About: Large-Scale Visualization of Sparse Matrices     Goto   Sponge   NotDistinct   Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

AttributesValues
rdf:type
rdfs:seeAlso
Description
  • An efficient algorithm for parallel acquisition of visualization data for large sparse matrices is presented and evaluated both analytically and empirically. The algorithm was designed to be application-independent, i.e., it works with any matrix-processors mapping and with any sparse storage format/scheme. The empirical scalability study of the algorithm was carried on using multiple modern HPC systems. In our largest experiment, we utilized 262,144 processors for 73 seconds to gather and store to a file the visualization data for a matrix with 1.17x10^13 nonzero elements. Using the proposed algorithm, one can thus visualize large sparse matrices with a minimal runtime overhead imposed on executed HPC codes.
  • An efficient algorithm for parallel acquisition of visualization data for large sparse matrices is presented and evaluated both analytically and empirically. The algorithm was designed to be application-independent, i.e., it works with any matrix-processors mapping and with any sparse storage format/scheme. The empirical scalability study of the algorithm was carried on using multiple modern HPC systems. In our largest experiment, we utilized 262,144 processors for 73 seconds to gather and store to a file the visualization data for a matrix with 1.17x10^13 nonzero elements. Using the proposed algorithm, one can thus visualize large sparse matrices with a minimal runtime overhead imposed on executed HPC codes. (en)
Title
  • Large-Scale Visualization of Sparse Matrices
  • Large-Scale Visualization of Sparse Matrices (en)
skos:prefLabel
  • Large-Scale Visualization of Sparse Matrices
  • Large-Scale Visualization of Sparse Matrices (en)
skos:notation
  • RIV/68407700:21240/14:00217664!RIV15-MSM-21240___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GAP202/12/2011), S
http://linked.open...iv/cisloPeriodika
  • 1
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 25678
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21240/14:00217664
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • visualization; sparse matrices; parallel system; distributed algorithm; data acquisition (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • RO - Rumunsko
http://linked.open...ontrolniKodProRIV
  • [2A2BE87B1D0F]
http://linked.open...i/riv/nazevZdroje
  • Scalable Computing: Practice and Experience
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 15
http://linked.open...iv/tvurceVysledku
  • Dytrych, T.
  • Langr, Daniel
  • Šimeček, Ivan
  • Tvrdík, Pavel
issn
  • 1895-1767
number of pages
http://bibframe.org/vocab/doi
  • 10.12694/scpe.v15i1.963
http://localhost/t...ganizacniJednotka
  • 21240
Faceted Search & Find service v1.16.118 as of Jun 21 2024


Alternative Linked Data Documents: ODE     Content Formats:   [cxml] [csv]     RDF   [text] [turtle] [ld+json] [rdf+json] [rdf+xml]     ODATA   [atom+xml] [odata+json]     Microdata   [microdata+json] [html]    About   
This material is Open Knowledge   W3C Semantic Web Technology [RDF Data] Valid XHTML + RDFa
OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 67 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software