Reference : UL HPC users'session: Mastering big data
Scientific Presentations in Universities or Research Centers : Scientific presentation in universities or research centers
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/36270
UL HPC users'session: Mastering big data
English
Bisdorff, Raymond mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
13-Jun-2018
National
UL HPC School 2018
from 12-06-2018 to 13-06-2018
Universtity of Luxembourg HPC team
Belval
Luxembourg
[en] HPC ; Big data ; multicriteria ranking
[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.
Researchers ; Students
http://hdl.handle.net/10993/36270

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Open access
hpcLux18-2x2.pdfPublisher postprint360.04 kBView/Open

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.