When evaluating an iterative graph query over a large graph, systems incur significant
overheads due to repeated graph transfer across the memory hierarchy coupled with …

Graph Processing is an emerging workload for applications working with unstructured data,
such as social network analysis, transportation networks, bioinformatics and operations …

To mitigate the data movement bottleneck on large multicore systems, the near-data
computing paradigm (NDC) offloads computation to where the data resides on-chip. The …

Autonomous robotic systems, like autonomous vehicles and robotic search and rescue,
require efficient on-device training for continuous adaptation of Deep Reinforcement …

Future intelligent robots are expected to process multiple inputs simultaneously (such as
image and audio data) and generate multiple outputs accordingly (such as gender and …

Evolving graphs consisting of slices are large and constantly changing. For example, in
Alipay, the graph generates hundreds of millions of new transaction records every day …

Many real-world networks are characterized by being temporal and dynamic, wherein the
temporal information signifies the changes in connections, such as the addition or removal …

Temporal graphs are widely used for time-critical applications, which enable the extraction
of graph structural information with temporal features but cannot be efficiently supported by …

Graph algorithms are challenging to implement due to their varying topology and irregular
access patterns. Real-world graphs are dynamic in nature and routinely undergo edge and …

A fundamental building block in any graph algorithm is a graph container-a data structure
used to represent the graph. Ideally, a graph container enables efficient access to the …