In recent years, significant advances have been made in the design and evaluation of
balanced (hyper) graph partitioning algorithms. We survey trends of the past decade in …

We propose a deep convolutional neural network architecture codenamed Inception that
achieves the new state of the art for classification and detection in the ImageNet Large-Scale …

Natural graphs with skewed distributions raise unique challenges to distributed graph
computation and partitioning. Existing graph-parallel systems usually use a “one-size-fits-all” …

In pursuit of graph processing performance, the systems community has largely abandoned
general-purpose distributed dataflow frameworks in favor of specialized graph processing …

From social networks to targeted advertising, big graphs capture the structure in data and
are central to recent advances in machine learning and data mining. Unfortunately, directly …

This paper introduces a new approach to building distributed-memory graph analytics
systems that exploits heterogeneity in processor types (CPU and GPU), partitioning policies …

Hypergraph partitioning has been an important problem widely encountered in VLSI layout
design [23]. Recent works have introduced new application areas, including one …

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 …

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

Modeling multi-way data can be accomplished using tensors, which are data structures
indexed along three or more dimensions. Tensors are increasingly used to analyze …