I've been researching ways of modeling and executing tasks which are dependent on each other (but in an acyclic way) and came up with task graphs. But the question that's bugging me is how can find out the maximum degree of concurrency in a given task graph.

In my case, I'm talking of a relatively small graph, around 100 nodes, but nodes, representing tasks are very long running tasks. So the occuracy, more then complexity of such an algorithm would matter.

Assuming I came up of such a degree, the second problem, is how should I distrubute tasks? I've read about topological sort, and transforming the result in a list of sets, with each set being run in parallel. But again, I suspect if this is the best approach.

I've been researching ways of modeling and executing tasks which are dependent on each other (but in an acyclic way) and came up with task graphs. But the question that's bugging me is how can I find out the maximum degree of concurrency in a given task graph.

In my case, I'm talking of a relatively small graph, around 100 nodes, but nodes, representing tasks, are long running tasks. So the occuracy, more then complexity of such an algorithm would matter.

Assuming I came up of such a degree, the second problem, is how should I distrubute tasks? I've read about topological sort, and transforming the result in a list of sets, with each set being run in parallel. But again, I suspect if this is the best approach.