Tratamento de Código Seqüencial no Modelo de Fluxo de Dados
Abstract
One of unanswered questions in multiprocessing is that of determining the ideal size or "granularity" of tasks or processes, aiming at an acceptable machine performance. The difficulties here stem from the fact that granularity conflicts with synchronization requirements. Multiprocessors based on the von Neumann model exploit parallelism at procedure and command level, thus supporting coarse granularity. This requires fewer synchronizations, but does not fully exploit application parallelism. Dataflow machines, on the other hand, exploit instruction-level parallelism, thus supporting fine granularity. Despite there better approach to parallelism, dataflow machines are penalized by the large number of required synchronizations. This paper addresses the issue of reducing the number of required synchronizations in the dataftow model by grouping sequential pieces of code.
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