Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

A Comprehensive Overview of GPU Accelerated Databases

Harshit Sharma, Anmol Sharma

TL;DR

A survey encompassing multiple GPU database systems is proposed, with the focus on elucidating the underlying mechanisms employed to deliver results and key performance metrics, utilizing benchmarks such as SSB and TPCH.

Abstract

Over the past decade, the landscape of data analytics has seen a notable shift towards heterogeneous architectures, particularly the integration of GPUs to enhance overall performance. In the realm of in-memory analytics, which often grapples with memory bandwidth constraints, the adoption of GPUs has proven advantageous, thanks to their superior bandwidth capabilities. The parallel processing prowess of GPUs stands out, providing exceptional efficiency for data-intensive workloads and outpacing traditional CPUs in terms of data processing speed. While GPU databases capitalize on these strengths, there remains a scarcity of comparative studies across different GPU systems. In light of this emerging interest in GPU databases for data analytics, this paper proposes a survey encompassing multiple GPU database systems. The focus will be on elucidating the underlying mechanisms employed to deliver results and key performance metrics, utilizing benchmarks such as SSB and TPCH. This undertaking aims to shed light on new avenues for research within the realm of GPU databases.

A Comprehensive Overview of GPU Accelerated Databases

TL;DR

A survey encompassing multiple GPU database systems is proposed, with the focus on elucidating the underlying mechanisms employed to deliver results and key performance metrics, utilizing benchmarks such as SSB and TPCH.

Abstract

Over the past decade, the landscape of data analytics has seen a notable shift towards heterogeneous architectures, particularly the integration of GPUs to enhance overall performance. In the realm of in-memory analytics, which often grapples with memory bandwidth constraints, the adoption of GPUs has proven advantageous, thanks to their superior bandwidth capabilities. The parallel processing prowess of GPUs stands out, providing exceptional efficiency for data-intensive workloads and outpacing traditional CPUs in terms of data processing speed. While GPU databases capitalize on these strengths, there remains a scarcity of comparative studies across different GPU systems. In light of this emerging interest in GPU databases for data analytics, this paper proposes a survey encompassing multiple GPU database systems. The focus will be on elucidating the underlying mechanisms employed to deliver results and key performance metrics, utilizing benchmarks such as SSB and TPCH. This undertaking aims to shed light on new avenues for research within the realm of GPU databases.
Paper Structure (24 sections, 1 equation, 6 figures, 1 table)

This paper contains 24 sections, 1 equation, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. Previous Works
  3. Survey of Database Systems
  4. DuckDB
  5. BlazingSQL
  6. Introduction
  7. Architecture and Features
  8. Example of End-to-End Query Execution
  9. OmniSciDB
  10. Introduction
  11. Architecture
  12. Example of End-to-End Query Execution
  13. Advantages and Industry Application
  14. Crystal+
  15. Introduction
  16. ...and 9 more sections

Figures (6)

  • Figure 1: BlazingSQL RAL Architecture
  • Figure 2: OmniSciDB High-Level Architecture
  • Figure 3: How Crystal+ implements hash-based join
  • Figure 4: TQP's compilation phase
  • Figure 5: TQP's represents data in tensors
  • ...and 1 more figures