Sptfs: Sparse tensor format selection for mttkrp via deep learning
Canonical polyadic decomposition (CPD) is one of the most common tensor computations
adopted in many scientific applications. The major bottleneck of CPD is matricized tensor …
adopted in many scientific applications. The major bottleneck of CPD is matricized tensor …
Input-aware sparse tensor storage format selection for optimizing MTTKRP
Canonical polyadic decomposition (CPD) is one of the most common tensor computations
adopted in many scientific applications. The major bottleneck of CPD is matricized tensor …
adopted in many scientific applications. The major bottleneck of CPD is matricized tensor …
Towards efficient canonical polyadic decomposition on sunway many-core processor
Abstract Canonical Polyadic Decomposition (CPD) is one of the most popular tensor
decomposition methods and plays an important role in big data analysis. For sparse tensor …
decomposition methods and plays an important role in big data analysis. For sparse tensor …
Exploiting hierarchical parallelism and reusability in tensor kernel processing on heterogeneous HPC systems
Canonical Polyadic Decomposition (CPD) of sparse tensors is an effective tool in various
machine learning and data analytics applications, in which sparse Matricized Tensor Times …
machine learning and data analytics applications, in which sparse Matricized Tensor Times …
Sparsity-aware tensor decomposition
Sparse tensor decomposition, such as Canonical Polyadic Decomposition (CPD), is a key
operation for data analytics and machine learning. Its computation is dominated by a set of …
operation for data analytics and machine learning. Its computation is dominated by a set of …
Efficient parallel CP decomposition with pairwise perturbation and multi-sweep dimension tree
L Ma, E Solomonik - 2021 IEEE International Parallel and …, 2021 - ieeexplore.ieee.org
The widely used alternating least squares (ALS) algorithm for the canonical polyadic (CP)
tensor decomposition is dominated in cost by the matricized-tensor times Khatri-Rao product …
tensor decomposition is dominated in cost by the matricized-tensor times Khatri-Rao product …
Reconfigurable low-latency memory system for sparse matricized tensor times khatri-rao product on fpga
S Wijeratne, R Kannan… - 2021 IEEE High …, 2021 - ieeexplore.ieee.org
Tensor decomposition has become an essential tool in many applications in various
domains, including machine learning. Sparse Matricized Tensor Times Khatri-Rao Product …
domains, including machine learning. Sparse Matricized Tensor Times Khatri-Rao Product …
NeuLFT: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors
AH igh-D imensional and I ncomplete (HDI) tensor is frequently encountered in a big data-
related application concerning the complex dynamic interactions among numerous entities …
related application concerning the complex dynamic interactions among numerous entities …
Dynasor: A dynamic memory layout for accelerating sparse mttkrp for tensor decomposition on multi-core cpu
S Wijeratne, R Kannan… - 2023 IEEE 35th …, 2023 - ieeexplore.ieee.org
Sparse Matricized Tensor Times Khatri-Rao Prod-uct (spMTTKRP) is the most time-
consuming compute kernel in sparse tensor decomposition. In this paper, we introduce a …
consuming compute kernel in sparse tensor decomposition. In this paper, we introduce a …
IAP-SpTV: An input-aware adaptive pipeline SpTV via GCN on CPU-GPU
Sparse tensor-times-vector (SpTV) is the core computation of tensor decomposition.
Optimizing the computational performance of SpTV on CPU-GPU becomes a challenge due …
Optimizing the computational performance of SpTV on CPU-GPU becomes a challenge due …