Abstract: Scheduling techniques transform dataflow graphs (DFGs), for example, of digital signal processing (DSP) arrangements of filters, into efficient schedules for concurrent execution on processing resources coupled to a memory. A DSP arrangement may be represented by an executable model having interconnected filters represented by model elements. The techniques may apply scheduling transforms according to a classification of the model elements based on a lifetime of their internal states (e.g., finite or infinite). Exemplary scheduling transforms may include unfolding, coordinated loop scheduling and pipelining to parallelize a DFG and enhance overall performance, i.e., reduce average sample execution time of the DSP arrangement. Notably, the scheduling transforms may aggregate (i.e., merge) multiple finite state model elements for concurrent execution and repeat execution of infinite state model elements to achieve the overall improved performance.