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

The architectural changes introduced with multi-core CPUs have triggered a redesign of
main-memory join algorithms. In the last few years, two diverging views have appeared. One …

Analytical databases are continuously adapting to the underlying hardware in order to
saturate all sources of parallelism. At the same time, hardware evolves in multiple directions …

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 …

As the amount of memory in database systems grows, entire database tables, or even
databases, are able to fit in the system's memory, making in-memory database operations …

There has been significant amount of excitement and recent work on GPU-based database
systems. Previous work has claimed that these systems can perform orders of magnitude …

Database community has made significant research efforts to optimize query processing on
GPUs in the past few years. However, we can hardly find that GPUs have been truly adopted …

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 …

Query co-processing on graphics processors (GPUs) has become an effective means to
improve the performance of main memory databases. However, the relatively low bandwidth …

Relational equi-joins are at the heart of almost every query plan. They have been studied,
improved, and reexamined on a regular basis since the existence of the database …