Partial task assignment of task graphs under heterogeneous resource constraints

This paper presents a novel partial assignment technique (PAT) that decides which tasks should be assigned to the same resource without explicitly defining assignment of these tasks to a particular resource. Our method simplifies the assignment and scheduling steps while imposing a small or no penalty on the final solution quality. This technique is specially suited for problems which have different resources constraints. Our method does not cluster tasks into a new task, as typical clustering techniques do, but specifies which tasks need to be executed on the same processor. Our experiments have shown that PAT, which may produce nonlinear groups of tasks, gives better results than linear clustering when multi-resource constraints are present. Linear clustering was proved to be optimal comparing to all other clusterings for problems with timing constraints only. In this paper, we show that, if used for multi-resource synthesis problem, as it is often used nowadays, linear clustering will produce inferior solutions.