KPU-SQL: Kernel Processing Unit for High-Performance SQL Acceleration
Authors: 
Hao Kong, Haishuang Fan, Jingya Wu, Liyun Cheng, Yan Chen, Wenyan Lu, Guihai Yan, 
Xiaowei LiAuthors Info & Claims
Pages 37 - 43
Abstract
Application-specific accelerator is a prominent way for analytic query processing. To achieve a substantial improvement over the state-of-the-art in performance while maintaining programmability, we propose a kernel processing unit (KPU) framework and apply it to SQL acceleration. Kernel customization and data transmission are two critical bottlenecks, we separately optimize them in the key core and shadow core with a self-designed data management system. A software stack named RACE with a performance model and function simulator is also introduced. The experiments demonstrate that KPU-SQL outperforms the CPU and GPU by 24.5x and 8.75x on average, respectively.
References
[1]
Cagri Balkesen, Nitin Kunal, Georgios Giannikis, Pit Fender, Seema Sundara, Felix Schmidt, Jarod Wen, Sandeep Agrawal, Arun Raghavan, Venkatanathan Varadarajan, Anand Viswanathan, Balakrishnan Chandrasekaran, Sam Idicula, Nipun Agarwal, and Eric Sedlar. 2018. RAPID: In-Memory Analytical Query Processing Engine with Extreme Performance per Watt. In Proceedings of the 2018 International Conference on Management of Data (Houston, TX, USA) (SIGMOD '18). Association for Computing Machinery, New York, NY, USA, 1407--1419. https://doi.org/10.1145/3183713.3190655
[2]
Peter Boncz, Thomas Neumann, and Orri Erling. 2014. TPC-H Analyzed: Hidden Messages and Lessons Learned from an Influential Benchmark. In Performance Characterization and Benchmarking, Raghunath Nambiar and Meikel Poess (Eds.). Springer International Publishing, Cham, 61--76.
[3]
Jared Casper and Kunle Olukotun. 2014. Hardware Acceleration of Database Operations. In Proceedings of the 2014 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (Monterey, California, USA) (FPGA '14). Association for Computing Machinery, New York, NY, USA, 151--160. https://doi.org/10.1145/2554688.2554787
[4]
Markus Dreseler, Martin Boissier, Tilmann Rabl, and Matthias Uflacker. 2020. Quantifying TPC-H Choke Points and Their Optimizations. Proc. VLDB Endow., Vol. 13, 8 (apr 2020), 1206--1220. https://doi.org/10.14778/3389133.3389138
[5]
Zubeyr F Eryilmaz, Aarati Kakaraparthy, Jignesh M Patel, Rathijit Sen, and Kwanghyun Park. 2021. FPGA for aggregate processing: The good, the bad, and the ugly. In 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 1044--1055.
[6]
HeteroDB 2018. PG-Strom. Retrieved April 1, 2023 from https://github.com/ heterodb/pg-strom
[7]
Wenyan Lu, Yan Chen, Jingya Wu, Yu Zhang, Xiaowei Li, and Guihai Yan. 2022. DOE: Database Offloading Engine for Accelerating SQL Processing. In 2022 IEEE 38th International Conference on Data Engineering Workshops (ICDEW). IEEE, 129--134.
[8]
Philippos Papaphilippou, Holger Pirk, and Wayne Luk. 2019. Accelerating the merge phase of sort-merge join. In 2019 29th International Conference on Field Programmable Logic and Applications (FPL). IEEE, 100--105.
[9]
Angshuman Parashar, Michael Pellauer, Michael Adler, Bushra Ahsan, Neal Crago, Daniel Lustig, Vladimir Pavlov, Antonia Zhai, Mohit Gambhir, Aamer Jaleel, Randy Allmon, Rachid Rayess, Stephen Maresh, and Joel Emer. 2013. Triggered Instructions: A Control Paradigm for Spatially-Programmed Architectures. In Proceedings of the 40th Annual International Symposium on Computer Architecture (Tel-Aviv, Israel) (ISCA '13). Association for Computing Machinery, New York, NY, USA, 142--153. https://doi.org/10.1145/2485922.2485935
[10]
Makoto Saitoh, Elsayed A Elsayed, Thiem Van Chu, Susumu Mashimo, and Kenji Kise. 2018. A high-performance and cost-effective hardware merge sorter without feedback datapath. In 2018 IEEE 26th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM). IEEE, 197--204.
[11]
Nikola Samardzic, Weikang Qiao, Vaibhav Aggarwal, Mau-Chung Frank Chang, and Jason Cong. 2020. Bonsai: High-performance adaptive merge tree sorting. In 2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture (ISCA). IEEE, 282--294.
[12]
Utku Sirin and Anastasia Ailamaki. 2020. Micro-Architectural Analysis of OLAP: Limitations and Opportunities. Proc. VLDB Endow., Vol. 13, 6 (feb 2020), 840--853. https://doi.org/10.14778/3380750.3380755
[13]
Lisa Wu, Andrea Lottarini, Timothy K. Paine, Martha A. Kim, and Kenneth A. Ross. 2014. Q100: The Architecture and Design of a Database Processing Unit. In Proceedings of the 19th International Conference on Architectural Support for Programming Languages and Operating Systems (Salt Lake City, Utah, USA) (ASPLOS '14). Association for Computing Machinery, New York, NY, USA, 255--268. https://doi.org/10.1145/2541940.2541961
[14]
Shuotao Xu, Thomas Bourgeat, Tianhao Huang, Hojun Kim, Sungjin Lee, and Arvind Arvind. 2020. Aquoman: An analytic-query offloading machine. In 2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO). IEEE, 386--399.
Index Terms
- KPU-SQL: Kernel Processing Unit for High-Performance SQL Acceleration
Recommendations
Software pipelining for graphic processing unit acceleration
The graphic processing unit GPU is becoming increasingly popular as a performance accelerator in various applications requiring high-performance parallel computing capability. In a central processing unit CPU or GPU hybrid system, software pipelining is ...
DOE: database offloading engine for accelerating SQL processing
AbstractThe CPU-Accelerator heterogeneous systems have demonstrated performance and efficiency benefits on DBMSs. However, the CPU-Cache-DRAM architecture can not fully utilize the bandwidth of DRAMs such that in-memory approach get limited improvement. ...
Acceleration of Monte-Carlo simulation on high performance computing platforms
RACS '18: Proceedings of the 2018 Conference on Research in Adaptive and Convergent SystemsMonte Carlo methods are often used to solve computational problems with randomness. The random sampling helps avoid the deterministic results, but it requires intensive computations to obtain the results. Several attempts have been made to boost the ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
June 2023
731 pages
ISBN:9798400701252
DOI:10.1145/3583781
- General Chairs:
- Himanshu Thapliyal,
- Ronald DeMara,
- Program Chairs:
- Inna Partin-Vaisband,
- Srinivas Katkoori
Copyright © 2023 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 05 June 2023
Check for updates
Author Tags
Qualifiers
- Research-article
Funding Sources
- National Natural Science Foundation of China (NSFC)
- The Strategic Priority Research Program of the Chinese Academy of Sciences
- Youth Innovation Promotion Association CAS
Conference
GLSVLSI '23
Sponsor:
Acceptance Rates
Overall Acceptance Rate 312 of 1,156 submissions, 27%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 377Total Downloads
- Downloads (Last 12 months)270
- Downloads (Last 6 weeks)48
Reflects downloads up to 26 Nov 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in