Partial least squares(PLS) algorithm is one of the most common used chemometric algorithms，and is often combined with infrared and near infrared spectroscopy analysis.However，its regular implementation in a single-threaded way makes the modeling process severely ineffective when there are a great deal of models to built，or when there are iterative optimizations of the wavelength ranges and its preprocessing methods need to build an optimal model which contains thousands of samples，enormous data points，and uses a large number of principal components.To give an effective modeling method in this situation，this paper presented a novel parallel chemometric computation strategy which takes the Graphic Processing Unit(GPU) as computing devices，and then the parallel PLS algorithm，i.e.CUPLS，is implemented using the CUBLAS library.Finally，using a large near infrared spectroscopy(NIR) dataset as the test bed，a performance comparison experiment is conducted，and the results showed that the speed of the parallel algorithm is 42 times faster than that of the CPU-based implementation，which dramatically improves the efficiency of chemometric modeling algorithm.The proposed method shines a light on speeding up other chemometric algorithms with appropriate adoption.