Exploring WebAssembly Performance

// Research_Overview

WebAssembly (WASM) promises near-native performance in the browser. But how does it actually perform in real-world scenarios? I ran some benchmarks to find out.

// Methodology

I tested three compute-intensive tasks:

1. Image processing (blur filter)
2. Mathematical calculations (prime numbers)
3. Game physics simulation

Each task was implemented in:

• Pure JavaScript
• WebAssembly (compiled from Rust)
• SIMD-enabled WebAssembly

// Results_Data

const benchmarkResults = {
  imageProcessing: {
    js: "245ms",
    wasm: "89ms", 
    wasmSIMD: "34ms"
  },
  primeCalculation: {
    js: "1840ms",
    wasm: "412ms",
    wasmSIMD: "198ms"
  },
  physicsSimulation: {
    js: "167ms",
    wasm: "56ms",
    wasmSIMD: "31ms"
  }
};

// Analysis

Key findings:

1. WASM is 2-4x faster than JS for compute-heavy tasks
2. SIMD adds another 2x boost for parallel operations
3. Diminishing returns for I/O-bound tasks
4. Startup overhead - WASM module loading takes ~50-100ms

[When to Use WASM]

✅ Good fit:

• Image/video processing
• Cryptography
• Game engines
• Scientific computing

❌ Overkill:

• DOM manipulation
• Network requests
• Simple business logic

// Real_World_Application

I’m using WASM for the game easter eggs on this site. The Snake and Balloon Pop games run smoother at 60fps with WASM-powered physics.

// Rust code compiled to WASM
#[wasm_bindgen]
pub fn update_physics(delta_time: f32) -> Vec<Entity> {
    // Physics calculations at 60fps
    entities.iter_mut()
        .map(|e| e.update(delta_time))
        .collect()
}

// Conclusion

WebAssembly is a powerful tool, but not a silver bullet. Use it strategically where performance truly matters.

Future research:

• WASM garbage collection proposal
• Multi-threading with SharedArrayBuffer
• Component model for better interop

$ cargo build --target wasm32-unknown-unknown
$ wasm-bindgen target/wasm32-unknown-unknown/release/game.wasm
# >> Performance optimized ✓

Full benchmark code available on GitHub.