Multiprocessor mapping and scheduling algorithms have been extensively studied over the past few decades and have been tackled from different perspectives. In the late 1980's, the two-step decomposition of schedulingnto clustering and cluster-scheduling - was introduced. Ever since, several clustering and merging algorithms have been proposed and individually reported to be efficient. However, it is not clear how effective they are and how well they compare against single-step scheduling algorithms or other multistep algorithms. In this paper, we explore the effectiveness of the two-phase decomposition of scheduling and describe efficient and novel techniques that aggressively streamline interprocessor communications and can be tuned to exploit the significantly longer compilation time that is available to embedded system designers. We evaluate a number of leading clustering and merging algorithms using a set of benchmarks with diverse structures. We present an experimental setup for comparing the single-step against the two-step scheduling approach. We determine the importance of different steps in scheduling and the effect of different steps on overall schedule performance and show that the decomposition of the scheduling process indeed improves the overall performance. We also show that the quality of the solutions depends on the quality of the clusters generated in the clustering step. Based on the results, we also discuss why the parallel time metric in the clustering step may not provide an accurate measure for the final performance of cluster-scheduling

For multiprocessor embedded systems, the dynamic voltage scaling (DVS) technique can be applied to scheduled applications for energy reduction. DVS utilizes slack in the schedule to slow down processes and save energy. Therefore, it is generally believed that the maximal energy saving is achieved on a schedule with the minimum makespan (maximal slack). Most current approaches treat task assignment, scheduling, and DVS separately. In this paper, we present a framework called CASPER (combined assignment, scheduling, and power-management) that challenges this common belief by integrating task scheduling and DVS under a single iterative optimization loop via genetic algorithm. We have conducted extensive experiments to validate the energy efficiency of CASPER. For homogeneous multiprocessor systems (in which all processors are of the same type), we consider a recently proposed slack distribution algorithm (PDP-SPM) by S. Hua and G. Qu (2005): applying PDP-SPM on the schedule with the minimal makespan gives an average of 53.8% energy saving; CASPER finds schedules with slightly larger makespan but a 57.3% energy saving, a 7.8% improvement. For heterogeneous systems, we consider the power variation DVS (PV-DVS) algorithm by Schmitz et al. (2004), CASPER improves its energy efficiency by 8.2%. Finally, our results also show that the proposed single loop CASPER framework saves 23.3% more energy over GMA+EE-GLSA by Schmitz et al. (2002), the only other known integrated approach with a nested loop that combines scheduling and power management in the inner loop but leaves assignment in the outer loop.