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+### Concurrency in Kotlin/Native
+
+  Kotlin/Native runtime doesn't encourage a classical thread-oriented concurrency
+ model with mutually exclusive code blocks and conditional variables, as this model is
+ known to be error-prone and unreliable. Instead, we suggest collection of
+ alternative approaches, allowing to use hardware concurrency and implement blocking IO.
+ Those approaches are as following, and will be elaborated in further sections:
+   * Workers with message passing
+   * Object subgraph ownership transfer
+   * Object subgraph freezing
+   * Object subgraph detachment
+   * Raw shared memory using C globals
+   * Coroutines for blocking operations (not covered in this document)
+
+ ## Workers
+
+  Instead of threads Kotlin/Native runtime offers concept of workers: concurrently executing
+ control flow streams with an associated request queue. Workers are very similar to actors
+ in the Actor Model. Worker can exchange Kotlin objects with other workers, so that at the moment
+ each mutable object is owned by the single worker, but ownership could be transferred.
+ See section [Object transfer and freezing](#transfer).
+
+  Once worker is started with `startWorker` function call, it can be uniquely addressed with an integer
+ worker id. Other workers, or non-worker concurrency primitives, such as OS threads, could send a message
+ to the worker with `schedule` call.
+ ```kotlin
+   val future = schedule(TransferMode.CHECKED, { SomeDataForWorker() }) {
+       // data returned by the second function argument comes to the
+       // worker routine as 'input' parameter.
+       input ->
+       // Here we create an instance to be returned when someone consumes result future.
+       WorkerResult(input.stringParam + " result")
+   }
+
+   future.consume {
+      // Here we see result returned from routine above. Note that future object or
+      // id could be transferred to another worker, so we don't have to consume future
+      // in same execution context it was obtained.
+      result ->
+      println("result is $result")
+   }
+```
+ The call to `schedule` uses function passed as its second parameter to produce an object subgraph
+ (i.e. set of mutually referring objects) which is passed as the whole to that worker, and no longer
+ available to the thread that initiated the request. This property is checked if the first parameter
+ is `TransferMode.CHECKED` by graph traversal and just assumed to be true, if it is `TransferMode.UNCHECKED`.
+ Last parameter to schedule is a special Kotlin lambda, which is not allowed to capture any state,
+ and is actually invoked in target worker's context. Once processed, result is transferred to whoever consumes
+ the future, and is attached to object graph of that worker/thread.
+
+  If an object is transferred in `UNCHECKED` mode and is still accessible from multiple concurrent executors,
+ program will likely crash unexpectedly, so consider that last resort in optimizing, not a general purpose
+ mechanism.
+
+  For more complete example please refer to the [workers example](https://github.com/JetBrains/kotlin-native/tree/master/samples/workers)
+ in the Kotlin/Native repository.
+
+
+ ## <a name="transfer"></a>Object transfer and freezing
+
+   Important invariant that Kotlin/Native runtime maintains is that object is either owned by a single
+  thread/worker, or is immutable (_shared XOR mutable_). This ensures that the same data has a single mutator, and so no need for
+  locking exists. To achieve such an invariant, we use concept of not externally referred object subgraphs.
+  This is a subgraph which has no external references from outside of the subgraph, what could be checked
+  algorithmically with O(N) complexity (in ARC systems), where N is number of elements in such a subgraph.
+  Such subgraphs are usually produced as a result of lambda expression, for example some builder, and may not
+  contain objects, referred externally.
+
+   Freezing is a runtime operation making given object subgraph immutable, by modifying the object header
+  so that future mutation attempts lead to throwing an `InvalidMutabilityException`. It is deep, so
+  if an object has a pointer to another objects - transitive closure of such objects will be frozen.
+  Freezing is the one way transformation, frozen objects cannot be unfrozen. Frozen objects has a nice
+  property that due to their immutability, they could freely shared between multiple workers/threads
+  not breaking the "mutable XOR shared" invariant.
+
+   If object is frozen could be checked with an extension property `isFrozen`, and if it is, object sharing
+ is allowed. Currently, Kotlin/Native runtime only freezes enum objects after creation, although additional
+ autofreezing of certain provably immutable objects could be implemented in the future.
+
+  ## <a name="detach"></a>Object subgraph detachment
+
+   Object subgraph without external references could be disconnected using `detachObjectGraph` to
+  a `COpaquePointer` value, which could be stored in `void*` data, so disconnected object subgraphs
+  could be stored in C data structure, and later attached back with `attachObjectGraph<T>` in arbitrary thread
+  or worker. Combined with [raw memory sharing](#shared) it allows side channel object transfer between
+  concurrent threads, if worker mechanisms are insufficient for the particular task.
+
+ ## <a name="shared"></a>Raw shared memory
+
+  Considering strong ties of Kotlin/Native with C via interoperability, in conjunction with other mechanisms
+ mentioned above one could build popular data structures, like concurrent hashmap or shared cache with
+ Kotlin/Native. One could rely upon shared C data, and store in it references to detached object subgraphs.
+ Consider the following .def file:
+```
+package = global
+
+---
+typedef struct {
+  int version;
+  void* kotlinObject;
+} SharedData;
+
+SharedData sharedData;
+```
+After running cinterop tool it allows sharing Kotlin data in versionized global structure,
+and interact with it from Kotlin transparently via autogenerated Kotlin like this:
+```kotlin
+class SharedData(rawPtr: NativePtr) : CStructVar(rawPtr) {
+    var version: Int
+    var kotlinObject: COpaquePointer?
+}
+```
+So combined with the top level variable declared above, it allows seeing the same memory from different
+threads and building traditional concurrent structures with platform-specific synchronization primitives.