A Comprehensive Overview of GPU Accelerated Databases
Harshit Sharma, Anmol Sharma

TL;DR
This paper surveys GPU-accelerated databases, highlighting their performance advantages in data analytics, comparing different systems, and analyzing key benchmarks to identify future research directions.
Contribution
It provides a comprehensive overview of GPU database systems, detailing their mechanisms, performance metrics, and benchmarking results, which was lacking in prior comparative studies.
Findings
GPU databases leverage high memory bandwidth and parallel processing for faster data analytics.
Benchmark results show significant performance improvements over traditional CPU systems.
The survey identifies gaps and future research opportunities in GPU database development.
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…
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Taxonomy
TopicsDistributed and Parallel Computing Systems · Parallel Computing and Optimization Techniques · Graph Theory and Algorithms
