UL HPC users'session: Mastering  big data

Reference : UL HPC users'session: Mastering big data


	Document type :	Scientific Presentations in Universities or Research Centers : Scientific presentation in universities or research centers
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/36270

Title :	UL HPC users'session: Mastering big data
Language :	English
Author, co-author :	Bisdorff, Raymond [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Publication date :	13-Jun-2018
Audience :	National
Event name :	UL HPC School 2018
Event date :	from 12-06-2018 to 13-06-2018
Event organizer :	Universtity of Luxembourg HPC team
Event place (city) :	Belval
Event country :	Luxembourg
Keywords :	[en] 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 centres :	University of Luxembourg: High Performance Computing - ULHPC
Target :	Researchers ; Students
Permalink :	http://hdl.handle.net/10993/36270

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