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

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 …

In the presence of exponential growth of the data produced every day in volume, velocity,
and variety, online analytical processing (OLAP) is becoming increasingly challenging …

The huge demand for computation in artificial intelligence (AI) is driving unparalleled
investments in hardware and software systems for AI. This leads to an explosion in the …

Query processing on GPU-style coprocessors is severely limited by the movement of data.
With teraflops of compute throughput in one device, even high-bandwidth memory cannot …

Since its invention, data-centric code generation has been adopted for query compilation by
various database systems in academia and industry. These database systems are fast but …

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

Stream Processing Engines (SPEs) execute long-running queries on unbounded data
streams. They follow an interpretation-based processing model and do not perform runtime …

The emergence of novel hardware accelerators has powered the tremendous growth of
machine learning in recent years. These accelerators deliver incomparable performance …

Database management systems are facing growing data volumes. Previous research
suggests that GPUs are well-equipped to quickly process joins and similar stateful …