This is not the latest version of this item. The latest version can be found here.
A Study of the Fundamental Performance Characteristics of GPUs and CPUs for Database Analytics
Name
2003.01178.pdf
Description
Submitted version
Size
1.01 MB
Format
Adobe PDF
Checksum (MD5)
54d1b4b0bc7b1524b9f654d9781c3766
Author(s)
Shanbhag, Anil
Madden, Samuel
Yu, Xiangyao
Date Issued
2020
Journal
Proceedings of the ACM SIGMOD International Conference on Management of Data
Publisher
ACM
Citation
Shanbhag, Anil, Madden, Samuel and Yu, Xiangyao. 2020. "A Study of the Fundamental Performance Characteristics of GPUs and CPUs for Database Analytics." Proceedings of the ACM SIGMOD International Conference on Management of Data.
Version
Original manuscript
Abstract
© 2020 Association for Computing Machinery. 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 better than CPU-based database systems on analytical workloads such as those found in decision support and business intelligence applications. A hardware expert would view these claims with suspicion. Given the general notion that database operators are memory-bandwidth bound, one would expect the maximum gain to be roughly equal to the ratio of the memory bandwidth of GPU to that of CPU. In this paper, we adopt a model-based approach to understand when and why the performance gains of running queries on GPUs vs on CPUs vary from the bandwidth ratio (which is roughly 16× on modern hardware). We propose Crystal, a library of parallel routines that can be combined together to run full SQL queries on a GPU with minimal materialization overhead. We implement individual query operators to show that while the speedups for selection, projection, and sorts are near the bandwidth ratio, joins achieve less speedup due to differences in hardware capabilities. Interestingly, we show on a popular analytical workload that full query performance gain from running on GPU exceeds the bandwidth ratio despite individual operators having speedup less than bandwidth ratio, as a result of limitations of vectorizing chained operators on CPUs, resulting in a 25× speedup for GPUs over CPUs on the benchmark.
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
10.1145/3318464.3380595
