Sparse-sparse matrix multiplication (SpGEMM) is a computation kernel widely used in
numerous application domains such as data analytics, graph processing, and scientific …

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

Sparse matrices are widely used in graph and data analytics, machine learning, engineering
and scientific applications. This paper describes and analyzes OuterSPACE, an accelerator …

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 linear algebra includes the fundamental and important operations in various large-
scale scientific computing and real-world applications. There exists performance bottleneck …

Important workloads, such as machine learning and graph analytics applications, heavily
involve sparse linear algebra operations. These operations use sparse matrix compression …

General sparse matrix-sparse matrix multiplication (SpGEMM) is one of the fundamental
linear operations in a wide variety of scientific applications. To implement efficient SpGEMM …

Sparse problems that dominate a wide range of applications fail to effectively benefit from
high memory bandwidth and concurrent computations in modern high-performance …

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 …

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