BibTeX source
@inproceedings{10196528,
title = {Error-bounded Scalable Parallel Tensor Train Decomposition},
author = {Xie, Shiyao and Miura, Akinori and Ono, Kenji},
booktitle = {2023 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)},
year = {2023},
month = {May},
volume = {},
number = {},
pages = {345-353},
doi = {10.1109/IPDPSW59300.2023.00064},
url = {},
abstract = {Tensor train (TT) decomposition is a method for approximating and analysing tensors. TT-SVD, the most commonly used TT decomposition algorithm, computes the TT-format of a tensor in a sequential manner by alternately reshaping and compressing the tensor. For large tensors, this requires a large amount of computation time and memory. In this paper, we propose a distributed parallel algorithm, PTTD, to perform TT decomposition, which distributes parts of the tensor to all processes, decomposes it in parallel using TT-SVD, and merges the results to obtain the TT-format of the original tensor. Rounding is applied to reduce the size of the merged TT-formats. The algorithm is deterministic, which means that approximation error is controllable and there is no need to know the TT-ranks of the tensor in advance. Experimental results show that PTTD achieves an average speedup of $5384 \times$ using 8192 cores, and that the approximation error decreases as the number of cores increases, at the cost of slowly growing TT-ranks.},
peer_reviewed = {true},
issn = {},
keywords = {Distributed processing;Tensors;Costs;Conferences;Memory management;Approximation error;Approximation algorithms;tensor train decomposition;distributed tensor decomposition;TT-rounding;TT-format},
}