An algorithm to maximize the number of parallel tasks I have a set of compute tasks I want to schedule, these tasks have dependencies and a task may not be executed until all its dependencies are executed.

The problem can be represented as a directed acyclic graph:



The current scheduler ensures correctness, and capable of culling unneeded tasks such as k in the previous graph (assuming the end goal is i).

I am struggling to find another equivalent algorithm to maximize number of tasks running in parallel (tasks on the same line may be executed in any order):

a, b, j
c
d
e, f, h, g
i


Flatten form, what is between () may be in any order and represent parallel tasks:

  (a, b, j), (c), (d), (e, f, h, g), (i)


The idea here I want to fill the compute engine with as much work as possible.
From your initial DAG $G(E, V)$ You start reversing all edges to build a new DAG $G'(E, V')$. Then do a BFS from $i$ in $G'$ to remove any unneeded tasks (unreached nodes in the BFS). This step is $O(N)$ with $N = |V'|$, the number of edges.

From $G$, build the number of requirements of each task ($R(V)$) and at the same time the list $L$ of the tasks with no requirements. this takes $O(N)$.

Then you iterate on this pseudo-algorithm:

while(root is not executed)
    L' = empty list
    for v in L
        for each node v' with an edge in G' from v to v'
             R(v') -= 1
             if R(v') == O then add v' to L'
    execute all L in a parrallel step
    L = L'


this is $O(N)$.