Although the dataflow model has been shown to allow the exploitation of parallelism at all levels, research of the past decade has revealed several fundamental problems: Synchronization at the instruction level, token matching, coloring and re-labeling operations have a negative impact on performance by significantly increasing the number of non-compute “overhead” cycles. Recently, many novel Hybrid von-Neumann Data Driven machines have been proposed to alleviate some of these problems. The major objective has been to reduce or eliminate unnecesssary synchronization costs through simplified operand matching schemes and increased task granularity. Moreover, the results from recent studies quantifying locality suggest sufficient spatial and temporal locality is present in dataflow execution to merit its exploitation.

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 to quantify coarse grain input and output latencies using a set of numeric 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 than 60% of the measured benchmarks favor a coarse grain execution. IN 64% of the cases the input latency to the cluster is the same in coarse or fine grain execution modes. The 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 intra-thread locality. Furthermore, simulation results indicate that strict or non-strict data structure access does not change the basic cluster characteristics.

Article A quantitative analysis of locality in dataflow programs Share on Authors: William Marcus Miller Department of Computer Science, Colorado State University, Ft. Collins, CO Department of Computer Science, Colorado State University, Ft. Collins, COView Profile , Walid A. Najjar Department of Computer Science, Colorado State University, Ft. Collins, CO Department of Computer Science, Colorado State University, Ft. Collins, COView Profile , A. P. Wim Böhm Department of Computer Science, Colorado State University, Ft. Collins, CO Department of Computer Science, Colorado State University, Ft. Collins, COView Profile Authors Info & Claims MICRO 24: Proceedings of the 24th annual international symposium on MicroarchitectureSeptember 1991 Pages 12–18https://doi.org/10.1145/123465.123469Online:01 September 1991Publication History 8citation242DownloadsMetricsTotal Citations8Total Downloads242Last 12 Months6Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access