High-performance implementations of graph algorithms are challenging to implement on
new parallel hardware such as GPUs because of three challenges:(1) the difficulty of coming …

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

With the extensive use of GPUs in modern supercomputers, accelerating sparse matrix-
vector multiplication (SpMV) on GPUs received much attention in the last couple of decades …

Sparse matrix-vector multiplication (SpMV) is a fundamental routine in computational
science and engineering. Its optimization methods on various homogeneous parallel …

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

We design and implement parallel graph coloring algorithms on the GPU using two different
abstractions—one data-centric (Gunrock), the other linear-algebra-based (GraphBLAS). We …

General sparse matrix–matrix multiplication (SpGEMM) is a fundamental building block of a
number of high-level algorithms and real-world applications. In recent years, several efficient …

GraphBLASis a recent standard that allows the expression of graph algorithms in the
language of linear algebra and enables automatic code parallelization and optimization …

GraphBLAS is a recent standard that allows the expression of graph algorithms in the
language of linear algebra and enables automatic code parallelization and optimization …

This work addresses the 2019 Sparse Deep Neural Network Graph Challenge with an
implementation of this challenge using the GraphBLAS programming model. We …