In this paper we extensively explore and illustrate the effectiveness of the two-phase decomposition of scheduling - into clustering and cluster-scheduling or merging - and mapping task graphs onto embedded multiprocessor systems. We describe efficient and novel partitioning (clustering) and scheduling techniques that aggressively streamline interprocessor communication and can be tuned to exploit the significantly longer compilation time that is available to embedded system designers. The increased compile-time tolerance results because embedded multiprocessor systems are typically designed as final implementations for dedicated functions. While multiprocessor mapping strategies for general-purpose systems are usually designed with low to moderate complexity as a constraint, embedded system design tools are allowed to employ more thorough and time-consuming optimization techniques. We implement a framework for performance comparison of guided probabilistic-search algorithms against deterministic algorithms. We also present an experimental setup for determining the importance of different phases in
scheduling and the effect of different approaches in achieving the final results. UMIACS-TR-2003-114