DeepAI AI Chat
Log In Sign Up

cuFasterTucker: A Stochastic Optimization Strategy for Parallel Sparse FastTucker Decomposition on GPU Platform

by   Zixuan Li, et al.
National University of Defense Technology
Hunan University

Currently, the size of scientific data is growing at an unprecedented rate. Data in the form of tensors exhibit high-order, high-dimensional, and highly sparse features. Although tensor-based analysis methods are very effective, the large increase in data size makes the original tensor impossible to process. Tensor decomposition decomposes a tensor into multiple low-rank matrices or tensors that can be exploited by tensor-based analysis methods. Tucker decomposition is such an algorithm, which decomposes a n-order tensor into n low-rank factor matrices and a low-rank core tensor. However, most Tucker decomposition methods are accompanied by huge intermediate variables and huge computational load, making them unable to process high-order and high-dimensional tensors. In this paper, we propose FasterTucker decomposition based on FastTucker decomposition, which is a variant of Tucker decomposition. And an efficient parallel FasterTucker decomposition algorithm cuFasterTucker on GPU platform is proposed. It has very low storage and computational requirements, and effectively solves the problem of high-order and high-dimensional sparse tensor decomposition. Compared with the state-of-the-art algorithm, it achieves a speedup of around 15X and 7X in updating the factor matrices and updating the core matrices, respectively.


SGD_Tucker: A Novel Stochastic Optimization Strategy for Parallel Sparse Tucker Decomposition

Sparse Tucker Decomposition (STD) algorithms learn a core tensor and a g...

Streaming probabilistic tensor train decomposition

The Bayesian streaming tensor decomposition method is a novel method to ...

Sparse and Low-rank Tensor Estimation via Cubic Sketchings

In this paper, we propose a general framework for sparse and low-rank te...

A weighted subspace exponential kernel for support tensor machines

High-dimensional data in the form of tensors are challenging for kernel ...

ADA-Tucker: Compressing Deep Neural Networks via Adaptive Dimension Adjustment Tucker Decomposition

Despite the recent success of deep learning models in numerous applicati...

Dynasor: A Dynamic Memory Layout for Accelerating Sparse MTTKRP for Tensor Decomposition on Multi-core CPU

Sparse Matricized Tensor Times Khatri-Rao Product (spMTTKRP) is the most...

SNeCT: Scalable network constrained Tucker decomposition for integrative multi-platform data analysis

Motivation: How do we integratively analyze large-scale multi-platform g...