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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F05%3A03107566%21RIV06-MSM-21230___
rdf:type
skos:Concept n10:Vysledek
dcterms:description
Every mathematical algorithm for the numerical algebra can be relatively easily rewrited into pseudocode: every mathematic operation in the algorithm is simply transformed into one subroutine from the linear algebra package. This simple algorithm leads to very transparent, error-free codes. But these codes do not respect the inner architecture of the CPU and the memory hierarchy and suffer from low temporal and spatial locality. These drawbacks can be reduced by the applications of SW transformation techniques. These transformations result in better temporal and spatial locality or in more efficient utilization of inner pipelines. In this paper, we will demonstrate idea of SW transformation on 2 codes of iterative solvers. We will also represent the new code restructuring transformation called dynamic loop reversal. This transformation offers a new way to improve the cache utilization. Every mathematical algorithm for the numerical algebra can be relatively easily rewrited into pseudocode: every mathematic operation in the algorithm is simply transformed into one subroutine from the linear algebra package. This simple algorithm leads to very transparent, error-free codes. But these codes do not respect the inner architecture of the CPU and the memory hierarchy and suffer from low temporal and spatial locality. These drawbacks can be reduced by the applications of SW transformation techniques. These transformations result in better temporal and spatial locality or in more efficient utilization of inner pipelines. In this paper, we will demonstrate idea of SW transformation on 2 codes of iterative solvers. We will also represent the new code restructuring transformation called dynamic loop reversal. This transformation offers a new way to improve the cache utilization. Každý řádně definovaný matematický algoritmus může být relativně snadno implementován v nějakém vyšším prg. jazyce za pomoci knihoven pro LA. Ale takto vzniklé kódy nerespektují vnitřní architekturu CPU a mají nízkou lokalitu dat. Tento nedostatek se dá zmenšit pomocí SW transfromací kódu. V tomto článku demonstrujeme tuto myšlenku na kódech dvou nepřímých řešičů. Také ilustrujeme možnosti nově vyvinuté SW transformace zvané dynamická změna smyslu cyklu (dynamic loop reversal) pro zlepšení využití skryté paměti.
dcterms:title
Architecture Dependent Linear Code Optimizations Architecture Dependent Linear Code Optimizations Optimalizace lineárních kódů v závislosti na architektuře
skos:prefLabel
Architecture Dependent Linear Code Optimizations Architecture Dependent Linear Code Optimizations Optimalizace lineárních kódů v závislosti na architektuře
skos:notation
RIV/68407700:21230/05:03107566!RIV06-MSM-21230___
n5:aktivita
n9:Z n9:P
n5:aktivity
P(IBS3086102), Z(MSM6840770014)
n5:dodaniDat
n16:2006
n5:domaciTvurceVysledku
n12:3481131 n12:9540814
n5:druhVysledku
n13:A
n5:duvernostUdaju
n19:S
n5:entitaPredkladatele
n6:predkladatel
n5:idSjednocenehoVysledku
513013
n5:idVysledku
RIV/68407700:21230/05:03107566
n5:jazykVysledku
n15:eng
n5:klicovaSlova
Cholesky factorization; Conjugate Gradient algorithm; High-performance; cache hierarchy; dynamic loop reversal; processor architecture
n5:klicoveSlovo
n7:cache%20hierarchy n7:dynamic%20loop%20reversal n7:processor%20architecture n7:High-performance n7:Conjugate%20Gradient%20algorithm n7:Cholesky%20factorization
n5:kodPristupu
n8:L
n5:kontrolniKodProRIV
[691290ADC249]
n5:mistoVydani
Praha
n5:nosic
neuvedeno
n5:obor
n14:IN
n5:pocetDomacichTvurcuVysledku
2
n5:pocetTvurcuVysledku
2
n5:projekt
n17:IBS3086102
n5:rokUplatneniVysledku
n16:2005
n5:tvurceVysledku
Tvrdík, Pavel Šimeček, Ivan
n5:zamer
n20:MSM6840770014
n11:isbn
80-01-03201-9
n18:organizacniJednotka
21230