MASES: Mobility And Slack Enhanced Scheduling For Latency-Optimized Pipelined Dataflow Graphs

Dataflow and task graph descriptions are widely used for mapping and scheduling of real-time streaming applications onto heterogeneous processing platforms. Such applications are often characterized by the need to process large-volume data streams with not only high throughput, but also low latency. Mapping such application descriptions into tightly constrained implementations requires optimization of pipelined scheduling of tasks on different processing elements. This poses the problem of finding an optimal solution across a latency-throughput objective space. In this paper, we present a novel list-scheduling based heuristic called MASES for pipelined dataflow scheduling to minimize latency under throughput and heterogeneous resource constraints. MASES explores the flexibility provided by mobility and slack of actors in a partial schedule. It can find a valid schedule if one exists even under tight throughput and resource constraints. Furthermore, MASES can improve runtime by up to 4x while achieving similar results as other latency-oriented heuristics for problems they can solve.