NumRs WebAssembly Bindings

High-performance numerical computing for JavaScript via WebAssembly.

🚀 Features

• Zero FFI overhead: Direct WASM calls, no Node.js native addon layer
• SIMD acceleration: Uses WebAssembly SIMD when available
• Works everywhere: Browser, Node.js, Deno, Bun
• TypeScript support: Auto-generated TypeScript definitions
• No native dependencies: Pure WASM, no compilation needed

📦 Installation

npm install @numrs/wasm

🎯 Usage

Node.js

const numrs = require('@numrs/wasm');

// Create arrays (using Float32Array for best performance)
const a = new Float32Array([1, 2, 3, 4]);
const b = new Float32Array([5, 6, 7, 8]);

// Element-wise operations
const result = numrs.add_f32(a, [4], b, [4]);
console.log(result); // [6, 8, 10, 12]

// Matrix multiplication
const m1 = new Float32Array([1, 2, 3, 4]);
const m2 = new Float32Array([5, 6, 7, 8]);
const product = numrs.matmul_f32(m1, [2, 2], m2, [2, 2]);

Browser (ES Modules)

import init, * as numrs from '@numrs/wasm/pkg-web/numrs_wasm.js';

await init(); // Initialize WASM module

const a = new Float32Array([1, 2, 3, 4]);
const result = numrs.add_f32(a, [4], a, [4]);

🔬 API

All functions work with Float32Array for optimal performance:

Binary Operations

• add_f32(a, shape_a, b, shape_b) - Element-wise addition
• sub_f32(a, shape_a, b, shape_b) - Element-wise subtraction
• mul_f32(a, shape_a, b, shape_b) - Element-wise multiplication
• div_f32(a, shape_a, b, shape_b) - Element-wise division
• matmul_f32(a, shape_a, b, shape_b) - Matrix multiplication

Unary Operations

• sin_f32(data, shape) - Sine
• cos_f32(data, shape) - Cosine
• exp_f32(data, shape) - Exponential
• sqrt_f32(data, shape) - Square root

Reductions

• sum_f32(data, shape) - Sum all elements
• mean_f32(data, shape) - Mean of all elements

Backend Info

• startup_log() - Print backend information
• backend_info() - Get backend details as JSON string

⚡ Performance

WASM bindings have zero FFI overhead compared to native Node.js addons. However, they don't have access to optimized BLAS libraries like MKL.

Best for:

• Small to medium-sized operations (<10K elements)
• Browser-based ML/data viz
• Cross-platform deployment
• Serverless/edge computing

Use native addons for:

• Large matrix operations (>100K elements)
• Heavy linear algebra with BLAS/LAPACK
• Maximum CPU performance

🏗️ Building from Source

# Install wasm-pack
cargo install wasm-pack

# Build for Node.js
npm run build

# Build for web
npm run build:web

# Build all targets
npm run build:all

📊 Benchmarks

Run benchmarks:

npm run bench

See BENCHMARK_WASM.md for detailed results.

🤝 Comparison with Native Bindings

📄 License

MIT