unrelated: so, I was talking with somebody (apologies, history goes fast in the slack channel and I'm bad with names, suffice to say it was somebody smarter than me) in this channel a while ago about a

SingleThreadedDispatcher

that would allow you to take a thread pool and put a facade on it such that any job submitted would be serialized (read: only run after the previous job had been completed). such a dispatcher would get you a similar effect as a

kotlinx.coroutines...sync.Mutex

as it relates to the behaviour of

sequentialDispatcherFacade.submit

and

mutex.withLock

. This issue of using

mutex

without the other more traditional garbage stuff in

java.concurrent

sounds like it can induce some insidious problems with threads caching values, unless somebody here tells me I'm mistaken which I'm desperately hoping they do. consider a simple little class:

class SomeStatefulClass {
  val mutex: Mutex = Mutex()

  var state: Int = 0

  fun doStatefulTransform(){
    mutex.withLock {
      val newState = state + 1
      state = newState //this is actuall just state++, but it illustrates how we might get a stale-read. 
    }
  }
}

@Test fun doStuff(){
  val statefulThing = SomeStatefulClass()
  val job1 = launch(JavaFX) { for(i in 0 until 10_000) statefulThing.doStatefulTransform() }
  val job2 = launch(CommonPool) { for(i in 0 until 10_000) statefulThing.doStatefulTransform() } 
  //a better test might be val jobs = 0 .. 10k .map { launch { ...

  job1.await(); job2.await()

  assertThat(statefulThing.state).isEqualTo( ??????? )
}

Such a test would likely pass on x86 with a good deal of regularity because my understanding is that x86 doesn't make CPU-local caches as aggressively as some platforms. But arm on the other hand might release the mutex acquired by

doStatefulTransform()

before flushing its local change in its L2 cache (or similar) through to main memory, thus letting the other thread acquire the mutex but read a stale

state

value. Is there something in "happens-before" semantics that I'm missing that would protect us from this? Or do you need to mark

state

with

@Volatile

(or use an

AtomicInt

or

Unsafe

etc etc)? Similarly, could we leverage jcstress or similar with a CI service on kotlinx.coroutines to discover this? Is it worth investing the time? If I could steal some time on jetbrain's quasi-public teamcity infrastructure, I'd be happy to try and set this up, if the kotlinx.coroutines commander-in-chief thinks its a good idea.

Hi,

Is there something in “happens-before” semantics that I’m missing that would protect us from this?

Yes, releasing the

Mutex

happens-before every subsequent acquisition of that mutex, you don’t have to do any kind of synchronization on data protected by a mutex. I will improve our documentation.

Similarly, could we leverage jcstress or similar with a CI service on kotlinx.coroutines

Currently

jcstress

is not properly supported by Gradle+Koltin due to plugins inference. Usually we write hand-rolled stress tests (which are, of course, less strict than

jcstress

, but much easier to write and to integrate into CI pipeline). For “hardcore” concurrency tests (e.g. to check linearizability) we’re using

lin-check

(https://github.com/Devexperts/lin-check) which is based on jcstress, but is simpler to use: you don’t have to mark events as “interesting”,

lin-check

knows how to produce sequential execution history and compares it with concurrent one.