While FPGAs have seen prior use in database systems, in recent years interest in using
FPGA to accelerate databases has declined in both industry and academia for the following …

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 …

Ongoing climate change calls for fast and accurate weather and climate modeling. However,
when solving large-scale weather prediction simulations, state-of-the-art CPU and GPU …

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 …

In modern data management systems, directly performing operations on compressed data
has been proven to be a big success facing big data problems. These systems have …

Stencil computations are commonly used in a wide variety of scientific applications, ranging
from large-scale weather prediction to solving partial differential equations. Stencil …

As the size of data generated every day grows dramatically, the computational bottleneck of
computer systems has been shifted toward the storage devices. Thanks to recent …

Graph analytics play a key role in a number of applications such as social networks, drug
discovery, and recommendation systems. Given the large size of graphs that may exceed …

Analytic workloads on terabyte data-sets are often run in the cloud, where application and
storage servers are separate and connected via network. In order to saturate the storage …