Magetypes

Process an image plane (exposure) or an audio buffer (gain), including a short scalar tail. The vector type is generic over the token selected by #[magetypes]; incant! chooses the CPU tier once outside the loop. No manual per-tier wrappers or raw pointers are needed.

Adapted from the zenfilters plane-scaling kernel; the complete production call chain and adaptation notes include pinned source links.

#![forbid(unsafe_code)]
use archmage::prelude::*;

#[magetypes(define(f32x8), v3, neon, wasm128, scalar)]
fn gain_impl(token: Token, plane: &mut [f32], gain: f32) {
    let factor = f32x8::splat_t(token, gain);
    let (chunks, tail) = f32x8::partition_slice_mut_t(token, plane);
    for chunk in chunks {
        (f32x8::load_t(token, chunk) * factor).store(chunk);
    }
    for value in tail {
        *value *= gain;
    }
}

pub fn apply_gain(plane: &mut [f32], gain: f32) {
    incant!(gain_impl(plane, gain), [v3, neon, wasm128, scalar])
}

let mut data = [2.0; 11];
apply_gain(&mut data, 0.5);
assert_eq!(data, [1.0; 11]);

Continue with the production patterns

1. Type and const generics: pixel types, mode specialization, and turbofish dispatch.
2. Reusable backend helpers: the complete zenblend SrcOver chain.
3. Memory, bounds, and tails: array references and row/strip contracts.
4. Direct intrinsic helpers: a feature-enabled entry and matched helper.
5. ISA quirks and fixups: the numerical contract and its costs.
6. Production coverage: which patterns the zen crates actually exercise.

Archmage provides tokens, feature contexts, and dispatch. Magetypes provides vectors such as f32x4<T> and f32x8<T>. Generics and define(...) compose with #[magetypes]; neither is a substitute for a feature-enabled entry. Inspect optimized code and benchmark the complete loop before claiming zero overhead.

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