PArray coherence issue

[mebenstein]

When doing a reduce operation using PArrays, the data in follow-up operations is not consistent or None. The following example without a reduction works fine:

def main():
    t = TaskSpace("tasks")
    a = TaskSpace("acc")

    n  = 4
    arr = asarray(np.zeros((n,256,256)))

    for i in range(n):
        @spawn(t[i], output=[arr[i]],placement=gpu)
        def task_a():
            arr[i] = 1

    for i in range(n):
        @spawn(t[n+i],dependencies=[t[:n]], input=[arr[i]],placement=gpu)
        def task_b():
            print(arr[i].mean())
    
if __name__ == "__main__":
    with Parla():
        main()

The outputs are 1.0, 1.0, 1.0, 1.0.

Adding a reduce operation leads to wrong values, often 0.5, 0.0, 0.0, 0.0

def main():
    t = TaskSpace("tasks")
    a = TaskSpace("acc")

    n  = 4
    arr = asarray(np.zeros((n,256,256)))

    for i in range(n):
        @spawn(t[i], output=[arr[i]],placement=gpu)
        def task_a():
            arr[i] = 1

    @spawn(a[0],dependencies=[t[:n]], input=[arr],placement=gpu)
    def acc():
        print(arr.mean())

   for i in range(n):
        @spawn(t[n+i],dependencies=[a[0]], input=[arr[i]],placement=gpu)
        def task_b():
            print(arr[i].mean())
    
if __name__ == "__main__":
    with Parla():
        main()

When the acc operation binds the parameter via inout the values are sometimes None and yield runtime exceptions. This only happens on GPU, not on CPU.