The Feedback-Guided Dynamic Loop Scheduling (FGDLS) algorithm [1] is a recent dynamic approach to the scheduling of a parallel loop within a sequential outer loop. Earlier papers have analysed convergence under the assumption that the workload is a positive, continuous, function of a continuous argument (the iteration number). However, this assumption is unrealistic since it is known that the iteration number is a discrete variable. In this paper we extend the proof of convergence of the algorithm to the case where the iteration number is treated as a discrete variable. We are able to establish convergence of the FGDLS algorithm for the case when the workload is monotonically decreasing.

In this paper we consider the Feedback-Guided Dynamic Loop Scheduling (FGDLS) method that was proposed by Bull. The method uses a feedback-guided mechanism to schedule a parallel loop within a sequential outer loop. The execution times and the scheduling bounds at a outer iteration are used to find the scheduling bound of the next outer iteration. In this way FGDLS achieves an optimal load balance. Two algorithms have been proposed so far by Tabirca et al. In this article we will review these two algorithms and will give a comparison between their performances.