We develop the first parallel graph coloring heuristics with strong theoretical guarantees on
work and depth and coloring quality. The key idea is to design a relaxation of the vertex …

Graph algorithms are challenging to parallelize on manycore architectures due to complex
data dependencies and irregular memory access. We consider the well studied problem of …

Influence maximization is an advanced graph-theoretic operation that aims to identify a set
of k most influential nodes in a network. The problem is of immense interest in many network …

Community detection is essential to various graph analysis applications. Infomap is a graph
clustering algorithm capable of achieving high-quality communities. However, it remains a …

Graph coloring-in a generic sense-is used to identify subsets of independent tasks in
parallel scientific computing applications. Traditional coloring heuristics aim to reduce the …

With its applicability spanning numerous data-driven fields, the implementation of graph
analytics on multicore platforms is gaining momentum. One of the most important …

Irregular applications comprise an increasingly important workload domain for many fields,
including bioinformatics, chemistry, physics, social sciences and machine learning …

Efficient processing of graph applications on heterogeneous CPU–GPU systems require
effectively harnessing the combined power of both the CPU and GPU devices. This paper …

We propose the analysis of a scalable parallel MCMC algorithm for graph coloring aimed at
balancing the color class sizes, provided that a suitable number of colors is made available …

In parallel computing, a valid graph coloring yields a lock-free processing of the colored
tasks, data points, etc., without expensive synchronization mechanisms. However, coloring …