We propose a new variant of nonnegative matrix factorization (NMF), combining separability and sparsity assumptions. Separability requires that the columns first NMF factor are equal to input matrix, while second sparse. call this sparse separable (SSNMF), which we prove be NP-complete, as opposed can solved in polynomial time. The main motivation consider model is handle underdetermined blind ...

Sparse matrix–vector multiplication (SpMV) is one of the most important kernels in high-performance computing (HPC), yet SpMV normally suffers from ill performance on many devices. Due to performance, requires special care store and tune for a given device. Moreover, HPC facing heterogeneous hardware containing multiple different compute units, e.g., many-core CPUs GPUs. Therefore, an emerging ...

General Sparse Matrix-Matrix Multiplication (SpGEMM) has attracted much attention from researchers in graph analyzing, scientific computing, and deep learning. Many optimization techniques have been developed for different applications computing architectures over the past decades. The objective of this paper is to provide a structured comprehensive overview researches on SpGEMM. Existing group...