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

This paper proposes a new storage format for sparse tensors, called Hierarchical
COOrdinate (HiCOO; pronounced:“haiku”). It derives from coordinate (COO) format, arguably …

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 …

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 …

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 …

Combinatorial algorithms such as those that arise in graph analysis, modeling of discrete
systems, bioinformatics, and chemistry, are often hard to parallelize. The Combinatorial …