Distributed-Memory Randomized Algorithms for Sparse Tensor CP Decomposition
Sparse Candecomp / PARAFAC decomposition, a generalization of the matrix singular value decomposition to higher-dimensional tensors, is a popular tool for analyzing diverse datasets. On tensors with billions of nonzero entries, computing a CP decomposition is a computationally intensive task. We propose the first distributed-memory implementations of two randomized CP decomposition algorithms, CP-ARLS-LEV and STS-CP, that offer nearly an order-of-magnitude speedup at high decomposition ranks over well-tuned non-randomized decomposition packages. Both algorithms rely on leverage score sampling and enjoy strong theoretical guarantees, each with varying time and accuracy tradeoffs. We tailor the communication schedule for our random sampling algorithms, eliminating expensive reduction collectives and forcing communication costs to scale with the random sample count. Finally, we optimize the local storage format for our methods, switching between an analogue of compressed sparse column and compressed sparse row formats to facilitate both random sampling and efficient parallelization of sparse-dense matrix multiplication. Experiments show that our methods are fast and scalable, producing 11x speedup over SPLATT to compute a decomposition of the billion-scale Reddit tensor on 512 CPU cores in under 2 minutes.