Multiple　Nonnegative　Matrices　Factorization　(MNMF)　is　a　promising　method　to　study　and　analyze　a　dataset　which　has　different　types　of　features　or　relationships.　However,　due　to　the　high　computational　cost,　MNMF　cannot　meet　the　needs　of　time　response　for　large-scale　datasets.　In　this　paper,　we　introduce　a　Parallel　Multiple　Nonnegative　Matrices　Factorization　(PMNMF)　approach　which　is　implemented　on　Graphics　Processing　Unit　(GPU)　under　the　Compute　Unified　Device　Architecture　(CUDA)　framework.　Experimental　studies　demonstrate　that　PMNMF　approach　using　GPU　is　able　to　obtain　100×　speedup　in　comparison　to　the　traditional　multiple　nonnegative　matrices　factorization　under　our　experimental　condition.　©　2016　ICIC　International.