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The problem of closed frequent itemset discovery is a fundamental problem of data mining, having applications in numerous domains. It is thus very important to have efficient parallel algorithms to solve this problem, capable of efficiently harnessing the power of multicore processors that exists in our computers (notebooks as well as desktops). In this paper we present PLCMQS, a parallel algorithm based on the LCM algorithm, recognized as the most efficient algorithm for sequential discovery of closed frequent itemsets. We also present a simple yet powerfull parallelism interface based on the concept of Tuple Space, which allows an efficient dynamic sharing of the work. Thanks to a detailed experimental study, we show that PLCMQS is efficient on both on sparse and dense databases.