Abstract--SUDA2 is a recursive search algorithm for Minimal Unique Itemset detection. Such sets of items are formed via combinations of non-obvious attributes enabling individual record identification. The nature of SUDA2 allows work to be divided into non-overlapping tasks enabling parallel execution. Earlier work developed a parallel implementation for SUDA2 on an SMP cluster, and this was found to be several orders of magnitude faster than sequential SUDA2. However, if fixedgranularity parallel tasks are scheduled naively in the order of their generation, the system load tends to be imbalanced with little work at the beginning and end of the search. This paper investigates the effectiveness of variable-grained and dynamic work generation strategies for parallel SUDA2. These methods restrict the number of sub-tasks to be generated, based on the criterion of probable work size. The further we descend in the search recursion tree, the smaller the tasks become, thus we only select the l...
Paraskevas Yiapanis, David J. Haglin, Anna M. Mann