Solving optimization problems with parallel algorithms has a long tradition in OR. Its future
relevance for solving hard optimization problems in many fields, including finance, logistics …

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

Partitioning and load balancing are important problems in scientific computing that can be
modeled as combinatorial problems using graphs or hypergraphs. The Zoltan toolkit was …

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 …

We present a suite of fast and effective algorithms, encapsulated in a software package
called ColPack, for a variety of graph coloring and related problems. Many of the coloring …

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 …

Combinatorial techniques have become essential tools across the landscape of
computational science, and some of the combinatorial ideas undergirding these tools are …

Graph algorithms are notorious for not getting good speedup on parallel architectures.
These algorithms tend to suffer from irregular dependencies and a high synchronization cost …

An unprecedented growth in data generation is taking place. Data about larger dynamic
systems is being accumulated, capturing finer granularity events, and thus processing …