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

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 …

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 …

We present INSIDER, a full-stack redesigned storage system to help users fully utilize the
performance of emerging storage drives with moderate programming efforts. On the …

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 …

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 …

Modern cloud databases adopt a storage-disaggregation architecture that separates the
management of computation and storage. A major bottleneck in such an architecture is the …