Tensor Acceleration
Matrix operations in WebAssembly
Rust Implementation
#[wasm_bindgen]
pub fn matrix_mult(a: &[f32], b: &[f32]) -> Vec {
let mut result = vec![0.0; 9];
for i in 0..3 {
for j in 0..3 {
for k in 0..3 {
result[i*3 + j] += a[i*3 + k] * b[k*3 + j];
}
}
}
result
}
This Rust function compiles to WebAssembly using wasm32-unknown-unknown
target.
Performance
WebAssembly outperforms JavaScript in tensor operations by:
- • 800ms JavaScript vs 120ms WebAssembly
- • 80% memory overhead reduction
- • Native SIMD instruction access
Use wasm-bindgen
+ rustwasm
toolchain for optimal performance
Model Compiler
ONNX to WebAssembly conversion
Step 1: Convert
$ onnx2wasm resnet50.onnx \
--target wasm32 \
--optimize \
--output resnet50.wasm
Step 2: Load
const rust = wasm_bindgen(resnet50.wasm);
rust.then(obj => {
obj.inference(input);
});
Output
- • WebAssembly binary
- • Text format (.wit)
- • Memory layout spec
SIMD Acceleration
Vectorized operations in WebAssembly
WebAssembly SIMD Instructions
WebAssembly includes v128 vector registers capable of:
- • 4x32bit floats
- • 8x16bit integers
- • 16x8bit instructions
v128.load
v128.store
v128.add
v128.mul
Performance Boost
SIMD acceleration provides 3-8× performance boost in matrix multiplication, image processing, and neural network operations.
Use wasmtime
or wasm3
engines for best SIMD results
Get Started
Install Rust with WebAssembly target:
$ rustup target add wasm32-unknown-unknown $ cargo install wasm-bindgen-cli
Create a new WASM project:
$ cargo new --lib wargml $ cd wargml $ cargo add wasm-bindgen $ cargo add ndarray
Compile and test:
$ cargo build --target wasm32-unknown-unknown $ wasm-bindgen target/wasm32-unknown-unknown/debug/libwargml.wasm --out-dir ./out