최대 처리량을 위한 SIMD 및 멀티스레딩

Once your WASM is optimized and benchmarked, two advanced features unlock another level of throughput:

• SIMD, do many operations at once
• Multithreading, run work in parallel

Both can dramatically speed up data-heavy workloads.

SIMD stands for Single Instruction, Multiple Data. One instruction processes a whole vector of values, for example adding four floats in a single step.

Great for image processing, audio, math, and ML.

WASM SIMD adds a 128-bit vector type, v128, holding e.g. four 32-bit ints or floats packed together.

Compile with the SIMD target feature so the compiler can emit vector instructions.

Often you do not write SIMD by hand, the compiler auto-vectorizes tight loops over slices when SIMD is enabled. Write clean loops and let the optimizer do the work.

Not every browser supports SIMD. Detect at runtime and fall back to a scalar build if needed.

WASM threads build on Web Workers plus shared memory. Each worker runs an instance that shares the same linear memory.

Threads coordinate through a SharedArrayBuffer-backed memory, created with shared: true.

Shared memory needs cross-origin isolation. Serve these headers or SharedArrayBuffer is disabled:

• Cross-Origin-Opener-Policy: same-origin
• Cross-Origin-Embedder-Policy: require-corp

Use Atomics to synchronize threads safely on shared memory, avoiding data races.

For maximum throughput:

• Enable SIMD and write vectorizable loops
• Detect features and provide scalar fallbacks
• Use shared memory + workers for parallelism
• Set COOP/COEP headers and use Atomics for safety

You reached for the highest-performance WASM features:

• SIMD processes vectors in one instruction; let the compiler auto-vectorize
• Detect features and fall back gracefully
• Multithreading uses workers + shared memory
• Enable COOP/COEP and coordinate with Atomics

Together, SIMD and threads push WASM to peak throughput for demanding workloads.