  Given an irregular dense tensor, how can we efficiently analyze it? An
irregular tensor is a collection of matrices whose columns have the same size
and rows have different sizes from each other. PARAFAC2 decomposition is a
fundamental tool to deal with an irregular tensor in applications including
phenotype discovery and trend analysis. Although several PARAFAC2 decomposition
methods exist, their efficiency is limited for irregular dense tensors due to
the expensive computations involved with the tensor. In this paper, we propose
DPar2, a fast and scalable PARAFAC2 decomposition method for irregular dense
tensors. DPar2 achieves high efficiency by effectively compressing each slice
matrix of a given irregular tensor, careful reordering of computations with the
compression results, and exploiting the irregularity of the tensor. Extensive
experiments show that DPar2 is up to 6.0x faster than competitors on real-world
irregular tensors while achieving comparable accuracy. In addition, DPar2 is
scalable with respect to the tensor size and target rank.
