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

Sparse general matrix–matrix multiplication (spGEMM) is an essential component in many
scientific and data analytics applications. However, the sparsity pattern of the input matrices …

Sparse general matrix-matrix multiplication (SpGEMM) is one of the most fundamental
building blocks in sparse linear solvers, graph processing frameworks and machine learning …

Sparse solvers provide essential functionality for a wide variety of scientific applications.
Highly parallel sparse solvers are essential for continuing advances in high-fidelity, multi …

Tensor Cores have been an important unit to accelerate Fused Matrix Multiplication
Accumulation (MMA) in all NVIDIA GPUs since Volta Architecture. To program Tensor Cores …

Linear systems are occurring in many applications, and solving them can take a large
amount of the total simulation time. The high performance library hypre provides a variety of …

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 …

As hardware architectures are evolving in the push towards exascale, developing
Computational Science and Engineering (CSE) applications depend on performance …

In the ongoing efforts targeting the vectorization of linear algebra primitives, sparse matrix-
matrix multiplication (SpGEMM) has received considerably less attention than sparse Matrix …

Sparse general matrix-matrix multiplication (SpGEMM) is an important kernel in
computational science and engineering, and has been widely studied on homogeneous …