For many applications, the data sets to be processed grow much faster than can be handled with the traditionally available algorithms. We therefore have to come up with new, dramatically more scalable approaches. In order to do that, we have to bring together know-how from the application, from traditional algorithm theory, and on low level aspects like parallelism, memory hierarchies, energy efficiency, and fault tolerance. The methodology of algorithm engineering with its emphasis on realistic models and its cycle of design, analysis, implementation, and experimental evaluation can serve as a glue between these requirements. This paper outlines the general challenges and gives examples from my work like sorting, full text indexing, graph algorithms, and database engines.


Fault Tolerance Memory Hierarchy Parallel Disk Disk Array Array Construction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Peter Sanders
    • 1
  1. 1.Karlsruhe Institute of TechnologyGermany

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