تمرير الرسائل والقنوات بين خيوط WASM

Concurrent systems coordinate either by sharing memory (SharedArrayBuffer + atomics) or by passing messages. Message passing avoids many data races by design.

Web Workers communicate with postMessage. Data is structured-cloned, so each side gets its own copy.

To avoid copying large buffers, transfer ownership instead of cloning.

A channel is a typed queue abstraction layered over postMessage: a send side and a receive side with backpressure and request IDs.

Attach a unique id to each message so async replies can be matched to their originating request via a pending-promise map.

For data parallelism, spawn N workers and round-robin tasks to them. Collect results as they arrive, preserving order with the request IDs.

Message passing for control, SharedArrayBuffer for bulk data is a powerful hybrid: send a small message saying which slice to process, while the data lives in shared memory.

Pure message-passing systems still deadlock if two parties each wait for the other. Keep protocols acyclic and prefer non-blocking sends.

Structured clone is convenient but costly for big graphs. Prefer transferables or shared memory when payloads grow large.

Create workers up front in a pool and reuse them; spinning up a worker per task wastes time loading and instantiating the WASM module each time.

Chain stages: source worker emits chunks, a transform worker processes them, a sink worker aggregates — each connected by a channel for a clean streaming pipeline.

You explored message passing with postMessage, transferables to avoid copies, and channel abstractions with request IDs and worker pools. Hybrid designs pair messages for control with shared memory for bulk data.