A C++ Library for Tensor Decomposition

In this paper, we develop a new library TenDeC++ for tensor decompositions in C++. TenDeC++ supports popular tensor decomposition functions including Canonical Polyadic, Tucker, tensor-train, and t-SVD, assisting C++ programmers to shorten the development cycle of deep learning applications. Compared with the resource-intensive Python and MATLAB, C++ has the nature advantages on fast running time and high compatibility. To further explore potentials of C++, we propose a novel underlying technology PointerDefomer leveraging the unique pointer. Since the transformation between tensor and size-specific matrix is indispensable in tensor decompositions, PointerDefomer can virtually achieve such a transformation by controlling the movement of pointer in memory address. As a result, the conventional transformation steps can be skipped to accelerate the decomposition process and there is no memory needed for saving the intermediate results of tensor transformation. In our experiment, TenDeC++ reduces decomposition time and support larger size of tensor compared with the classic Tensorly in Python and TensorLab in MATLAB, respectively.