State-of-the-Art on Query & Transaction Processing Acceleration

TL;DR

This paper reviews the use of GPUs for accelerating query and transaction processing in database systems, highlighting key properties, challenges, and future research directions for GPU-accelerated databases.

Contribution

It provides a comprehensive overview of GPU-accelerated database architectures, identifying open challenges and research areas for future development.

Findings

GPU architectures offer significant potential for data processing acceleration.

Key challenges include data transfer bottlenecks and architecture design complexities.

Open research areas involve optimizing GPU integration in database systems.

Abstract

The vast amount of processing power and memory bandwidth provided by modern Graphics Processing Units (GPUs) make them a platform for data-intensive applications. The database community identified GPUs as effective co-processors for data processing. In the past years, there were many approaches to make use of GPUs at different levels of a database system. In this Internal Technical Report, based on the [1] and some other research papers, we identify possible research areas at LIP6 for GPU-accelerated database management systems. We describe some key properties, typical challenges of GPU-aware database architectures, and identify major open challenges.

Full text

11affiliationtext: LIP6, Campus Pierre et Marie Curie, Sorbonne Université, Paris22affiliationtext: Huawei Technologies, Paris Research Center

State-of-the-Art on Query & Transaction Processing Acceleration

Bernd Amann

Youry Khmelevsky

Gaétan Hains

(

**HUAWEI Technical Report CSI-PARIS-TR-2018-10

**2018–09–27

Huawei Technologies

2012Labs/CSI/DPSL/CSI-PARIS)