use crate StackJob;
use crate SpinLatch;
use crate;
use crate unwind;
use Any;
use crate FnContext;
/// Takes two closures and *potentially* runs them in parallel. It
/// returns a pair of the results from those closures.
///
/// Conceptually, calling `join()` is similar to spawning two threads,
/// one executing each of the two closures. However, the
/// implementation is quite different and incurs very low
/// overhead. The underlying technique is called "work stealing": the
/// Rayon runtime uses a fixed pool of worker threads and attempts to
/// only execute code in parallel when there are idle CPUs to handle
/// it.
///
/// When `join` is called from outside the thread pool, the calling
/// thread will block while the closures execute in the pool. When
/// `join` is called within the pool, the calling thread still actively
/// participates in the thread pool. It will begin by executing closure
/// A (on the current thread). While it is doing that, it will advertise
/// closure B as being available for other threads to execute. Once closure A
/// has completed, the current thread will try to execute closure B;
/// if however closure B has been stolen, then it will look for other work
/// while waiting for the thief to fully execute closure B. (This is the
/// typical work-stealing strategy).
///
/// # Examples
///
/// This example uses join to perform a quick-sort (note this is not a
/// particularly optimized implementation: if you **actually** want to
/// sort for real, you should prefer [the `par_sort` method] offered
/// by Rayon).
///
/// [the `par_sort` method]: ../rayon/slice/trait.ParallelSliceMut.html#method.par_sort
///
/// ```rust
/// # use rayon_core as rayon;
/// let mut v = vec![5, 1, 8, 22, 0, 44];
/// quick_sort(&mut v);
/// assert_eq!(v, vec![0, 1, 5, 8, 22, 44]);
///
/// fn quick_sort<T:PartialOrd+Send>(v: &mut [T]) {
/// if v.len() > 1 {
/// let mid = partition(v);
/// let (lo, hi) = v.split_at_mut(mid);
/// rayon::join(|| quick_sort(lo),
/// || quick_sort(hi));
/// }
/// }
///
/// // Partition rearranges all items `<=` to the pivot
/// // item (arbitrary selected to be the last item in the slice)
/// // to the first half of the slice. It then returns the
/// // "dividing point" where the pivot is placed.
/// fn partition<T:PartialOrd+Send>(v: &mut [T]) -> usize {
/// let pivot = v.len() - 1;
/// let mut i = 0;
/// for j in 0..pivot {
/// if v[j] <= v[pivot] {
/// v.swap(i, j);
/// i += 1;
/// }
/// }
/// v.swap(i, pivot);
/// i
/// }
/// ```
///
/// # Warning about blocking I/O
///
/// The assumption is that the closures given to `join()` are
/// CPU-bound tasks that do not perform I/O or other blocking
/// operations. If you do perform I/O, and that I/O should block
/// (e.g., waiting for a network request), the overall performance may
/// be poor. Moreover, if you cause one closure to be blocked waiting
/// on another (for example, using a channel), that could lead to a
/// deadlock.
///
/// # Panics
///
/// No matter what happens, both closures will always be executed. If
/// a single closure panics, whether it be the first or second
/// closure, that panic will be propagated and hence `join()` will
/// panic with the same panic value. If both closures panic, `join()`
/// will panic with the panic value from the first closure.
/// Identical to `join`, except that the closures have a parameter
/// that provides context for the way the closure has been called,
/// especially indicating whether they're executing on a different
/// thread than where `join_context` was called. This will occur if
/// the second job is stolen by a different thread, or if
/// `join_context` was called from outside the thread pool to begin
/// with.
/// If job A panics, we still cannot return until we are sure that job
/// B is complete. This is because it may contain references into the
/// enclosing stack frame(s).
// cold path
unsafe !