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Statements

Subject Item
n2:RIV%2F68407700%3A21240%2F14%3A00213823%21RIV15-MSM-21240___
rdf:type
skos:Concept n18:Vysledek
dcterms:description
Sparse matrix-vector multiplication (shortly spMV) is one of the most common subroutines in the numerical linear algebra. The parallelization of this task looks easy and straightforward, but it is not optimal in general case. This paper discuss some matrix-processor mappings and their impact on parallel spMV execution on massively parallel systems. We try to balance the performance and the overhead of the required transformation. We also present algorithms for redistribution. We propose four quality measures and derive lower and upper bound for different mappings. Our $spMV$ algorithms are scalable for almost all matrices arising from various technical areas. Sparse matrix-vector multiplication (shortly spMV) is one of the most common subroutines in the numerical linear algebra. The parallelization of this task looks easy and straightforward, but it is not optimal in general case. This paper discuss some matrix-processor mappings and their impact on parallel spMV execution on massively parallel systems. We try to balance the performance and the overhead of the required transformation. We also present algorithms for redistribution. We propose four quality measures and derive lower and upper bound for different mappings. Our $spMV$ algorithms are scalable for almost all matrices arising from various technical areas.
dcterms:title
The study of impact of matrix-processor mapping on the parallel sparse matrix-vector multiplication The study of impact of matrix-processor mapping on the parallel sparse matrix-vector multiplication
skos:prefLabel
The study of impact of matrix-processor mapping on the parallel sparse matrix-vector multiplication The study of impact of matrix-processor mapping on the parallel sparse matrix-vector multiplication
skos:notation
RIV/68407700:21240/14:00213823!RIV15-MSM-21240___
n3:aktivita
n5:I
n3:aktivity
I
n3:dodaniDat
n17:2015
n3:domaciTvurceVysledku
n14:3481131 n14:2243997
n3:druhVysledku
n16:D
n3:duvernostUdaju
n13:S
n3:entitaPredkladatele
n15:predkladatel
n3:idSjednocenehoVysledku
48284
n3:idVysledku
RIV/68407700:21240/14:00213823
n3:jazykVysledku
n21:eng
n3:klicovaSlova
parallel execution; sparse matrix-vector multiplication; sparse matrix representation; matrix-processor mapping; scalability
n3:klicoveSlovo
n6:scalability n6:parallel%20execution n6:matrix-processor%20mapping n6:sparse%20matrix-vector%20multiplication n6:sparse%20matrix%20representation
n3:kontrolniKodProRIV
[5E0EE7E30378]
n3:mistoKonaniAkce
Temešvár
n3:mistoVydani
Los Alamitos
n3:nazevZdroje
15th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing
n3:obor
n19:IN
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
3
n3:rokUplatneniVysledku
n17:2014
n3:tvurceVysledku
Šimeček, Ivan Langr, Daniel Srnec, E.
n3:typAkce
n8:WRD
n3:zahajeniAkce
2013-09-23+02:00
s:numberOfPages
8
n10:doi
10.1109/SYNASC.2013.49
n11:hasPublisher
IEEE Computer Society
n20:isbn
978-1-4799-3035-7
n12:organizacniJednotka
21240