research-article

FPGAs: a new point in the database design space

Authors:

Jens TeubnerAuthors Info & Claims

EDBT '10: Proceedings of the 13th International Conference on Extending Database Technology

Pages 721 - 723

https://doi.org/10.1145/1739041.1739137

Published: 22 March 2010 Publication History

Abstract

In line with the insight that "one size" of databases will not fit all application needs [19] the database community is currently exploring various alternatives to commodity, CPU-based system designs. One particular candidate in this trend are field-programmable gate arrays (FPGAs), programmable chips that allow tailor-made hardware designs optimized for specific systems, applications, or even user queries.

With a focus on database use, this tutorial introduces into FPGA technology, demonstrates its potential, but also pinpoints some challenges that need to be addressed before FPGA-accelerated database systems can go mainstream. The goal of this tutorial is to develop an intuition of an FPGA development cycle, receive guidelines for a "good" FPGA design, but also learn the limitations that hardware-implemented database processing faces. Our more high-level ambition is to spur a broader interest in database processing on novel hardware technology.

References

[1]

Zachary K. Baker and Viktor K. Prasanna. Efficient Hardware Data Mining with the Apriori Algorithm on FPGAs. In Proc. of the 13th Symposium on Field-Programmable Custom Computing Machines (FCCM), Napa, CA, USA, April 2005.

Digital Library

[2]

Nagender Bandi, Ahmed Metwally, Divyakant Agrawal, and Amr El Abbadi. Data Stream Algorithms using Associative Memories. In Proc. of the ACM SIGMOD Int'l Conference on Management of Data, Beijing, China, June 2007.

Digital Library

[3]

Nagender Bandi, Chengyu Sun, Divyakant Agrawal, and Amr El Abbadi. Processing Spacial Data Using Graphics Processors. In Proc. of the Int'l Conference on Very Large Databases (VLDB), Toronto, ON, Canada, 2004.

[4]

Jatin Chhugani, Anthony D. Nguyen, Victor W. Lee, William Macy, Mostafa Hagog, Yen-Kuang Chen, Akram Baransi, Sanjeev Kumar, and Pradeep Dubey. Efficient Implementation of Sorting on Multi-Core SIMD CPU architecture. Proc. of the VLDB Endowment, 1(2), 2008.

Digital Library

[5]

Netezza Corp. TwinFin#8482;. http://www.netezza.com/.

[6]

Jeffrey Dean and Sanjay Ghemawat. MapReduce: Simplified Data Processing on Large Clusters. In 6th Symposium on Operating System Design and Implementation, San Francisco, CA, USA, December 2004.

Digital Library

[7]

Buğra Gedik, Rajesh R. Bordawekar, and Philip S. Yu. CellSort: High Performance Sorting on the Cell Processor. In Int'l Conference on Very Large Databases (VLDB), Vienna, Austria, September 2007.

Digital Library

[8]

Buğra Gedik, Philip S. Yu, and Rajesh Bordawekar. Executing Stream Joins on the Cell Processor. In Proc. of the Int'l Conference on Very Large Databases (VLDB), Vienna, Austria, 2007.

Digital Library

[9]

Brian T. Gold, Anastassia Ailamaki, Larry Huston, and Babak Falsafi. Accelerating Database Operations Using a Network Processor. In Workshop on Data Management on New Hardware (DaMoN), Baltimore, MD, USA, June 2005.

Digital Library

[10]

Naga Govindaraju, Jim Gray, Ritesh Kumar, and Dinesh Manocha. GPUTeraSort: High Performance Graphics Co-processor Sorting for Large Database Management. In Proc. of the ACM SIGMOD Int'l Conference on Management of Data, Chicago, IL, USA, June 2006.

Digital Library

[11]

Naga K. Govindaraju, Brandon Lloyd, Wei Wang, Ming Lin, and Dinesh Manocha. Fast Computation of Database Operations using Graphics Processors. In Proc. of the ACM SIGMOD Int'l Conference on Management of Data, Paris, France, June 2004.

Digital Library

[12]

Bingsheng He, Ke Yang, Rui Fang, Mian Lu, Naga K. Govindaraju, Qiong Luo, and Pedro V. Sander. Relational Joins on Graphics Processors. In Proc. of the ACM SIGMOD Int'l Conference on Management of Data, Vancouver, BC, Canada, June 2008.

Digital Library

[13]

XtremeData Inc. dbX. http://www.xtremedatainc.com/.

[14]

Kickfire. Analytic Appliance. http://www.kickfire.com.

[15]

H. T. Kung and Charles E. Leiserson. Systolic Arrays (for VLSI). In Sparse Matrix Proceedings, Knoxville, TN, USA, November 1978.

[16]

MonetDB. http://monetdb.cwi.nl/.

[17]

Rene Mueller, Jens Teubner, and Gustavo Alonso. Data Processing on FPGAs. Proc. of the VLDB Endowment, 2(1), August 2009.

Digital Library

[18]

Rene Mueller, Jens Teubner, and Gustavo Alonso. Streams on Wires---A Query Compiler for FPGAs. Proc. of the VLDB Endowment, 2(1), August 2009.

Digital Library

[19]

Michael Stonebraker and Uğur Çetintemel. "One Size Fits All": An Idea Whose Time Has Come and Gone. In Proc. of the 21st Int'l Conference on Data Engineering (ICDE), Tokyo, Japan, April 2005.

Digital Library

[20]

Jens Teubner, Rene Mueller, and Gustavo Alonso. FPGA Acceleration for the Frequent Item Problem. In Proc. of the 26th Int'l Conference on Data Engineering (ICDE), Long Beach, CA, USA, March 2010.

[21]

Dina Thomas, Rajesh Bordawekar, Charu C. Aggarwal, and Philip S. Yu. On Efficient Query Processing of Stream Counts on the Cell Processor. In Proc. of the 25th Int'l Conference on Data Engineering (ICDE), Shanghai, China, March 2009.

Digital Library

[22]

Thomas Willhalm, Nicolae Popovici, Yazan Boshmaf, Hasso Plattner, Alexander Zeier, and Jan Schaffner. SIMD-Scan: Ultra Fast in-Memory Table Scan using on-Chip Vector Processing Units. Proc. of the VLDB Endowment, 2(1), August 2009.

Digital Library

Cited By

Habich DPietrzyk JBarcelo PSanchez-Pi NMeliou ASudarshan S(2024)SIMDified Data Processing - Foundations, Abstraction, and Advanced TechniquesCompanion of the 2024 International Conference on Management of Data10.1145/3626246.3654694(613-621)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3626246.3654694
Guo BYu JYang DLeng HLiao B(2022)Energy-Efficient Database Systems: A Systematic SurveyACM Computing Surveys10.1145/353822555:6(1-53)Online publication date: 7-Dec-2022
https://dl.acm.org/doi/10.1145/3538225
Fang JMulder YHidders JLee JHofstee H(2019)In-memory database acceleration on FPGAs: a surveyThe VLDB Journal10.1007/s00778-019-00581-wOnline publication date: 26-Oct-2019
https://doi.org/10.1007/s00778-019-00581-w
Show More Cited By

Index Terms

FPGAs: a new point in the database design space
1. Hardware
  1. Very large scale integration design
    1. VLSI system specification and constraints
2. Information systems
  1. Data management systems
    1. Database management system engines

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Information

Published In

cover image ACM Other conferences

EDBT '10: Proceedings of the 13th International Conference on Extending Database Technology

March 2010

741 pages

ISBN:9781605589459

DOI:10.1145/1739041

Editors:
Ioana Manolescu
INRIA, France
,
Stefano Spaccapietra
EPFL, Switzerland
,
Jens Teubner
ETH Zurich, Switzerland
,
Masaru Kitsuregawa
Tokyo University, Japan
,
Alain Leger
Orange - France Telecom R&D, France
,
Felix Naumann
Hasso Plattner Institute, Germany
,
Anastasia Ailamaki
EPFL, Switzerland
,
Fatma Ozcan
IBM Almaden Research Center

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 March 2010

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Swiss National Science Foundation

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EDBT/ICDT '10

EDBT/ICDT '10: EDBT/ICDT '10 joint conference

March 22 - 26, 2010

Lausanne, Switzerland

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Overall Acceptance Rate 7 of 10 submissions, 70%

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Cited By

Habich DPietrzyk JBarcelo PSanchez-Pi NMeliou ASudarshan S(2024)SIMDified Data Processing - Foundations, Abstraction, and Advanced TechniquesCompanion of the 2024 International Conference on Management of Data10.1145/3626246.3654694(613-621)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3626246.3654694
Guo BYu JYang DLeng HLiao B(2022)Energy-Efficient Database Systems: A Systematic SurveyACM Computing Surveys10.1145/353822555:6(1-53)Online publication date: 7-Dec-2022
https://dl.acm.org/doi/10.1145/3538225
Fang JMulder YHidders JLee JHofstee H(2019)In-memory database acceleration on FPGAs: a surveyThe VLDB Journal10.1007/s00778-019-00581-wOnline publication date: 26-Oct-2019
https://doi.org/10.1007/s00778-019-00581-w
Heinrich DWerner SBlochwitz CPionteck TGroppe S(2018)Hardware-aided update acceleration in a hybrid Semantic Web database systemThe Journal of Supercomputing10.1007/s11227-018-2462-yOnline publication date: 20-Jun-2018
https://doi.org/10.1007/s11227-018-2462-y
Agrawal SIdicula SRaghavan AVlachos EGovindaraju VVaradarajan VBalkesen CGiannikis GRoth CAgarwal NSedlar EHunter HMoreno JEmer JSanchez D(2017)A many-core architecture for in-memory data processingProceedings of the 50th Annual IEEE/ACM International Symposium on Microarchitecture10.1145/3123939.3123985(245-258)Online publication date: 14-Oct-2017
https://dl.acm.org/doi/10.1145/3123939.3123985
Ritter DDann JMay NRinderle-Ma S(2017)Hardware Accelerated Application Integration ProcessingProceedings of the 11th ACM International Conference on Distributed and Event-based Systems10.1145/3093742.3093911(215-226)Online publication date: 8-Jun-2017
https://dl.acm.org/doi/10.1145/3093742.3093911
Dursun KBinnig CCetintemel UPetrocelli R(2017)SiliconDBProceedings of the 13th International Workshop on Data Management on New Hardware10.1145/3076113.3076124(1-4)Online publication date: 14-May-2017
https://dl.acm.org/doi/10.1145/3076113.3076124
Heinrich DWerner SBlochwitz CPionteck TGroppe S(2017)Search & Update Optimization of a B $$^+$$ Tree in a Hardware Aided Semantic Web Database SystemProceedings of the 7th International Conference on Emerging Databases10.1007/978-981-10-6520-0_18(172-182)Online publication date: 14-Oct-2017
https://doi.org/10.1007/978-981-10-6520-0_18
Nguyen XNguyen HPham C(2016)An FPGA approach for fast bitmap indexingIEICE Electronics Express10.1587/elex.13.2016000613:4(20160006-20160006)Online publication date: 2016
https://doi.org/10.1587/elex.13.20160006
Vaidya PLee JPai VLee MHur S(2016)Symbiote Coprocessor Unit—A Streaming Coprocessor for Data Stream AccelerationIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2015.243206324:3(813-826)Online publication date: 1-Mar-2016
https://dl.acm.org/doi/10.1109/TVLSI.2015.2432063
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