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 …

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 …

Cross-silo federated learning (FL) adopts various cryptographic operations to preserve data
privacy, which introduces significant performance overhead. In this paper, we identify nine …

Energy efficient architecture is essential to improve both the performance and power
consumption of a computer system. However, modern computers suffer from the severe …

Large-scale sorting is always an important yet demanding task for data center applications.
In addition to powerful processing capability, high-performance sorting system requires …

Data movement and communication have become the primary bottlenecks in large multicore
systems. The near-data computing paradigm provides a solution: move computation to …

Today's big data query engines are constantly under pressure to keep up with the rapidly
increasing demand for faster processing of more complex workloads. In the past few years …

Database analytic query workloads are heavy consumers of data-center cycles, and there is
constant demand to improve their performance. Associative processors (AP) have re …

This paper presents, a log analytics platform with near-storage accelerators for high-
performance, cost-and power-efficient unstructured log processing. offloads log analytics …