Data sets in large applications are often too massive to fit completely inside the computers
internal memory. The resulting input/output communication (or I/O) between fast internal …

Data mining of massive data sets is transforming the way we think about crisis response,
marketing, entertainment, cybersecurity and national intelligence. Collections of documents …

Graphs are powerful data representations favored in many computational domains. Modern
GPUs have recently shown promising results in accelerating computationally challenging …

Data-intensive, graph-based computations are pervasive in several scientific applications,
and are known to to be quite challenging to implement on distributed memory systems. In …

This paper discusses fast parallel algorithms for evaluating several centrality indices
frequently used in complex network analysis. These algorithms have been optimized to …

Data sets in large applications are often too massive to fit completely inside the computer's
internal memory. The resulting input/output communication (or I/O) between fast internal …

Processing large graphs is becoming increasingly important for many domains such as
social networks, bioinformatics, etc. Unfortunately, many algorithms and implementations do …

Graph is a significant data structure that describes the relationship between entries. Many
application domains in the real world are heavily dependent on graph data. However, graph …

Due to the limited capacity of GPU memory, the majority of prior work on graph applications
on GPUs has been restricted to graphs of modest sizes that fit in memory. Recent hardware …

We present an experimental study of the single source shortest path problem with non-
negative edge weights (NSSP) on large-scale graphs using the Δ-stepping parallel …