Model-Driven Sparse Cp Decomposition For Higher-Order Tensors

Given an input tensor, its CANDECOMP/PARAFAC decomposition (or CPD) is a low-rank representation. CPDs are of particular interest in data analysis and mining, especially when the data tensor is sparse and of higher order (dimension). This paper focuses on the central bottleneck of a CPD algorithm, which is evaluating a sequence of matricized tensor times Khatri-Rao products (MTTKRPs). To speed up the MTTKRP sequence, we propose a novel, adaptive tensor memoization algorithm, ADATM. Besides removing redundant computations within the MTTKRP sequence, which potentially reduces its overall asymptotic complexity, our technique also allows a user to make a space-time tradeoff by automatically tuning algorithmic and machine parameters using a model-driven framework. Our method improves as the tensor order grows, making its performance more scalable for higher-order data problems. We show speedups of up to 8x and 820x on real sparse data tensors with orders as high as 85 over the SPLATT package and Tensor Toolbox library respectively; and on a full CPD algorithm (CP-ALS), ADATM can be up to 8x faster than state-of-the-art method implemented in SPLATT.