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Description
  • Tato zpráva popisuje pravděpodobnostní modely pro chování skrytých pamětí během násobení řídké matice vektorem (pro obecné a symetrické matice) a metodu sdružených gradientů. Modely jsou odvozeny pro 3 základní typy datových cache pamětí. (cs)
  • In this paper, we describe a probabilistic model for estimation of the numbers of cache misses during the sparse matrix-vector multiplication (for both general and symmetric matrices) and the Conjugate Gradient algorithm for 3 types of data caches: direct mapped, s-way set associative with random or with LRU replacement strategies. Using HW cache monitoring tools, we compare the predicted number of cache misses with real numbers on Intel x86 architecture with L1 and L2 caches. The accuracy of our analytical model is around 96%.
  • In this paper, we describe a probabilistic model for estimation of the numbers of cache misses during the sparse matrix-vector multiplication (for both general and symmetric matrices) and the Conjugate Gradient algorithm for 3 types of data caches: direct mapped, s-way set associative with random or with LRU replacement strategies. Using HW cache monitoring tools, we compare the predicted number of cache misses with real numbers on Intel x86 architecture with L1 and L2 caches. The accuracy of our analytical model is around 96%. (en)
Title
  • Analytical Modelling of Linear Codes
  • Analytical Modelling of Linear Codes (en)
  • Analytické modely lineárních kódů (cs)
skos:prefLabel
  • Analytical Modelling of Linear Codes
  • Analytical Modelling of Linear Codes (en)
  • Analytické modely lineárních kódů (cs)
skos:notation
  • RIV/68407700:21110/06:03114521!RIV07-AV0-21110___
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  • RIV/68407700:21110/06:03114521
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  • Conjugate Gradient algorithm; cache miss; probabilistic cache model; sparse matrix-vector multiplication (en)
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  • Šimeček, Ivan
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  • 80-01-03439-9
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  • 21110
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