We propose a scheme that allows memory-efficient in-place updates of intermediate matrices. Motivated by recent advances in big tensor decomposition from ...

Entire X needs to be accessed for each computation in each ALS iteration, incurring large data transport costs. Memory access pattern of the tensor data is ...

We propose a scheme that allows memory-efficient in-place updates of intermediate matrices. Motivated by recent advances in big tensor decomposition from ...

Memory-Efficient Parallel Computation of Tensor and Matrix Products for Big Tensor Decomposition. Niranjay Ravindran. ∗. , Nicholas D. Sidiropoulos.

Feb 7, 2015 · A parallel algorithm for low-rank tensor decomposition that is especially well-suited for big tensors is proposed. The new algorithm is based on ...

Co-authors ; Memory-efficient parallel computation of tensor and matrix products for big tensor decomposition. N Ravindran, ND Sidiropoulos, S Smith, G Karypis.

Memory-efficient parallel computation of tensor and matrix products for big tensor decomposition. In Proceedings of the Asilomar Conference on Signals ...

ABSTRACT. The decomposition of higher-order joint cumulant tensors of spatio- temporal data sets is useful in analyzing multi-variate non-Gaussian.

Performance study of parallel CP-ALS implementations for sparse tensors. Measure memory usage, processor stall cycles, execution time and scalability.

A parallel algorithm for low-rank tensor decomposition that is es- pecially well-suited for big tensors is proposed. The new algorithm.