Efficient implementation on multiprocessors: The problem of signal processing applications modelling

In signal processing area, applications involve a large amount of computation, suggesting the use of multiprocessors to speed up processing. However, obtaining good performance is not easy because the machine should take advantage of the potential parallelisms of the studied application. That is why several parallel implementation methods using mapping and scheduling algorithms has been developped. One of development shells aims is application partitioning so that every part will be processed by a different processor, like SynDEx [1] or Ptolemy[2]. These shells use some graph models to exhibit both potential parallelisms of the application and the available multiprocessor parallelisms [3], but the task granularity problem is not considered when the application is modelized. The purpose of this paper is to emphasize the problem of the task granularity when the application is modelized by means of a graph and to study the impact on speedup. As a solution for this problem, this paper presents an original implementation method based on the variation of the granularity and regular application size.