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(eng): CANDECOMP/PARAFAC (CP) has found numerous applications in wide variety of areas such as in chemometrics, telecommunication, data mining, neuroscience, separated representations. For an order- tensor, most CP algorithms can be computationally demanding due to computation of gradients which are related to products between tensor unfoldings and Khatri-Rao products of all factor matrices except one. These products have the largest workload in most CP algorithms. In this paper, we propose a fast method to deal with this issue. Themethod also reduces the extra memory requirements of CP algorithms. As a result, we can accelerate the standard alternating CP algorithms 20–30 times for order-5 and order-6 tensors, and even higher ratios can be obtained for higher order tensors (e.g., N>=10). The proposed method is more efficient than the state-of-the-art ALS algorithm which operates two modes at a time (ALSo2) in the Eigenvector PLS toolbox, especially for tensors with order N>=5 and high rank.