A graph neural network (GNN) enables deep learning on structured graph data. There are
two major GNN training obstacles: 1) it relies on high-end servers with many GPUs which …

Recent deep learning models have moved beyond low dimensional regular grids such as
image, video, and speech, to high-dimensional graph-structured data, such as social …

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” …

Graph neural networks (GNNs) emerge as a powerful approach to process non-euclidean
data structures and have been proved powerful in various application domains such as …

Performance of in-memory key-value store (KVS) continues to be of great importance as
modern KVS goes beyond the traditional object-caching workload and becomes a key …

Chaos scales graph processing from secondary storage to multiple machines in a cluster.
Earlier systems that process graphs from secondary storage are restricted to a single …

Processing a one trillion-edge graph has recently been demonstrated by distributed graph
engines running on clusters of tens to hundreds of nodes. In this paper, we employ a single …

For the widely deployed ECN-based congestion control schemes, the marking threshold is
the key to deliver high bandwidth and low latency. However, due to traffic dynamics in the …

There is currently an active debate on which RDMA primitive (ie, one-sided or two-sided) is
optimal for distributed transactions. Such a debate has led to a number of optimizations …

There is a growing need to perform a diverse set of real-time analytics (batch and stream
analytics) on evolving graphs to deliver the values of big data to users. The key requirement …