Tensor factorizations are powerful tools in many machine learning and data analytics
applications. Tensors are often sparse, which makes sparse tensor factorizations memory …

This paper proposes a new storage format for sparse tensors, called Hierarchical
COOrdinate (HiCOO; pronounced:“haiku”). It derives from coordinate (COO) format, arguably …

Sparse matricized tensor times Khatri-Rao product (MTTKRP) is one of the most
computationally expensive kernels in sparse tensor computations. This work focuses on …

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 …

Emerging hybrid memory systems that comprise technologies such as Intel's Optane DC
Persistent Memory, exhibit disparities in the access speeds and capacity ratios of their …

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 …

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 …

Sparse Matricized Tensor Times Khatri-Rao Product (spMTTKRP) is the most
computationally intensive kernel in sparse tensor decomposition. In this paper, we propose …

Tensor methods have gained increasingly attention from various applications, including
machine learning, quantum chemistry, healthcare analytics, social network analysis, data …

Emerging workloads benefit from massive memory capacities provided by hybrid memory
platforms. Recent system-level hybrid memory management solutions integrate machine …