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 …

Processing-In-Memory (PIM) architectures based on recent technology advances (eg,
Hybrid Memory Cube) demonstrate great potential for graph processing. However, existing …

Stochastic gradient descent (SGD) is one of the most popular numerical algorithms used in
machine learning and other domains. Since this is likely to continue for the foreseeable …

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

Efficient streaming graph processing systems leverage incremental processing by updating
computed results to reflect the change in graph structure for the latest graph snapshot …

Graph mining workloads aim to extract structural properties of a graph by exploring its
subgraph structures. General purpose graph mining systems provide a generic runtime to …

Continuous processing of a streaming graph maintains an approximate result of the iterative
computation on a recent version of the graph. Upon a user query, the accurate result on the …

Graph processing workloads are memory intensive with irregular access patterns and large
memory footprint resulting in low data locality. Their popular software implementations …

State-of-the-art streaming graph processing systems that provide Bulk Synchronous Parallel
(BSP) guarantees remain oblivious to the computation sparsity present in iterative graph …

Single-PC, disk-based processing of big graphs has recently gained much popularity. At the
core of an efficient disk-based system is a well-designed partition structure that can minimize …