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Frequent itemset mining is a fundamental and essential issue in data mining field and can be used in many data mining tasks. Most of these mining tasks require multiple passes over the database and if the database size is large, which is usually the case, scalable high performance solutions involving multiple processors are required. In this paper, we present a novel parallel frequent itemset mining algorithm which is called HPFP-Miner. The proposed algorithm is based on FP-Growth and introduces little communication overheads by efficiently partitioning the list of frequent elements list over processors. The results of experiment show that HPFP-Miner has good scalability and performance.