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A Novel Multicontext Coarse-Grained Reconfigurable Architecture (CGRA) For Accelerating Column-Oriented Databases

Published:01 May 2011Publication History
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Abstract

The storage model of column-oriented databases is similar in structure to densely packed matrices/vectors found in many high-performance computing applications. Hence, hardware-accelerated vectorized matrix operations using Reconfigurable Logic (RL) coprocessors may find parallels in hardware acceleration of databases. In this article, we explore this hypothesis by proposing a multicontext, coarse-grained Reconfigurable coprocessor Unit (RU) model that is used to accelerate some of the database operations in hardware for column-oriented databases. We then describe the implementation of hardware algorithms for the equi-join, nonequi-join, and inverse-lookup database operations. Finally, we evaluate these algorithms using a microbenchmark query. Our results indicate that the query execution on the proposed RU model is one to two orders of magnitude faster than the software-only query execution.

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References

  1. Abadi, D., Madden, S., and Hachem, N. 2008. Column-Stores vs. row-stores: How different are they really? In Proceedings of the ACM SIGMOD International Conference on Management of Data. 967--980. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Ailamaki, A., Govindaraju, N. K., Harizopoulos, S., and Manocha, D. 2006. Query co-processing on commodity processors. In Proceedings of the Very Large Data Bases Conference. VLDB Endowment, 1267--1267. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Am2045. 2009. Ambric Corporate website. http://www.ambric.com/.Google ScholarGoogle Scholar
  4. AMD. 2009. 3DNow! technology manual. http://www.amd.com/us-en/assets/content_type/white_papers_and_tech_docs/ 21928.pdf.Google ScholarGoogle Scholar
  5. Banerjee, J. and Hsiao, D. K. 1978. A methodology for supporting existing CODASYL databases with new database machines. In Proceedings of the ACM Annual Conference. ACM, New York, 925--936. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Boncz, P. A. 2002. Monet: A next-generation DBMS kernel for query-intensive applications. Ph.D. thesis, Universiteit van Amsterdam, Amsterdam, The Netherlands.Google ScholarGoogle Scholar
  7. Boral, H. and DeWitt, D. J. 1989. Database machines: An idea whose time has passed? A critique of the future of database machines. Parall. Archit. Datab. Syst., 11--28. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Cherabuddi, R. 2009. Faster, greener, cheaper: Why every MySQL database server will one day have a SQL chip. In Proceedings of the MySQL Conference and Expo.Google ScholarGoogle Scholar
  9. Codd, E. 1990. The Relational Model for Database Management: Version 2. Addison-Wesley Longman Publishing, Boston, MA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Davidson, G., Boyack, K., Zacharski, R., Helmreich, S., and Cowie, J. 2011. Data-centric computing with the netezza architecture. Sandia. rep., SAND2006-3640. Sandia National Laboratories.Google ScholarGoogle Scholar
  11. DS2000. 2009. DRC Comp. Corp. http://www.drccomputer.com/pdfs/DRC_DS2000_fall07.pdf.Google ScholarGoogle Scholar
  12. ElementCXI. 2009. ElementCXI Corporate. website. http://www.elementcxi.com/.Google ScholarGoogle Scholar
  13. Gokhale, M. and Graham, P. S. 2005. Reconfigurable Computing: Accelerating Computation with Field-Programmable Gate Arrays. Springer, New York. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. GxEmul. 2009. GxEmul Homepage. http://gavare.se/gxemul.Google ScholarGoogle Scholar
  15. He, B., Yang, K., Fang, R., Lu, M., Govindaraju, N., Luo, Q., and Sander, P. 2008. Relational joins on graphics processors. In Proceedings of the ACM SIGMOD International Conference on Management of Data. 511--524. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Kung, H. T. and Lehman, P. L. 1980. Systolic (VLSI) arrays for relational database operations. In Proceedings of the ACM SIGMOD International Conference on Management of Data. 105--116. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Lee, K. C., Hickey, T. M., Mak, V. W., and Herman, G. E. 1991. VLSI accelerators for large database systems. IEEE Micro 11, 6, 8--20. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Lipovski, G. J. 1978. Architectural features of CASSM: A context addressed segment sequential memory. In Proceedings of the International Symposium on Computer Architecture. 31--38. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Magnusson, P. S., Christensson, M., Eskilson, J., Forsgren, D., Hållberg, G., Högberg, J., Larsson, F., Moestedt, A., and Werner, B. 2002. Simics: A full system simulation platform. IEEE Comput. 35, 2, 50--58. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Merrett, T. H. 1983. Practical hardware for linear execution of relational database operations. SIGMOD Rec. 14, 1, 39--44. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Mueller, S. and Paul, W. 2000. Computer Architecture: Complexity and Correctness. Springer. Google ScholarGoogle ScholarCross RefCross Ref
  22. Shahbahrami, A., Juurlink, B., and Vassiliadis, S. 2005. Efficient vectorization of the FIR filter. In Proceedings of the Annual Workshop on Circuits, Systems and Signal Processing. 432--437.Google ScholarGoogle Scholar
  23. Sun, C., Agrawal, D., and Abbadi, A. E. 2003. Hardware acceleration for spatial selections and joins. In Proceedings of the ACM SIGMOD International Conference on Management of Data. 455--466. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Sun, D., Blough, D., and Mooney, V. 2002. Atalanta - A new multiprocessor RTOS kernel for system-on-a-chip applications. Tech. rep., GIT-CC-02-19. Georgia Institute of Technology.Google ScholarGoogle Scholar
  25. Vaidya, P. and Lee, J. 2007. Design space exploration of multiprocessor systems with multicontext reconfigurable co-processors. In Proceedings of the Engineering of Reconfigurable Systems and Algorithms Conference. 51--60.Google ScholarGoogle Scholar
  26. Vassiliadis, S. and Soudris, D. 2007. Fine and Coarse-Grain Reconfigurable Computing. Springer, New York. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. XD1000. 2009. XtremeData Inc. http://www.xtremedatainc.com/xd1000_brief.html.Google ScholarGoogle Scholar
  28. Zhou, J. and Ross, K. A. 2002. Implementing database operations using SIMD instructions. In Proceedings of the ACM SIGMOD International Conference on Management of Data. 145--156. Google ScholarGoogle ScholarDigital LibraryDigital Library

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