Abstract
As a distributed, fault-tolerant data warehouse system for large-scale data analytics, Apache Hive has been used for various applications in many organizations (e.g., Facebook, Amazon, and Huawei). Exploiting the large degrees of parallelism of GPU to improve the performance of online analytical processing (OLAP) in database system is a common practice in the industry. Meanwhile, it is a common practice to exploit the large degrees of parallelism of GPU to improve the performance of online analytical processing (OLAP) in database systems. This demo presents GHive, which enables Apache Hive to accelerate OLAP queries by jointly utilizing CPU and GPU in intelligent and efficient ways. The takeaways for SIGMOD attendees include: (1) the superior performance of GHive compared with vanilla Hive that only uses CPU; (2) intuitive visualizations of execution statistics for Hive and GHive to understand where the acceleration of GHive comes from; (3) detailed profiling of the time taken by each operator on CPU and GPU to show the advantages of GPU execution.
Originalsprog | Engelsk |
---|---|
Titel | SIGMOD 2022 - Proceedings of the 2022 International Conference on Management of Data |
Antal sider | 4 |
Forlag | Association for Computing Machinery |
Publikationsdato | 10 jun. 2022 |
Sider | 2417-2420 |
ISBN (Elektronisk) | 9781450392495 |
DOI | |
Status | Udgivet - 10 jun. 2022 |
Begivenhed | 2022 ACM SIGMOD International Conference on the Management of Data, SIGMOD 2022 - Virtual, Online, USA Varighed: 12 jun. 2022 → 17 jun. 2022 |
Konference
Konference | 2022 ACM SIGMOD International Conference on the Management of Data, SIGMOD 2022 |
---|---|
Land/Område | USA |
By | Virtual, Online |
Periode | 12/06/2022 → 17/06/2022 |
Sponsor | ACM SIGMOD |
Navn | Proceedings of the ACM SIGMOD International Conference on Management of Data |
---|---|
ISSN | 0730-8078 |
Bibliografisk note
Publisher Copyright:© 2022 ACM.