Quantitative Analysis of Data ow Program Execution { Preliminaries to a

While the dataaow execution model can potentially uncover all forms and levels of parallelism in a program, in its traditional ne-grain form, it does not exploit any form of locality. Recent evidence indicates that the exploitation of locality in dataaow programs could have a dramatic impact on performance. The current trend in the design of dataaow processors suggest a synthesis of traditional non-strict ne 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. We deene ne grain intra-thread locality as a dynamic measure of instruction level locality and quantify it using a set of numeric and non-numeric benchmarks. The results point to a very large degree of intra-thread locality and a remarkable uniformity and consistency of the distribution of thread locality across a wide variety of benchmarks. We also evaluate the resulting latency incurred through the partitioning of ne grain instructions into coarser grain threads. We deene the concept of a cluster of ne grain instructions to quantify coarse grain input and output latencies. The results of our experiments ooer compelling evidence that the inner loops of a signiicant number of numeric codes would beneet from coarse grain execution. Based on cluster execution times, more than 60% of the measured benchmarks favor a coarse grain execution. In 63% of the cases the input latency to the cluster is the same in coarse or ne grain execution modes. These results suggest that the eeects of increased instruction granularity on latency is minimal for a high percentage of the measured codes, and in large part is ooset by available intra-thread locality. Furthermore, simulation results indicate that strict or non-strict data structure access does not change the basic cluster characteristics.