Tiered memory, combining multiple memory components with different performance and
capacity, provides a cost-effective solution to increase memory capacity and improve …

In recent years, many CPU-GPU heterogeneous graph processing systems have been
developed in both academic and industrial to facilitate large-scale graph processing in …

With the development of loT and edge computing, data analytics on edge has become
popular, and text analytics directly on compression (TADOC) has been proven to be a …

Directly performing operations on compressed data has been proven to be a big success
facing Big Data problems in modern data management systems. These systems have …

Recent studies have shown the promise of directly processing compressed graphs.
However, its benefits have been limited by high peak-memory usage and unbearably long …

Evolving graphs consisting of slices are large and constantly changing. For example, in
Alipay, the graph generates hundreds of millions of new transaction records every day …

Embedding columns are important for deep recommendation models to achieve high
accuracy, but they can be very time-consuming during inference. Machine learning (ML) …

Lossless data compression is an effective way to handle the huge transmission and storage
overhead of massive text data. Its utility is even more significant today when data volumes …

A fundamental building block in any graph algorithm is a graph container-a data structure
used to represent the graph. Ideally, a graph container enables efficient access to the …

Graphics Processing Units (GPUs) leverage massive parallelism and large memory
bandwidth to support high-performance computing applications, such as multimedia …