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

Generalized Sparse Matrix-Matrix Multiplication (SpGEMM) is a ubiquitous task in various
engineering and scientific applications. However, inner product based SpGEMM introduces …

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

There has been significant recent interest in parallel frameworks for processing graphs due
to their applicability in studying social networks, the Web graph, networks in biology, and …

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

This paper presents a scalable high-performance software library to be used for graph
analysis and data mining. Large combinatorial graphs appear in many applications of high …

Data-intensive, graph-based computations are pervasive in several scientific applications,
and are known to to be quite challenging to implement on distributed memory systems. In …

Generalized 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 key operation in numerous areas from
information to the physical sciences. Implementing SpGEMM efficiently on throughput …

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 …