With the explosive growth of user load data in power consumption information collection and load control systems,traditional computing frameworks and methods are faced with tremendous computational pressure when dealing with massive user load clustering and carrying out load characteristic analysis.In this paper,with a view to increasing accuracy and computational power of graphic process unit (GPU),the fast parallel K-means clustering algorithm for load curves is proposed based on Nvidia compute uniform device architecture (CUDA).This algorithm uses parallel acceleration strategies,such as parallelization of distance computing and curves counting,and rational allocation of thread blocks,which greatly improve the clustering speed of user load curves.A number of test examples show that the proposed clustering algorithm in this paper has a high acceleration ratio and strong adaptability,which is a good way to solve the problem of massive load curve clustering.