Parallel Work

Async tasks overlap waiting work. CPU-heavy work needs real parallelism. Use sifr.parallel when a map over owned data would otherwise block the async runtime.

When to Use Parallel Work

Kind of work	Prefer
Network, timers, waiting on I/O	Async tasks with `await` and scopes
CPU-heavy transform over a collection	`sifr.parallel.map` or a `Pool`
Blocking library call inside async code	Move it out of the async path, or offload with parallel helpers

Sifr rejects blocking or CPU-heavy calls that sit directly inside async code, because they prevent other tasks from making progress.

Parallel Map

map preserves output order. Inputs and outputs must be owned and sendable across the worker boundary.

from sifr.parallel import map as parallel_map

def heavy(value: int) -> int:
    return value * value

def run_cpu_work(values: list[int]) -> list[int]:
    return parallel_map(values, heavy)

Use try_map when each item can fail independently and you want typed per-item outcomes:

from sifr.parallel import try_map, WorkerError

outcomes: list[Result[int, WorkerError]] = try_map(
    [1, 0, 3],
    lambda x: 10 // x,
)

Worker Pools

For repeated parallel work in a long-lived process, configure a Pool explicitly:

from sifr.parallel import Pool, PoolConfig

config: PoolConfig = PoolConfig(workers=4)
pool: Pool = Pool(config)

results: list[str] = pool.map(["a", "b", "c"], str.upper)
pool.close()

Lock guards, task handles, and borrowed values cannot cross the worker boundary. Pass owned data in, and receive owned results back.

Async Concurrency vs Parallelism

Use Structured Tasks to run many async operations together.
Use sifr.parallel to speed up CPU-bound transforms.
Keep mutable borrows and lock guards out of both paths; see Ownership Across Tasks.

Next Steps

Concurrency API

Full sifr.parallel, sifr.task, and sifr.sync reference.

Async and Await

Review suspension points and why blocking work is rejected.

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Parallel Work

When to Use Parallel Work

Parallel Map

Worker Pools

Async Concurrency vs Parallelism

Next Steps

Concurrency API

Async and Await

​When to Use Parallel Work

​Parallel Map

​Worker Pools

​Async Concurrency vs Parallelism

​Next Steps

Concurrency API

Async and Await

When to Use Parallel Work

Parallel Map

Worker Pools

Async Concurrency vs Parallelism

Next Steps