Towards memory-optimal schedules for SDF

Abstract

The Synchronous Data Flow (SDF) programming model is an established programming paradigm for stream processing applications. SDF programs are expressed by actors and streams that establish communication among actors. Streams are implemented as FIFO buffers, and the size of the FIFO buffers depends on the steady-state schedule. Finding a steady-state schedule that minimizes the sizes of FIFO buffers, is of great importance to minimize the memory consumption. The state-of-the-art provides ad-hoc heuristics only, so finding memory-optimal steady-state schedules is still an open challenge. In this work, we study three objective functions capturing the memory utilization of three different implementations of the FIFO buffers. We show that one objective is NP-hard to optimize, while the other two can be solved optimally in polynomial time. The algorithm for computing these optimal schedules is implementable as an online algorithm. We show the effectiveness of our new algorithm comparing it with the state-of-the-art heuristics. Our experiments show that for large synthetic instances, our algorithm generates schedules that use up to 8 times less memory.