ),
>>::default();
let value: f32 = noise.sample(Vec2::new(1.5, 2.0));

This will generate the large features from perlin noise and use value noise to add some detail on top.

Neat Tricks

If you are filling a volume with data sourced from noise, consider sampling the noise sparsely and interpolating the results. For example, to fill an image, only sample the noise for every other pixel and fill in pixels that weren’t filled by noise based on the surrounding filled pixels. This is especially useful for voxel volume generation.