Generation and quantitative evaluation of dataflow clusters
Lucas Roh, Walid A. Najjar, Stanislav Böhm · 1993
Multithreaded or hybrid von Neumann/dataflow execution models have an advantage over the fine-grain dataflow model in that they significantly reduce the run time overhead incurred by matching. In this paper, we look at two issues related to the evaluation of a coarse-grain dataflow model of execution. The first issue concerns the compilation into a coarsegrain code from a fine-grain one. In this study, the concept of coarse-grain code is captured by clusters which can be thought of as mini-dataflow graphs which execute strictly, deterministically and without blocking. We look at two bottom-up algorithms: the basic block and the dependence sets methods, to partition dataflow graphs into clusters. The second issue is the actual performance of the clusterbased execution as several architecture parameters are varied (e.g. number of processors, matching cost, network latency, etc.). From the extensive simulation data we evaluate (1) the potential speedup over the fine-grain execution and (2...