A-tucker: Input-adaptive and matricization-free tucker decomposition for dense tensors on CPUs and GPUs
M Li, C Xiao, C Yang - arXiv preprint arXiv:2010.10131, 2020 - arxiv.org
Tucker decomposition is one of the most popular models for analyzing and compressing
large-scale tensorial data. Existing Tucker decomposition algorithms usually rely on a single …
large-scale tensorial data. Existing Tucker decomposition algorithms usually rely on a single …
a-tucker: fast input-adaptive and matricization-free tucker decomposition of higher-order tensors on GPUs
Tucker decomposition is one of the most popular models for analyzing and compressing
large-scale tensorial data. Existing Tucker decomposition algorithms are usually based on a …
large-scale tensorial data. Existing Tucker decomposition algorithms are usually based on a …
A GPU-based tensor decomposition method for large-scale tensors
Recently, as the sizes of real tensors have become overwhelmingly large including billions
of nonzeros, fast and scalable Tucker decomposition methods have become increasingly …
of nonzeros, fast and scalable Tucker decomposition methods have become increasingly …
Faster TKD: Towards Lightweight Decomposition for Large-Scale Tensors With Randomized Block Sampling
The Tucker Decomposition (TKD) is able to provide the low-dimensional and informative
representations of real-world large-scale tensorial data, which are necessary to extract …
representations of real-world large-scale tensorial data, which are necessary to extract …
cuFasterTucker: A Stochastic Optimization Strategy for Parallel Sparse FastTucker Decomposition on GPU Platform
Z Li, Y Qin, Q Xiao, W Yang, K Li - ACM Transactions on Parallel …, 2024 - dl.acm.org
The amount of scientific data is currently growing at an unprecedented pace, with tensors
being a common form of data that display high-order, high-dimensional, and sparse …
being a common form of data that display high-order, high-dimensional, and sparse …
D-tucker: Fast and memory-efficient tucker decomposition for dense tensors
Given a dense tensor, how can we find latent patterns and relations efficiently? Existing
Tucker decomposition methods based on Alternating Least Square (ALS) have limitations in …
Tucker decomposition methods based on Alternating Least Square (ALS) have limitations in …
Efficient parallel sparse symmetric tucker decomposition for high-order tensors
Tensor based methods are receiving renewed attention in recent years due to their
prevalence in diverse real-world applications. There is considerable literature on tensor …
prevalence in diverse real-world applications. There is considerable literature on tensor …
Accelerating the tucker decomposition with compressed sparse tensors
S Smith, G Karypis - European Conference on Parallel Processing, 2017 - Springer
The Tucker decomposition is a higher-order analogue of the singular value decomposition
and is a popular method of performing analysis on multi-way data (tensors). Computing the …
and is a popular method of performing analysis on multi-way data (tensors). Computing the …
Optimizing sparse tensor times matrix on GPUs
This work optimizes tensor-times-dense matrix multiply (Ttm) for general sparse and semi-
sparse tensors on CPU and NVIDIA GPU platforms. Ttm is a computational kernel in tensor …
sparse tensors on CPU and NVIDIA GPU platforms. Ttm is a computational kernel in tensor …
GPUTucker: Large-Scale GPU-Based Tucker Decomposition Using Tensor Partitioning
Tucker decomposition is used extensively for modeling multi-dimensional data represented
as tensors. Owing to the increasing magnitude of nonzero values in real-world tensors, a …
as tensors. Owing to the increasing magnitude of nonzero values in real-world tensors, a …