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Parallel Data Processing

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A Course in In-Memory Data Management

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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.

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Author information

Authors and Affiliations

  1. Hasso Plattner Institute, August-Bebel-Str.88, 14482, Potsdam, Brandenburg, Germany

    Hasso Plattner

Authors
  1. Hasso Plattner
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Correspondence to Hasso Plattner .

Self Test Questions

Self Test Questions

 

  1. 1.

    Shared Memory

    What limits the use of shared memory?

    1. (a)

      The number of workers, which share the same resources and the limited memory itself

    2. (b)

      The caches of each CPU

    3. (c)

      The operation frequency of the processor

    4. (d)

      The usage of SSE instructions.

 

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Plattner, H. (2013). Parallel Data Processing. In: A Course in In-Memory Data Management. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36524-9_17

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