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Techniques avancées de génération de code pour la performance


@@ -27,7 +27,7 @@

+

Le HPC: un paysage de plus en plus complexe

Le matériel:

    @@ -51,12 +51,12 @@

    Le HPC: un paysage de plus e

Comment assurer la portabilité et la pérennité du code haute performance ?

-
+

Le millefeuille d'abstractions du HPC en C++


-
+

Concilier abstraction et performance

  • @@ -83,7 +83,7 @@

    Concilier abstraction et performan

    Pour ces raisons, on se tourne vers la métaprogrammation

-
+

La métaprogrammation pour la performance

  • @@ -105,7 +105,7 @@

    La métaprogrammation pou

    Peut-on aller plus haut en niveau d'abstraction ?
    Oui, via les Domain Specific Embedded Languages

-
+

Les Domain Specific Embedded Languages (DSELs)

  • @@ -130,7 +130,7 @@

    Les Domain Specific Embedd }

-
+

Problématiques des DSEL pour le HPC



@@ -146,7 +146,7 @@

Problématiques des DSEL pour

-
+

Problématiques des DSEL pour le HPC



@@ -165,7 +165,7 @@

Problématiques des DSEL pou

-
+

Les travaux de cette thèse


    @@ -190,7 +190,7 @@

    Les travaux de cette thèse

-
+

Génération de noyaux de calcul SIMD


    @@ -214,7 +214,7 @@

    Génération de noyaux de

    Est-il possible de générer ce code au lieu de le réimplémenter
    pour chaque architecture?

-
+

Génération de noyaux GEMV performants

  • @@ -244,12 +244,12 @@

    Génération de noyaux

-
+

Optimisation de GEMV


3 étapes en SIMD: Broadcast, produit, réduction

-
+

Implémentation générique de GEMV

template <typename T, std::size_t M, std::size_t N>
 void gemv(mat<T, M, N> &mat, vec<T, N> &vec, vec<T, N> &r) {
@@ -274,17 +274,17 @@ 
Implémentation génér
 }
-
+

Performances des noyaux GEMV


Code généré vs OpenBLAS - x86 (Intel i5-7200)

-
+

Performances des noyaux GEMV


Code généré vs OpenBLAS - ARM (ARM Cortex A57)

-
+

GEMV: conclusions

  • @@ -305,7 +305,7 @@

    GEMV: conclusions


    Quels outils pour les analyser ?

-
+

Les travaux de cette thèse



@@ -324,8 +324,8 @@

Les travaux de cette thèse

-
-

L'évaluation directe de code C++ à la compilation

+
+

L'exécution de programmes à la compilation en C++

  • Croissance du support et de l'utilisation de la métaprogrammation:

    @@ -349,7 +349,7 @@

    L'évaluation

-
+

ctbench - github.com/jpenuchot/ctbench

  • @@ -376,7 +376,7 @@

    ctbench - github.com/jpenuchot/ctbe

-
+

Cas d'usage de ctbench

  • Entiers sous forme de types
    • @@ -398,7 +398,7 @@

    Cas d'usage de ctbench

    constexpr std::size_t result = decltype(foo())::value;
-
+

Cas d'usage de ctbench

  • 1e implémentation: récursion
    • @@ -423,12 +423,12 @@

    Cas d'usage de ctbench

    }
-
+

Cas d'usage de ctbench - résultats


Comparaison du temps de compilation, récursion vs parameter pack

-
+

Conclusion sur ctbench

  • @@ -458,7 +458,7 @@

    Conclusion sur ctbench

-
+

Les travaux de cette thèse



@@ -478,7 +478,7 @@

Les travaux de cette thèse

-
+

Etat de l'art des DSEL

  • Pour le HPC: Eigen (2009), Blaze (2012), NT2 (2014)
    • @@ -499,7 +499,7 @@

    Etat de l'art des DSEL

-
+

Compile Time Regular Expressions (CTRE)

Hana Dusíková, 2018
github.com/hanickadot/compile-time-regular-expressions

@@ -522,7 +522,7 @@

Compile Time Regular Expressions

-
+

Vers des compilateurs embarqués constexpr

  • @@ -545,7 +545,7 @@

    Vers des compilateurs em

    Comment contourner cette contrainte ?

-
+

Le langage Brainfuck

@@ -599,7 +599,7 @@

Le langage Brainfuck

>>.>---.+++++++..+++.>>.<-.<.+++.------.--------.>>+.>++. -
+

Un parser Brainfuck

std::tuple<ast_block_t, token_vec_t::const_iterator>
 parse_block(token_vec_t::const_iterator parse_begin,
@@ -623,7 +623,7 @@ 
Un parser Brainfuck

 }
-
+

Un parser Brainfuck constexpr

constexpr std::tuple<ast_block_t, token_vec_t::const_iterator>
 parse_block(token_vec_t::const_iterator parse_begin,
@@ -647,7 +647,7 @@ 
Un parser Brainfuck constexpr

 }
-
+

Génération de programmes Brainfuck

  • @@ -680,7 +680,7 @@

    Génération de programmes

-
+

Temps de génération via expression templates

Temps de compilation en secondes

@@ -723,7 +723,7 @@

Temps de génér -
+

Temps de génération sans expression templates

Temps de compilation en secondes

@@ -770,7 +770,7 @@

Temps de géné

Qu'est-ce qu'il se passe sur des benchmarks de plus petite taille ?

-
+

Benchmarks synthétiques de petite taille

  • Boucles imbriquées: [[...]] (AST profond)
    • @@ -781,7 +781,7 @@

    Benchmarks synthétiques
  • La forme des expression templates influe sur le temps de compilation

-
+

Conversion de tableaux dynamiques en tableaux statiques

constexpr std::vector<int> foo() { return {0, 1, 2, 3}; }
 template <auto Value> struct my_type {};
@@ -807,7 +807,7 @@ 
Conversion de t
-
+

Temps de compilation via sérialisation

@@ -849,12 +849,12 @@

Temps de compilation via s
  • Temps de compilation linéaire
    • -
    +

    Benchmarks synthétiques de petite taille

    Impact négligeable sur les cas de petite taille

    -
    +

    Application pour le calcul numérique

    • Langage mathématique simple: Tiny Math Language
      • @@ -886,19 +886,19 @@

      Application pour le calcul nu

    -
    -

    Les performances de TML

    +
    +

    Temps de compilation de Blaze et TML


    Mesures de temps de compilation pour des séries d'additions
    (0 + x + y + ...)

    -
    -

    Les performances de TML

    +
    +

    Temps de compilation de Blaze et TML


    Mesures de temps de compilation pour des séries d'additions
    (0 + x + y + ...)

    -
    +

    Conclusions

    • @@ -920,7 +920,7 @@

      Conclusions

    -
    +

    Perspectives

    • @@ -941,7 +941,7 @@

      Perspectives

    -
    +

    Contributions et logiciels

    Publications

      @@ -1056,4 +1056,4 @@

      Logiciels

      year = {2022}, url = {https://www.youtube.com/watch?v=ekFPm7e__vI}, }

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