AN EVALUATION OF MEDIUM-GRAIN DATA-FLOW CODE

In this paper, we study several issues related to the medium grain dataflow model of execution. We present bottom-up compilation of medium grain clusters from a fine grain dataflow graph. We compare the basic block and the dependence sets algorithms that partition dataflow graphs into clusters. For an extensive set of benchmarks we assess the average number of instructions in a cluster and the reduction in matching operations compared with fine grain dataflow execution. We study the performance of medium grain dataflow when several architectural parameters, such as the number of processors, matching cost, and network latency, are varied. The results indicate that medium grain execution offers a good speedup over the fine grain model, that it is scalable, and tolerates network latency and high matching costs well. Medium grain execution can benefit from a higher output bandwidth of a processor and finally, a simple superscalar processor with an issue rate of two is sufficient to exploit the internal parallelism of a cluster.