Graph neural networks (GNN) have shown great success in learning from graph-structured
data. They are widely used in various applications, such as recommendation, fraud …

There is growing interest in graph pattern mining (GPM) problems such as motif counting.
GPM systems have been developed to provide unified interfaces for programming …

Graph analytics is an emerging application which extracts insights by processing large
volumes of highly connected data, namely graphs. The parallel processing of graphs has …

Many graph processing systems have been recently developed for many-core processors.
However, for iterative graph processing, due to the dependencies between vertices' states …

Address translation is a performance bottleneck in data-intensive workloads due to large
datasets and irregular access patterns that lead to frequent high-latency page table walks …

High-performance large-scale graph analytics are essential to timely analyze relationships
in big datasets. Conventional processor architectures suffer from inefficient resource usage …

Intel developed the Programmable Integrated Unified Memory Architecture (PIUMA) to
address inefficiencies seen in conventional processor architectures for at-scale sparse …

Point-to-Point Shortest Distance (PPSD) query is a crucial primitive in graph database
applications. Hub labeling algorithms compute a labeling that converts a PPSD query into a …

Many application scenarios such as social network analysis and real-time financial fraud
detection involve performing batched updates and analytics on a time-evolving or streaming …

As the memory footprint of emerging applications continues to increase, the address
translation becomes a critical performance bottleneck owing to frequent misses on the …