@async in Julia is not parallelism - but @Thread.spawn is...

 The Core Idea

In Julia, @async gives you concurrency, not parallelism.

• @async → runs multiple tasks, but on the same thread
• Threads.@spawn → runs tasks on multiple CPU threads (true parallelism)

Demonstration 1

function task(name)

println("$name running on thread ", Threads.threadid())

sleep(1)

println("$name finished on thread ", Threads.threadid())

end

@sync begin

@async task("Task A")

@async task("Task B")

end

If we run the above code, we will get 

Task A running on thread 1

Task B running on thread 1

Task A finished on thread 1

Task B finished on thread 1

What is happening internally?

Julia uses cooperative scheduling for @async:

• Tasks yield control (e.g., sleep, I/O)
• Scheduler switches between them
• But execution remains on one OS thread

As you can see from the output, both tasks are running on the same thread.

Demonstration 2

function task(name)

println("$name running on thread ", Threads.threadid())

sleep(1)

println("$name finished on thread ", Threads.threadid())

end

@sync begin

Threads.@spawn task("Task A")

Threads.@spawn task("Task B")

end

If we run the above code, we will get

Task B running on thread 3

Task A running on thread 4

Task B finished on thread 3

Task A finished on thread 4

As you can see from the output, two threads are running in parallel.

Summary

Feature	@async	Threads.@spawn
Threads used	Single thread	Multiple threads
Parallelism	No	Yes
Concurrency	Yes	Yes
Best for	I/O, networking	CPU-heavy tasks
Scheduling	Cooperative	Preemptive (OS threads)