Abstract
Simultaneous exploitation of task and data parallelism provides significant benefits for many applications. The basic approach for exploiting task and data parallelism is to use a task graph representation (Macro Dataflow Graph) for programs to decide on the degree of data parallelism to be used for each task (allocation) and an execution order for the tasks (scheduling). Previously, we presented a two step approach for allocation and scheduling by considering the two steps to be independent of each other. In this paper, we present a new simultaneous approach which uses constraints to model the scheduler during allocation. The new simultaneous approach provides significant benefits over our earlier approach for the benchmark task graphs that we have considered.
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