Abstract— This paper explores the problem of efficiently ordering interprocessor communication operations in both statically and dynamically-scheduled multiprocessors for iterative dataflow graphs with probabilistic execution times. In most digital signal processing applications, the throughput of the system is significantly affected by communication costs. We explicitly model these costs within an effective graph-theoretic analysis framework. We show that ordered transaction schedules can significantly outperform both self-timed schedules and dynamic schedules for moderate task execution time variability. As the task execution time variability increases, we show that first selftimed and then dynamic scheduling policies are preferred. We perform an extensive experimental comparison on both real and simulated benchmarks to gauge the effect of synchronization and communication overhead costs on these crossover points.
Neal K. Bambha, Shuvra S. Bhattacharyya