Optimizing cyclic data-flow graphs via associativity

An iterative or recursive algorithm, with interiteration precedence relations is represented by a cyclic data-flow graph (DFG), where nodes represented operations. Such a DFG has a lower bound on the schedule length, which is determined by the loops (cycles) in the cyclic DFG. Associativity of the operations can be applied to restructure a DFC while preserving the behavior of the given recursive algorithm. We propose a measure of criticalness on regions of a DFG in order to guide the application of associativity to effectively reduce the lower bound or schedule length. Experimental results show that the transformed dataflow graph gives the best known schedules even under resource constraints