Graph is a well known data structure to represent the associated relationships in a variety of
applications, eg, data science and machine learning. Despite a wealth of existing efforts on …

Simple graph algorithms such as PageRank have been the target of numerous hardware
accelerators. Yet, there also exist much more complex graph mining algorithms for problems …

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 …

Read mapping is a fundamental step in many genomics applications. It is used to identify
potential matches and differences between fragments (called reads) of a sequenced …

There has been significant recent interest in parallel graph processing due to the need to
quickly analyze the large graphs available today. Many graph codes have been designed …

The conventional approach of moving data to the CPU for computation has become a
significant performance bottleneck for emerging scale-out data-intensive applications due to …

Graph neural networks (GNNs) can extract features by learning both the representation of
each objects (ie, graph nodes) and the relationship across different objects (ie, the edges …

The von Neumann bottleneck is imposed due to the explosion of data transfers and
emerging data-intensive applications in heterogeneous system architectures. The …

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 …

Recent advancements in deep learning techniques facilitate intelligent-query support in
diverse applications, such as content-based image retrieval and audio texturing. Unlike …