Maximal frequent itemsets are one of sevelral condensed representations of frequent itemsets, which store most of the information contained in frequent itemsets using less space. This paper proposes an efficient implementation of maximal frequent itemset mining MG utilizing graphics processing units. Our method employs a single-instruction-multiple-data architecture to accelerate the mining speed with using a bitmap data structure of frequent itemsets. Our experimental results show that our algorithm is effective and efficient.