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UL HPC users'session: Mastering big data
Bisdorff, Raymond
2018
 

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Keywords :
HPC; Big data; multicriteria ranking
Abstract :
[en] We illustrate in this presentation an optimized HPC implementation for outranking digraphs of huge orders, up to several millions of decision alternatives. The proposed outranking digraph model is based on a quantiles equivalence class decomposition of the underlying multicriteria performance tableau. When locally ranking each of these ordered components, we may readily obtain an overall linear ranking of big sets of decision alternatives. The proposed optimization strategies tackles algorithmic refinements of the ranking algorithm, reducing the size of python data objects, typing the data for efficient cython and C compilation, efficient sharing of static data via global python variables, using a multiprocessing task queue, and, last but not least, use the efficient UL HPC equipements.
Research center :
ULHPC - University of Luxembourg: High Performance Computing
Disciplines :
Computer science
Author, co-author :
Bisdorff, Raymond ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
Language :
English
Title :
UL HPC users'session: Mastering big data
Publication date :
13 June 2018
Event name :
UL HPC School 2018
Event organizer :
Universtity of Luxembourg HPC team
Event place :
Belval, Luxembourg
Event date :
from 12-06-2018 to 13-06-2018
Focus Area :
Computational Sciences
Available on ORBilu :
since 02 August 2018

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