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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F06%3A00116279%21RIV12-MSM-21230___
rdf:type
n5:Vysledek skos:Concept
rdfs:seeAlso
http://shimi.webzdarma.cz/vyzkum/workshop06/colek_workshop06b.doc
dcterms:description
It is really difficult to predict the cache behavior even for a simple program because every modern CPU use a complex memory hierarchy, which consists of levels of cache memories. One challenging task is to predict the exact number of cache misses during the sparse matrix-vector multiplication. Due to matrix sparsity, the memory access patterns are irregular and the utilization of a cache suffers from low spatial and temporal locality. It is really difficult to predict the cache behavior for all cases of input parameters. The cache misses data were also analyzed by means of data mining methods. This is the main topic of this paper and we will discuss the data mining analysis bellow in the more detailed form. It is really difficult to predict the cache behavior even for a simple program because every modern CPU use a complex memory hierarchy, which consists of levels of cache memories. One challenging task is to predict the exact number of cache misses during the sparse matrix-vector multiplication. Due to matrix sparsity, the memory access patterns are irregular and the utilization of a cache suffers from low spatial and temporal locality. It is really difficult to predict the cache behavior for all cases of input parameters. The cache misses data were also analyzed by means of data mining methods. This is the main topic of this paper and we will discuss the data mining analysis bellow in the more detailed form.
dcterms:title
Cache Misses Analysis by Means of Data Mining Methods Cache Misses Analysis by Means of Data Mining Methods
skos:prefLabel
Cache Misses Analysis by Means of Data Mining Methods Cache Misses Analysis by Means of Data Mining Methods
skos:notation
RIV/68407700:21230/06:00116279!RIV12-MSM-21230___
n3:aktivita
n4:Z n4:P
n3:aktivity
P(IBS3086102), Z(MSM6840770012), Z(MSM6840770014)
n3:dodaniDat
n16:2012
n3:domaciTvurceVysledku
n13:3481131 n13:1266500
n3:druhVysledku
n12:D
n3:duvernostUdaju
n19:S
n3:entitaPredkladatele
n10:predkladatel
n3:idSjednocenehoVysledku
467601
n3:idVysledku
RIV/68407700:21230/06:00116279
n3:jazykVysledku
n8:eng
n3:klicovaSlova
bayes classifiers; cache model; data mining; decision trees; neural networks
n3:klicoveSlovo
n14:decision%20trees n14:data%20mining n14:bayes%20classifiers n14:neural%20networks n14:cache%20model
n3:kontrolniKodProRIV
[C94AD330C621]
n3:mistoKonaniAkce
Praha
n3:mistoVydani
Praha
n3:nazevZdroje
Proceedings of Workshop 2006
n3:obor
n23:IN
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:projekt
n17:IBS3086102
n3:rokUplatneniVysledku
n16:2006
n3:tvurceVysledku
Kordík, Pavel Šimeček, Ivan
n3:typAkce
n11:EUR
n3:zahajeniAkce
2006-02-20+01:00
n3:zamer
n22:MSM6840770012 n22:MSM6840770014
s:numberOfPages
2
n20:hasPublisher
České vysoké učení technické v Praze
n18:isbn
80-01-03439-9
n9:organizacniJednotka
21230