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 …

The performance of large-scale graph processing suffers from challenges including poor
locality, lack of scalability, random access pattern, and heavy data conflicts. Some …

Aiming at the current problems that most physical hosts in the cloud data center are so
overloaded that it makes the whole cloud data center'load imbalanced and that existing load …

A critical step of genome sequence analysis is the mapping of sequenced DNA fragments
(ie, reads) collected from an individual to a known linear reference genome sequence (ie …

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 …

Non-relational database systems (NRDS) such as graph and key-value have gained
attention in various trending business and analytical application domains. However, while …

In this paper, we propose a novel design for large-scale graph processing on FPGA. Our
design uses large external memory for storing massive graph data and FPGA for …

Cloud providers widely deploy FPGAs as application-specific accelerators for customer use.
These providers seek to multiplex their FPGAs among customers via virtualization, thereby …

Hardware accelerators for graph analytics have gained increasing interest. Vertex-centric
and edge-centric paradigms are widely used to design graph analytics accelerators …