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In-Time Transaction Accelerator Architecture for RDBMS

  • Su Jin Kim
  • Seong Mo Lee
  • Ji Hoon Jang
  • Yeong Seob Jeong
  • Sang Don Kim
  • Seung Eun LeeEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 260)

Abstract

In this paper, we propose a hardware architecture for in-time transaction accelerator that reduces the bottlenecks between the DB server and the DB storage in margin FX trading system’s RDBMS (Relational Database Management System). In-time transaction accelerator located between the DB server and the DB storage analyzes and processes the queries used for margin FX trading system by co-processing of the CPU and the FPGA. The accelerator analyzes the patterns and the consistency of the queries to reduce the total database access in order to increase the RDBMS’s throughput.

Keywords

In-time transaction accelerator Margin FX trading RDBMS FPGA 

Notes

Acknowledgments

This study was supported in part by the IT R&D program of MKE/KEIT [10043896, Development of virtual memory system on multi-server and application software to provide real-time processing of exponential transaction and high availability service] and the Seoul National University of Science and Technology.

References

  1. 1.
  2. 2.
  3. 3.
    Nie C (2012) An FPGA-based smart database storage engine. Master’s thesis, ETH zurichGoogle Scholar
  4. 4.
    Guha R, Al-Dabass D (2010) Performance prediction of parallel computation of streaming applications on FPGA platform. In: 12th international conference on computer modeling and simulation, UKSim, Cambridge pp 579–585Google Scholar
  5. 5.
    Mueller R, Teubner JM, Alonso G (2009) Data processing on FPGAs. J Proc VLDB Endowment, pp 910–921Google Scholar
  6. 6.
    Sukhwani B, Min H, Thoennes M, Dube P, Iyer B, Brezzo B, Dillenberger D, Asaad S (2012) Database analytics acceleration using FPGAs. In: Proceedings of the 21st international conference on parallel architectures and compilation techniques, ACM New York pp 411–420Google Scholar
  7. 7.
    Mueller R, Teubner J, Alonso G (2010) Glacier: a query-to-hardware compiler. In: Proceedings of the 2010 ACM SIGMOD international conference on management of data, ACM New York pp 1159–1162Google Scholar
  8. 8.
    Francisco P (2011) The Netezza data appliance architecture: a platform for high performance data warehousing and analytics. Technical Report, IBMGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Su Jin Kim
    • 1
  • Seong Mo Lee
    • 1
  • Ji Hoon Jang
    • 1
  • Yeong Seob Jeong
    • 1
  • Sang Don Kim
    • 1
  • Seung Eun Lee
    • 1
    Email author
  1. 1.Department of Electronic and Information EngineeringSeoul National University of Science and TechnologyNowon-gu, Seoul-siKorea

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