Parallel Data Processing

Chapter

Abstract

In the following, we discuss how to achieve parallelism in in-memory and traditional database management systems. Pipelined parallelism and data parallelism are two approaches to speed up query processing.

References

  1. [Amd67]
    G.M. Amdah, Validity of the single processor approach to achieving large scale computing capabilities. In: Proceedings of the April 18–20, 1967, Spring Joint computer Conference, AFIPS ’67 (Spring). (ACM, New York,1967), pp. 483–485 Google Scholar
  2. [DG08]
    Jeffrey Dean, Sanjay Ghemawat, Mapreduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)CrossRefGoogle Scholar
  3. [DM98]
    Leonardo Dagum, Ramesh Menon, Openmp: an industry-standard api for shared-memory programming. IEEE Comput. Sci. Eng. 5(1), 46–55 (1998)CrossRefGoogle Scholar
  4. [GLS94]
    William Gropp, Ewing Lusk, Anthony Skjellum, Using MPI: portable parallel programming with the message-passing interface (MIT Press, Cambridge, 1994)Google Scholar
  5. [Gus88]
    J.L. Gustafson, Reevaluating amdahl’s law. Commun. ACM 31(5), 532–533 (1988)CrossRefGoogle Scholar
  6. [HP11]
    J.L. Hennessy, D.A. Patterson, Computer Architecture: a quantitative approach 5th edn. (Elsevier Science, Burlington, 2011)Google Scholar
  7. [Li86]
    K. Li, Shared virtual memory on loosely coupled multiprocessors. Ph.D. thesis. (New Haven, 1986), AAI8728365 Google Scholar
  8. [Moo65]
    G. Moore, Cramming more components onto integrated circuits. Electroni. 38, p 114 ff. (1965)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Hasso Plattner InstitutePotsdamGermany

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