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Conference Paper
A Comprehensive Empirical Study of Query Performance Across GPU DBMSes
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- Authors
- Young-Kyoon Suh, Junyoung An, Byungchul Tak, Gap-Joo Na
- Issue Date
- 2022-06
- Citation
- International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS) 2022, pp.1-29
- Language
- English
- Type
- Conference Paper
- Abstract
- In recent years, GPU database management systems (DBMSes) have rapidly become popular largely due to their remarkable acceleration capability obtained through extreme parallelism in query evaluations. However, there has been relatively little study on the characteristics of these GPU DBMSes for a better understanding of their query performance in various contexts. Also, little has been known about what the potential factors could be that affect the query processing jobs within the GPU DBMSes. To fill this gap, we have conducted a study to identify such factors and to propose a structural causal model, including key factors and their relationships, to explicate the variances of the query execution times on the GPU DBMSes. We have also established a set of hypotheses drawn from the model that explained the performance characteristics. To test the model, we have designed and run comprehensive experiments and conducted in-depth statistical analyses on the obtained empirical data. As a result, our model achieves about 77% amount of variance explained on the query time and indicates that reducing kernel time and data transfer time are the key factors to improve the query time. Also, our results show that the studied systems should resolve several concerns such as bounded processing within GPU memory, lack of rich query evaluation operators, limited scalability, and GPU under-utilization.
- KSP Keywords
- Causal model, Data transfer time, Database management systems, GPU database, Key factors, Query Execution, Query evaluation, Query performance, Query processing, Statistical analyses, empirical data