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Statements

Subject Item
n2:RIV%2F68407700%3A21240%2F14%3A00217676%21RIV15-GA0-21240___
rdf:type
skos:Concept n23:Vysledek
rdfs:seeAlso
http://80.link.springer.com.dialog.cvut.cz/chapter/10.1007/978-3-642-55224-3_18
dcterms:description
We present a method and an accompanying algorithm for scalable parallel generation of sparse matrices intended primarily for benchmarking purposes, namely for evaluation of performance and scalability of generic massively parallel algorithms that involve sparse matrices. The proposed method is based on enlargement of small input matrices, which are supposed to be obtained from public sparse matrix collections containing numerous matrices arising in different application domains and thus having different structural and numerical properties. The resulting matrices are distributed among processors of a parallel computer system. The enlargement process is designed so its users may easily control structural and numerical properties of resulting matrices as well as the distribution of their nonzero elements to particular processors. We present a method and an accompanying algorithm for scalable parallel generation of sparse matrices intended primarily for benchmarking purposes, namely for evaluation of performance and scalability of generic massively parallel algorithms that involve sparse matrices. The proposed method is based on enlargement of small input matrices, which are supposed to be obtained from public sparse matrix collections containing numerous matrices arising in different application domains and thus having different structural and numerical properties. The resulting matrices are distributed among processors of a parallel computer system. The enlargement process is designed so its users may easily control structural and numerical properties of resulting matrices as well as the distribution of their nonzero elements to particular processors.
dcterms:title
Scalable Parallel Generation of Very Large Sparse Benchmark Matrices Scalable Parallel Generation of Very Large Sparse Benchmark Matrices
skos:prefLabel
Scalable Parallel Generation of Very Large Sparse Benchmark Matrices Scalable Parallel Generation of Very Large Sparse Benchmark Matrices
skos:notation
RIV/68407700:21240/14:00217676!RIV15-GA0-21240___
n3:aktivita
n18:P
n3:aktivity
P(GAP202/12/2011)
n3:dodaniDat
n13:2015
n3:domaciTvurceVysledku
n14:2243997 n14:9540814 n14:3481131
n3:druhVysledku
n21:D
n3:duvernostUdaju
n12:S
n3:entitaPredkladatele
n22:predkladatel
n3:idSjednocenehoVysledku
43843
n3:idVysledku
RIV/68407700:21240/14:00217676
n3:jazykVysledku
n20:eng
n3:klicovaSlova
sparse matrix; benchmark matrix; enlargement; parallel algorithm; scalability
n3:klicoveSlovo
n9:benchmark%20matrix n9:sparse%20matrix n9:scalability n9:parallel%20algorithm n9:enlargement
n3:kontrolniKodProRIV
[0570793925F5]
n3:mistoKonaniAkce
Warsaw
n3:mistoVydani
Berlin
n3:nazevZdroje
Parallel Processing and Applied Mathematics
n3:obor
n15:IN
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
4
n3:projekt
n10:GAP202%2F12%2F2011
n3:rokUplatneniVysledku
n13:2014
n3:tvurceVysledku
Dytrych, T. Tvrdík, Pavel Šimeček, Ivan Langr, Daniel
n3:typAkce
n4:WRD
n3:wos
000349159200018
n3:zahajeniAkce
2013-09-08+02:00
s:issn
0302-9743
s:numberOfPages
10
n11:doi
10.1007/978-3-642-55224-3_18
n19:hasPublisher
Springer-Verlag
n16:isbn
978-3-642-55224-3
n6:organizacniJednotka
21240