Multidimensional dataflow graphs

In many signal processing applications, the tokens in a stream of tokens have a dimension higher than one. For example, the tokens in a video stream represent images so that a video application is actually three- or four-dimensional: Two dimensions are required in order to describe the pixel coordinates, one dimension indexes the different color components, and the time finally corresponds to the last dimension. Static multidimensional (MD) streaming applications can be modeled using one-dimensional dataflow graphs[7], but these are at best cyclostatic dataflow graphs, often with many phases in the actor’s vector valued token production and consumption patterns. These models incur a high control overhead. Furthermore such a notation hides many important algorithm properties such as inherent data parallelism, fine grained data dependencies and thus required memory sizes. Finally, the model is very implementation specific in that some of the degrees of freedom such as the processing order are already nailed down and cannot be changed easily without completely recreating the model