Load-balanced sparse mttkrp on gpus
Sparse matricized tensor times Khatri-Rao product (MTTKRP) is one of the most
computationally expensive kernels in sparse tensor computations. This work focuses on …
computationally expensive kernels in sparse tensor computations. This work focuses on …
Efficient and effective sparse tensor reordering
This paper formalizes the problem of reordering a sparse tensor to improve the spatial and
temporal locality of operations with it, and proposes two reordering algorithms for this …
temporal locality of operations with it, and proposes two reordering algorithms for this …
An efficient mixed-mode representation of sparse tensors
The Compressed Sparse Fiber (CSF) representation for sparse tensors is a generalization of
the Compressed Sparse Row (CSR) format for sparse matrices. For a tensor with d modes …
the Compressed Sparse Row (CSR) format for sparse matrices. For a tensor with d modes …
Minimum cost loop nests for contraction of a sparse tensor with a tensor network
R Kanakagiri, E Solomonik - Proceedings of the 36th ACM Symposium …, 2024 - dl.acm.org
Sparse tensor decomposition and completion are common in numerous applications,
ranging from machine learning to computational quantum chemistry. Typically, the main …
ranging from machine learning to computational quantum chemistry. Typically, the main …
Efficient, out-of-memory sparse MTTKRP on massively parallel architectures
A Nguyen, AE Helal, F Checconi… - Proceedings of the 36th …, 2022 - dl.acm.org
Tensor decomposition (TD) is an important method for extracting latent information from high-
dimensional (multi-modal) sparse data. This study presents a novel framework for …
dimensional (multi-modal) sparse data. This study presents a novel framework for …
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 …
Distributed-memory tensor completion for generalized loss functions in python using new sparse tensor kernels
Tensor computations are increasingly prevalent numerical techniques in data science, but
pose unique challenges for high-performance implementation. We provide novel algorithms …
pose unique challenges for high-performance implementation. We provide novel algorithms …
Multi-level optimization of the canonical polyadic tensor decomposition at large-scale: Application to the stratification of social networks through deflation
Tensors are multi-dimensional mathematical objects that allow to model complex
relationships and to perform decompositions for analytical purpose. They are used in a wide …
relationships and to perform decompositions for analytical purpose. They are used in a wide …
Distributed-memory tensor completion for generalized loss functions in python using new sparse tensor kernels
Tensor computations are increasingly prevalent numerical techniques in data science, but
pose unique challenges for high-performance implementation. We provide novel algorithms …
pose unique challenges for high-performance implementation. We provide novel algorithms …
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 …