WebAssembly SIMD & Vector Graphics: Accelerating 2D Path Rasterization in the Browser

Complex 2D vector applications—such as browser-based CAD tools, typography layout engines, and interactive design suites—frequently encounter CPU rendering bottlenecks when calculating thousands of cubic Bézier curve segments in JavaScript. While the HTML5 Canvas 2D API executes serial CPU path flattening, WebAssembly with 128-bit Single Instruction Multiple Data (Wasm SIMD) unlocks hardware-level parallel math in the browser. By processing four 32-bit floating-point coordinate pairs simultaneously (v128 vectors), Wasm SIMD accelerates curve subdivision, anti-aliased edge rasterization, and mesh tessellation by over 400%.

The Architecture of Wasm SIMD Vector Parallelism

Wasm SIMD executes vectorized mathematics directly on host CPU registers:

⚡ The 128-Bit SIMD Lane Invariant

Wasm SIMD exposes four 32-bit floating point lanes within a single v128 vector. Evaluating cubic Bézier polynomial coefficients across four distinct parametric t-values ($t_0, t_1, t_2, t_3$) occurs in a single clock cycle, eliminating scalar loop overhead.

Vector Graphics Rasterization Engines Comparison Matrix

Rasterization Engine	Vector Parallelism	Bézier Subdivision Throughput	Memory Zero-Copy
Native HTML5 Canvas2D Context	Scalar (Single-threaded JS)	~150k curves / sec	ImageData marshaling copy
Standard WebAssembly (Scalar)	Scalar (Single instruction)	~450k curves / sec	Direct Wasm Memory Heap
WebAssembly 128-bit SIMD	4x 32-bit Float Vector Lanes	~1.9M curves / sec (4.2x)	Zero-Copy WebGL Staging Buffer

Rust / Wasm SIMD Vector Bézier Evaluator

Compute 4 cubic curve points concurrently using SIMD intrinsics:

// Rust Wasm SIMD Cubic Bézier Point Evaluator
use core::arch::wasm32::*;

#[inline(always)]
pub unsafe fn evaluate_bezier_simd_4x(p0: v128, p1: v128, p2: v128, p3: v128, t: v128) -> v128 {
    let ones = f32x4_splat(1.0);
    let one_minus_t = f32x4_sub(ones, t);
    
    let one_minus_t_sq = f32x4_mul(one_minus_t, one_minus_t);
    let one_minus_t_cb = f32x4_mul(one_minus_t_sq, one_minus_t);
    
    let t_sq = f32x4_mul(t, t);
    let t_cb = f32x4_mul(t_sq, t);
    
    let three = f32x4_splat(3.0);
    let term0 = f32x4_mul(one_minus_t_cb, p0);
    let term1 = f32x4_mul(f32x4_mul(three, f32x4_mul(one_minus_t_sq, t)), p1);
    let term2 = f32x4_mul(f32x4_mul(three, f32x4_mul(one_minus_t, t_sq)), p2);
    let term3 = f32x4_mul(t_cb, p3);
    
    f32x4_add(f32x4_add(term0, term1), f32x4_add(term2, term3))
}

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