Growing main memory capacity has fueled the development of in-memory big data
management and processing. By eliminating disk I/O bottleneck, it is now possible to support …

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 …

The increase in the capacity of main memory coupled with the decrease in cost has fueled
research in and development of in-memory databases. In recent years, the emergence of …

The Single Instruction Multiple Data (SIMD) architecture, supported by various high-
performance computing platforms, efficiently utilizes data-level parallelism. The SIMD model …

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 …

Technology limitations are making the use of heterogeneous computing devices much more
than an academic curiosity. In fact, the use of such devices is widely acknowledged to be the …

Graphics Processing Units (GPUs) have evolved as a powerful query co-processor for main
memory On-Line Analytical Processing (OLAP) databases. However, existing GPU-based …

Implementing parallel operators in multi-core machines often involves a data partitioning
step that divides the data into cache-size blocks and arranges them so to allow concurrent …

Processor manufacturers build increasingly specialized processors to mitigate the effects of
the power wall in order to deliver improved performance. Currently, database engines have …

We introduce BriskStream, an in-memory data stream processing system (DSPSs)
specifically designed for modern shared-memory multicore architectures. BriskStream's key …