Wilkinson defined a sparse matrix as one with enough zeros that it pays to take advantage of
them. 1 This informal yet practical definition captures the essence of the goal of direct …

Graph data modeling and querying arises in many practical application domains such as
social and biological networks where the primary focus is on concepts and their …

There has been significant recent interest in parallel graph processing due to the need to
quickly analyze the large graphs available today. Many graph codes have been designed …

For large-scale graph analytics on the GPU, the irregularity of data access and control flow,
and the complexity of programming GPUs, have presented two significant challenges to …

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 …

The problem of computing (α, β α, β)-core in a bipartite graph for given α α and β β is a
fundamental problem in bipartite graph analysis and can be used in many applications such …

As graphs continue to grow, external memory graph processing systems serve as a
promising alternative to inmemory solutions for low cost and high scalability. Unfortunately …

Applications with low data reuse and frequent irregular memory accesses, such as graph or
sparse linear algebra workloads, fail to scale well due to memory bottlenecks and poor core …

With high computation power and memory bandwidth, graphics processing units (GPUs)
lend themselves to accelerate data-intensive analytics, especially when such applications fit …

Connected components is a fundamental kernel in graph applications. The fastest existing
parallel multicore algorithms for connectivity are based on some form of edge sampling …