Graph pattern mining (GPM) is a class of algorithms widely used in many real-world
applications in bio-medicine, e-commerce, security, social sciences, etc. GPM is a …

Graph analytics is an important workload that achieves suboptimal performance due to poor
cache locality. State-of-the-art cache replacement policies fail to capture the highly dynamic …

Federated learning (FL) enables multiple devices to collaboratively train a shared model
while preserving data privacy. However, despite its emerging applications in many areas …

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 …

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

Many solutions have been recently proposed to support the processing of streaming graphs.
However, for the processing of each graph snapshot of a streaming graph, the new states of …

Streaming graph has been broadly employed across various application domains. It
involves updating edges to the graph and then performing analytics on the updated graph …

Recently, Graph Neural Networks (GNNs) have been receiving a spotlight as a powerful tool
that can effectively serve various inference tasks on graph structured data. As the size of real …

Irregular applications, such as graph analytics and sparse linear algebra, exhibit frequent
indirect, data-dependent accesses to single or short sequences of elements that cause high …

Graph analytics, which mainly includes graph processing, graph mining, and graph learning,
has become increasingly important in several domains, including social network analysis …