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Principle component analysis (PCA)  is widely utilized in hyperspectral image analysis [3, 4, 5]. There are three major approaches of principle component analysis: singular value decomposition (SVD) , covariance-matrix and iterative method (NIPALS) [6, 7]. In our previous work , we have demonstrated the advantage of the GPU implementation of covariance method for medium-sized hyperspectral images. In this paper, we present an improvement which combines the multithreading in CPU, GPU and CUDA's graphics interoperability . It is found that this combined framework approaches real-time processing much further.