A Quantitative Analysis of Dataflow Program Execution - Preliminaries to a Hybrid Design

While the dataflow execution model can potentially uncover all forms and levels of parallelism in a program, in its traditional fine grain form it does not exploit any form of locality. 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 suggests a synthesis of traditional nonstrict fine grain instruction execution and strict coarse grain execution in order to exploit locality. While an increase in instruction granularity favors the exploitation of locality within a single execution thread, the resulting grain size may increase latency among execution threads. We define fine grain intrathread locality as a dynamic measure of instruction level locality and quantify it using a set of numeric and nonnumeric benchmarks. The results point to a very large degree of intrathread locality and a remarkable uniformity and consistency of the distribution of thread locality across a wide variety of benchmarks. As the execution is moved to a coarser granularity it can result in an increase of the input latency of operands that would have a detrimental effect on performance. We evaluate the resulting latency incurred through the partitioning of fine grain instructions into coarser grain threads. We define the concept of a cluster of fine grain instructions to quantify coarse grain input and output latencies. The results of our experiments offer compelling evidence that a coarse grain execution outperforms a fine grain grain one on a significant number of numeric codes. These results suggest that the effects of increased instruction granularity on latency is minimal for a high percentage of the measured codes, and in large part is offset by available intrathread locality. Furthermore, simulation results indicate that strict or nonstrict data structure access does not change the basic cluster characteristics.