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 …

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 …

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 …

There has been a growing interest in using GPU to accelerate data analytics due to its
massive parallelism and high memory bandwidth. The main constraint of using GPU for data …

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

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 dual in-line memory modules (DIMMs) support processing-in-memory (PIM) by
implementing in-DIMM processors (IDPs) located near memory banks. PIM can greatly …

GPUs are increasingly used for high-performance and interactive data analytics workloads
due to their capability to accelerate computation using massive parallelism. A key constraint …

GPUs offer massive parallelism and high-bandwidth memory access, making them an
attractive option for accelerating data analytics in database systems. However, while modern …