A model for dataflow based vector execution
William Marcus Miller, Walid A. Najjar, Stanislav Böhm · 1994
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.