Due to their high computational power and internal memory bandwidth, graphic processing
units (GPUs) have been extensively studied by the database systems research community …

Specialized on-chip accelerators are widely used to improve the energy efficiency of
computing systems. Recent advances in memory technology have enabled near-data …

GPUs have long been discussed as accelerators for database query processing because of
their high processing power and memory bandwidth. However, two main challenges limit the …

The popularity of large-scale real-time analytics applications (real-time inventory/pricing,
recommendations from mobile apps, fraud detection, risk analysis, IoT, etc.) keeps rising …

GPU-accelerated database systems have been studied for more than 10 years, ranging from
prototyping development to industry products serving in multiple domains of data …

Data-intensive applications fueled the evolution of log structured merge (LSM) based key-
value engines that employ the out-of-place paradigm to support high ingestion rates with low …

Traditionally, analytical database engines have used task parallelism provided by modern
multisocket multicore CPUs for scaling query execution. Over the past few years, GPUs have …

Modern server hardware is increasingly heterogeneous as hardware accelerators, such as
GPUs, are used together with multicore CPUs to meet the computational demands of …

Modern Hybrid Transactional/Analytical Processing (HTAP) systems use an integrated data
processing engine that performs analytics on fresh data, which are ingested from a …

An exponential growth in data volume, combined with increasing demand for real-time
analysis (ie, using the most recent data), has resulted in the emergence of database systems …