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 …

Data-intensive queries are common in business intelligence, data warehousing and
analytics applications. Typically, processing a query involves full inspection of large in …

Constructing energy-efficient database systems to reduce economic costs and
environmental impact has been studied for 10 years. With the emergence of the big data …

Neural personalized recommendation models are used across a wide variety of datacenter
applications including search, social media, and entertainment. State-of-the-art models …

Read mapping is a fundamental step in many genomics applications. It is used to identify
potential matches and differences between fragments (called reads) of a sequenced …

The cost of data movement in big-data systems motivates careful examination of near-data
processing (NDP) frameworks. The concept of NDP was actively researched in the 1990s …

Complex data queries, because of their need for random accesses, have proven to be slow
unless all the data can be accommodated in DRAM. There are many domains, such as …

We explore the potential of making programmability a central feature of the SSD interface.
Our prototype system, called Willow, allows programmers to augment and extend the …

Modern data center solid state drives (SSDs) integrate multiple general-purpose embedded
cores to manage flash translation layer, garbage collection, wear-leveling, and etc., to …

Modern data appliances face severe bandwidth bottlenecks when moving vast amounts of
data from storage to the query processing nodes. A possible solution to mitigate these …