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HPC Ranking of Big Performance Tableaux

In: Algorithmic Decision Making with Python Resources

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  • Raymond Bisdorff

Abstract

The sparse outranking digraph, introduced in Chap. 9 , is suitable for tackling the ranking of big multiple-criteria performance tableaux with thousands or millions of records. To effectively compute rankings from performance tableaux of these sizes, we propose in this chapter a collection of C-compiled and optimised Digraph3 modules that may be run on HPC equipment as available, for instance, at the University of Luxembourg.

Suggested Citation

  • Raymond Bisdorff, 2022. "HPC Ranking of Big Performance Tableaux," International Series in Operations Research & Management Science, in: Algorithmic Decision Making with Python Resources, chapter 0, pages 137-147, Springer.
    • Handle: RePEc:spr:isochp:978-3-030-90928-4_11
    DOI: 10.1007/978-3-030-90928-4_11
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