An Analysis of Latency in Dataflow Execution

Recent evidence indicates that the exploitation of locality in dataflow programs could have a dramatic impact on performance. The current trend in the design of dataflow processors suggest a synthesis of traditional non-strict fine grain instruction execution and a strict coarse grain execution in order to exploit locality. While an increase in instruction granularity will favor the exploitation of locality within a single execution thread, the resulting grain size may increase latency among execution threads. In this paper, the resulting latency incurred through the partitioning of fine grain instructions into coarser grain threads is evaluated. We define the concept of a cluster of fine grain instructions to quantify coarse grain input and output latencies using a set of numeric/floating point benchmarks. The results offer compelling evidence that the inner loops of a significant number of numeric codes would benefit from coarse grain execution. Based on cluster execution times, more..