General Sparse Matrix-Matrix Multiplication (SpGEMM) has attracted much attention from
researchers in graph analyzing, scientific computing, and deep learning. Many optimization …

Phenomenally successful in practical inference problems, convolutional neural networks
(CNN) are widely deployed in mobile devices, data centers, and even supercomputers. The …

Sparse matrix-matrix multiplication (or SpGEMM) is a key primitive for many high-
performance graph algorithms as well as for some linear solvers, such as algebraic …

Sparse matrix-matrix multiplication (SpGEMM) is a sparse kernel that is used in a number of
scientific applications. Although several SpGEMM algorithms have been proposed, almost …

This paper shows how to extend sparse tensor algebra compilers to introduce temporary
tensors called workspaces to avoid inefficient sparse data structures accesses. We develop …

Sparse matrix-matrix multiplication (SpGEMM) is a computational primitive that is widely
used in areas ranging from traditional numerical applications to recent big data analysis and …

Sparse matrix-matrix multiplication is a key kernel that has applications in several domains
such as scientific computing and graph analysis. Several algorithms have been studied in …

Sparse matrix-matrix multiplication (SpGEMM) is a computational primitive that is widely
used in areas ranging from traditional numerical applications to recent big data analysis and …

The duality between graphs and matrices means that many common graph analyses can be
expressed with primitives such as generalized sparse matrix-vector multiplication (SpMSpV) …

For many years, the highest energy cost in processing has been data movement rather than
computation, and energy is the limiting factor in processor design [21]. As the data needed …