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Given an irregular dense tensor, how can we efficiently analyze it? Anirregular tensor is a collection of matrices whose columns have the same sizeand rows have different sizes from each other. PARAFAC2 decomposition is afundamental tool to deal with an irregular tensor in applications includingphenotype discovery and trend analysis. Although several PARAFAC2 decompositionmethods exist, their efficiency is limited for irregular dense tensors due tothe expensive computations involved with the tensor. In this paper, we proposeDPar2, a fast and scalable PARAFAC2 decomposition method for irregular densetensors. DPar2 achieves high efficiency by effectively compressing each slicematrix of a given irregular tensor, careful reordering of computations with thecompression results, and exploiting the irregularity of the tensor. Extensiveexperiments show that DPar2 is up to 6.0x faster than competitors on real-worldirregular tensors while achieving comparable accuracy. In addition, DPar2 isscalable with respect to the tensor size and target rank.

DPar2: Fast and Scalable PARAFAC2 Decomposition for Irregular Dense Tensors