Sparse matrix-vector multiplication (SpMV) is an important primitive across a wide range of
application domains such as scientific computing and graph analytics. Due to its intrinsic …

Scalable parallel computing is essential for processing large scale-free (power-law) graphs.
The distribution of data across processes becomes important on distributed-memory …

Sparse Matrix-vector Multiplication (SpMV) is an important computation kernel widely used
in HPC and data centers. The irregularity of SpMV is a well-known challenge that limits …

We propose Distributed Neighbor Expansion (Distributed NE), a parallel and distributed
graph partitioning method that can scale to trillion-edge graphs while providing high …

We introduce XtraPuLP, a new distributed-memory graph partitioner designed to process
trillion-edge graphs. XtraPuLP is based on the scalable label propagation community …

At extreme scale, irregularities in the structure of scale-free graphs such as social network
graphs limit our ability to analyze these important and growing datasets. A key challenge is …

Driven by the importance of relational aspects of data to decision-making, graph algorithms
have been developed, based on simplified pairwise relationships, to solve a variety of …

We present techniques to process large scale-free graphs in distributed memory. Our aim is
to scale to trillions of edges, and our research is targeted at leadership class …

Sparse Matrix-Vector multiplication (SpMV) is a fundamental kernel, used by a large class of
numerical algorithms. Emerging big-data and machine learning applications are propelling …

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