BLOCK SCHEDULING OF ITERATIVE ALGORITHMS AND GRAPH-LEVEL PRIORITY SCHEDULING IN A SIMULATED DATA-FLOW MULTIPROCESSOR

While data-flow principles permit the utilization of large-scale multi processor systems with high programmability and good efficiency, they also introduce much overhead at runtime. In this paper, we have studie d an important class of PDE solvers, namely iterative methods for solv ing linear systems. Although these methods are inherently highly seque ntial, we have found that much parallelism could be exploited in a dat a-flow system by scheduling the iterative part of the algorithms in bl ocks and by looking ahead across several iterations. This approach is general and will apply to other iterative and loop-based problems. It is also demonstrated by simulation means, that relying solely on data- driven scheduling of parallel and unrolled loops results in low resour ce utilization and poor performance. A graph-level priority scheduling mechanism has been developed that drastically improves resource utili zation and yields higher performance.