Tiling is a key technique for data locality optimization and is widely used in high-
performance implementations of dense matrix-matrix multiplication for multicore/manycore …

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

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 …

We implement two novel algorithms for sparse-matrix dense-matrix multiplication (SpMM) on
the GPU. Our algorithms expect the sparse input in the popular compressed-sparse-row …

Triangle counting serves as a key building block for a set of important graph algorithms in
network science. In this paper, we address the IEEE HPEC Static Graph Challenge problem …

Network science methodology is increasingly applied to a large variety of real-world
phenomena, often leading to big network data sets. Thus, networks (or graphs) with millions …

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

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 …

This paper presents a new distributed-memory algorithm called FastSV for finding
connected components in an undirected graph. Our algorithm simplifies the classic Shiloach …

In 2013, we released a position paper to launch a community effort to define a common set
of building blocks for constructing graph algorithms in the language of linear algebra. This …