Sparse tensors arise in problems in science, engineering, machine learning, and data
analytics. Programs that operate on such tensors can exploit sparsity to reduce storage …

Several manufacturers have already started to commercialize near-bank Processing-In-
Memory (PIM) architectures, after decades of research efforts. Near-bank PIM architectures …

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

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 …

Many scientific applications compute using sparse data and store that data in a variety of
sparse formats because each format has unique space and performance benefits …

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 …

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 …

An important sparse tensor computation is sparse-tensor-dense-matrix multiplication
(SpTM), which is used in tensor decomposition and applications. SpTM is a multi …

Irregular applications comprise an increasingly important workload domain for many fields,
including bioinformatics, chemistry, physics, social sciences and machine learning …

Streaming tensor factorization is an effective tool for unsupervised analysis of time-evolving
sparse data, which emerge in many critical domains such as cybersecurity and trend …