Abstract
The research on data science is heavily dependent upon efficient computing of higher dimensional matrix computation specially for recommender system, tensor computation, multilinear algebra, data mining, etc. Multidimensional matrices, a subset of tensors, are the basic data structure for such applications. The multidimensional matrix computation becomes complex when the number of dimension and length of each dimension increases as the workload for CPU becomes excessive that causes the performance degradation. Therefore, parallel algorithms for this computation are necessary and important. In this paper, we propose efficient parallel algorithm for multidimensional matrix operations. We partition the large matrix using the linearization technique. We used the Graphics Processing Unit (GPU) for partitioning and paralleling the multidimensional matrix operations. We found good speed up for large data sets.
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