Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/2847263.2847295acmconferencesArticle/Chapter ViewAbstractPublication PagesfpgaConference Proceedingsconference-collections
poster

Accelerating Database Query Processing on OpenCL-based FPGAs (Abstract Only)

Published: 21 February 2016 Publication History

Abstract

The release of OpenCL support for FPGAs represents a significant improvement in extending database applications to the reconfigurable domain. Taking advantage of the programmability offered by the OpenCL HLS tool, an OpenCL database can be easily ported and re-designed for FPGAs. A single SQL query in these database systems usually consists of multiple operators, and each one of these operators in turn consists of multiple OpenCL kernels. Due to the specific properties of FPGAs, each OpenCL kernel can have different optimization combinations (in terms of CU and SIMD) which is critical to the overall performance of query processing. In this paper, we propose an efficient method to implement database operators on OpenCL-based FPGAs. We use a cost model to determine the optimum query plan for an input query. Our cost model has two components: unit cost and query plan generation. The unit cost component generates multiple (unit cost, resource utilization) pairs for each kernel. The query plan generation component employs a dynamic programming approach to generate the optimum query plan which consider the possibilities to use multiple FPGA images. The experiments show that 1) our cost model can accurately predict the performance of each feasible query plan for the input query, and is able to guide the generation of the optimum query plan, 2) our optimized query plan achieves a performance speedup 1.5X-4X over the state-of-the-art query processing on OpenCL-based FPGAs.

References

[1]
Altera SDK for OpenCL Optimization Guide. (2013). http://www.altera.com/literature/hb/opencl-sdk/aocl_optimization_guide.pdf
[2]
Stratix V Device Overview. (2014). http://www.altera.com/literature/hb/stratix-v/stx5_51001.pdf
[3]
Arcas-Abella, G. Ndu, N. Sonmez, M. Ghasempour, A. Armejach, J. Navaridas, Wei Song, J. Mawer, A. Cristal, and M. Lujan. 2014. An empirical evaluation of High-Level Synthesis languages and tools for database acceleration. In Field Programmable Logic and Applications (FPL), 2014 24th International Conference on. 1--8. http://dx.doi.org/10.1109/FPL.2014.6927484
[4]
Lew Art and Holger Mauch. Dynamic Programming: A Computational Tool. Springer.
[5]
Peter A. Boncz, Marcin Zukowski, and Niels Nes. MonetDB/X100: Hyper-Pipelining Query Execution. In CIDR 2005. http://www.cidrdb.org/cidr2005/papers/P19.pdf
[6]
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 (FPGA '14). ACM, New York, NY, USA, 151--160. ISBNx978-1-4503-2671-1 http://dx.doi.org/10.1145/2554688.2554787
[7]
D. Chen and D. Singh. 2012. Invited paper: Using OpenCL to evaluate the efficiency of CPUS, GPUS and FPGAS for information filtering. In Field Programmable Logic and Applications (FPL), 2012 22nd International Conference on. 5--12. http://dx.doi.org/10.1109/FPL.2012.6339171
[8]
T.S. Czajkowski, U. Aydonat, D. Denisenko, J. Freeman, M. Kinsner, D. Neto, J. Wong, P. Yiannacouras, and D.P. Singh. 2012. From opencl to high-performance hardware on FPGAS. In Field Programmable Logic and Applications (FPL), 2012 22nd International Conference on. 531--534. http://dx.doi.org/10.1109/FPL.2012.6339272
[9]
C. Dennl, D. Ziener, and J. Teich. 2012. On-the-fly Composition of FPGA-Based SQL Query Accelerators Using a Partially Reconfigurable Module Library. In Field-Programmable Custom Computing Machines (FCCM), 2012 IEEE 20th Annual International Symposium on. 45--52. http://dx.doi.org/10.1109/FCCM.2012.18
[10]
Bingsheng He, Mian Lu, Ke Yang, Rui Fang, Naga K. Govindaraju, Qiong Luo, and Pedro V. Sander. 2009. Relational Query Coprocessing on Graphics Processors. ACM Trans. Database Syst. 34, 4, Article 21 (Dec. 2009), 39 pages. ISSN 0362-5915 http://dx.doi.org/10.1145/1620585.1620588
[11]
Bingsheng He, Ke Yang, Rui Fang, Mian Lu, Naga Govindaraju, Qiong Luo, and Pedro Sander. 2008. Relational Joins on Graphics Processors. In Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data (SIGMOD '08). ACM, New York, NY, USA, 511--524. ISBN x978-1-60558-102-6 http://dx.doi.org/10.1145/1376616.1376670
[12]
Jiong He, Mian Lu, and Bingsheng He. 2013. Revisiting Co-processing for Hash Joins on the Coupled CPU-GPU Architecture. Proc. VLDB Endow. 6, 10 (Aug. 2013), 889--900. ISSN 2150-8097 http://dx.doi.org/10.14778/2536206.2536216
[13]
Jiong He, Shuhao Zhang, and Bingsheng He. 2014. In-cache Query Co-processing on Coupled CPU-GPU Architectures. Proc. VLDB Endow. 8, 4 (Dec. 2014), 329--340. ISSN 2150-8097 http://dx.doi.org/10.14778/2735496.2735497
[14]
Z. Istvan, G. Alonso, M. Blott, and K. Vissers. 2013. A flexible hash table design for 10GBPS key-value stores on FPGAS. In Field Programmable Logic and Applications (FPL), 2013 23rd International Conference on. 1--8. http://dx.doi.org/10.1109/FPL.2013.6645520
[15]
Zsolt Istvan, Louis Woods, and Gustavo Alonso. 2014. Histograms As a Side Effect of Data Movement for Big Data. In Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data (SIGMOD '14). ACM, New York, NY, USA, 1567--1578. ISBN x978-1-4503-2376-5 http://dx.doi.org/10.1145/2588555.2612174
[16]
Donald E. Knuth and Jayme L. Szwarcfiter. A structured program to generate all topological sorting arrangements. In Articles of IPL 1974.
[17]
Dirk Koch and Jim Torresen. 2011. FPGASort: A High Performance Sorting Architecture Exploiting Run-time Reconfiguration on Fpgas for Large Problem Sorting. In Proceedings of the 19th ACM/SIGDA International Symposium on Field Programmable Gate Arrays (FPGA '11). ACM, New York, NY, USA, 45--54. ISBN x978-1-4503-0554-9 http://dx.doi.org/10.1145/1950413.1950427
[18]
Loring Wirbel. 2014. Xilinx SDAccel A Unified Development Environment for Tomorrow's Data Center. http://www.altera.com/literature/hb/opencl-sdk/aocl_optimization_guide.pdf
[19]
Rene Mueller and Jens Teubner. 2009. FPGA: What's in It for a Database?. In Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data (SIGMOD '09). ACM, New York, NY, USA, 999--1004. ISBNx 978-1-60558-551-2 http://dx.doi.org/10.1145/1559845.1559965
[20]
Rene Mueller, Jens Teubner, and Gustavo Alonso. 2009. Data Processing on FPGAs. Proc. VLDB Endow. 2, 1 (Aug. 2009), 910--921. ISSN 2150-8097 http://dx.doi.org/10.14778/1687627.1687730
[21]
Rene Mueller, Jens Teubner, and Gustavo Alonso. 2010. Glacier: A Query-to-hardware Compiler. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data (SIGMOD '10). ACM, New York, NY, USA, 1159--1162. ISBNx978-1-4503-0032-2 http://dx.doi.org/10.1145/1807167.1807307
[22]
Mohammadreza Najafi, Mohammad Sadoghi, and Hans-Arno Jacobsen. 2013. Flexible Query Processor on FPGAs. Proc. VLDB Endow. 6, 12 (Aug. 2013), 1310--1313. ISSN2150-8097 http://dx.doi.org/10.14778/2536274.2536303
[23]
K. Shagrithaya, K. Kepa, and P. Athanas. 2013. Enabling development of OpenCL applications on FPGA platforms. In Application-Specific Systems, Architectures and Processors (ASAP), 2013 IEEE 24th International Conference on. 26--30. ISSN 2160-0511 http://dx.doi.org/10.1109/ASAP.2013.6567546
[24]
Bharat Sukhwani, Mathew Thoennes, Hong Min, Parijat Dube, Bernard Brezzo, Sameh Asaad, and Donna Dillenberger. 2015. A Hardware/Software Approach for Database Query Acceleration with FPGAs. International Journal of Parallel Programming 43, 6 (2015), 1129--1159. ISSN 0885-7458 http://dx.doi.org/10.1007/s10766-014-0327-4
[25]
Zeke Wang, Bingsheng He, and Wei Zhang. 2015. A study of data partitioning on OpenCL-based FPGAs. In Field Programmable Logic and Applications (FPL), 2015 25th International Conference on. 1--8. http://dx.doi.org/10.1109/FPL.2015.7293941
[26]
Louis Woods, Zsolt István, and Gustavo Alonso. 2014. Ibex: An Intelligent Storage Engine with Support for Advanced SQL Offloading. Proc. VLDB Endow. 7, 11 (July 2014), 963--974. ISSN 2150-8097 http://dx.doi.org/10.14778/2732967.2732972
[27]
Shuhao Zhang, Jiong He, Bingsheng He, and Mian Lu. 2013. OmniDB: Towards Portable and Efficient Query Processing on Parallel CPU/GPU Architectures. Proc. VLDB Endow. 6, 12 (Aug. 2013), 1374--1377. ISSN 2150-8097 http://dx.doi.org/10.14778/2536274.2536319

Cited By

View all
  • (2022)TCUDB: Accelerating Database with Tensor ProcessorsProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3517869(1360-1374)Online publication date: 10-Jun-2022
  • (2018)Accelerating Key In-memory Database Functionality with FPGA Technology2018 International Conference on ReConFigurable Computing and FPGAs (ReConFig)10.1109/RECONFIG.2018.8641722(1-8)Online publication date: Dec-2018
  • (2017)CaribouProceedings of the VLDB Endowment10.14778/3137628.313763210:11(1202-1213)Online publication date: 1-Aug-2017

Index Terms

  1. Accelerating Database Query Processing on OpenCL-based FPGAs (Abstract Only)

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    FPGA '16: Proceedings of the 2016 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays
    February 2016
    298 pages
    ISBN:9781450338561
    DOI:10.1145/2847263
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 21 February 2016

    Check for updates

    Author Tags

    1. fpga
    2. opencl
    3. query processing

    Qualifiers

    • Poster

    Funding Sources

    • Ministry of Education Singapore

    Conference

    FPGA'16
    Sponsor:

    Acceptance Rates

    FPGA '16 Paper Acceptance Rate 20 of 111 submissions, 18%;
    Overall Acceptance Rate 125 of 627 submissions, 20%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)0
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 12 Jan 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2022)TCUDB: Accelerating Database with Tensor ProcessorsProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3517869(1360-1374)Online publication date: 10-Jun-2022
    • (2018)Accelerating Key In-memory Database Functionality with FPGA Technology2018 International Conference on ReConFigurable Computing and FPGAs (ReConFig)10.1109/RECONFIG.2018.8641722(1-8)Online publication date: Dec-2018
    • (2017)CaribouProceedings of the VLDB Endowment10.14778/3137628.313763210:11(1202-1213)Online publication date: 1-Aug-2017

    View Options

    View options

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media