The symmetric sparse matrix-vector multiplication (SymmSpMV) is an important building
block for many numerical linear algebra kernel operations or graph traversal applications …

Graph coloring has been employed since the 1980s to efficiently compute sparse Jacobian
and Hessian matrices using either finite differences or automatic differentiation. Several …

With the prevalence of graph data in real-world applications (eg, social networks, mobile
phone networks, web graphs, etc.) and their ever-increasing size, many distributed graph …

Graph component labelling, which is a subset of the general graph colouring problem, is a
computationally expensive operation that is of importance in many applications and …

We study the problem of implementing graph algorithms efficiently on Pregel-like systems,
which can be surprisingly challenging. Standard graph algorithms in this setting can incur …

This paper introduces the largest-log-degree-first (LLF) and smallest-log-degree-last (SLL)
ordering heuristics for parallel greedy graph-coloring algorithms, which are inspired by the …

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 …

Abstract The Graph Coloring Problem (GCP) can be simply defined as partitioning the
vertices of a graph into independent sets while minimizing the number of colors used. So far …

We explore the interplay between architectures and algorithm design in the context of
shared-memory platforms and a specific graph problem of central importance in scientific …

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 …