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Many modern computing platforms, including "aggressive" multicore architectures, proposed exascale architectures, and many modalities of Internetbased computing are "task hungry"--their performance is enhanced by always having as many tasks eligible for allocation to processors as possible. The AREAOriented scheduling (AO-scheduling) paradigm for computations with intertask dependencies--modeled as DAGs--was developed to address the "hunger" of such platforms, by executing an input DAG so as to render tasks eligible for execution quickly. AO-scheduling is a weaker, but more robust, successor to IC-scheduling. The latter renders tasks eligible for execution maximally fast--a goal that is not achievable for many DAGs.AO-scheduling coincides with IC-scheduling on DAGs that admit optimal IC-schedules--and optimal AO-scheduling is possible for all DAGs. The computational complexity of optimal AO-scheduling is not yet known; therefore, this goal is replaced here by a multi-phase heuristic that produces optimal AO-schedules for series-parallel DAGs but possibly suboptimal schedules for general DAGs. This paper employs simulation experiments to assess the computational benefits of AO-scheduling in a variety of scenarios and on a range of DAGs whose structure is reminiscent of ones encountered in scientific computing. The experiments pit AO-scheduling against a range of heuristics, fromlightweight ones such as FIFO scheduling to computationally more intensive ones that mimic IC-scheduling's local decisions. The observed results indicate that AO-scheduling does enhance the efficiency of task-hungry platforms, by amounts that vary according to the availability patterns of processors and the structure of the DAG being executed.