From c9389c5838efd5d6872ab508eaa000e3a769ed18 Mon Sep 17 00:00:00 2001 From: RCE Date: Sat, 9 Jan 2016 22:26:19 +0100 Subject: [PATCH] RCE : Update du 9/1/2016 --- Grid architecture.PNG | Bin 0 -> 114152 bytes Grid5000 sites.PNG | Bin 0 -> 183234 bytes MPI.pdf | Bin 0 -> 133123 bytes Performance Analysis techniques.pdf | Bin 0 -> 34767 bytes Simgrid - In a nutshell.pdf | Bin 0 -> 27103 bytes These_RCE.aux | 178 +++++------ These_RCE.dvi | Bin 0 -> 253884 bytes These_RCE.lof | 31 +- These_RCE.log | 439 ++++++++++++++++------------ These_RCE.lot | 3 +- These_RCE.out | 72 ++--- These_RCE.pdf | Bin 5050903 -> 0 bytes These_RCE.synctex.gz | Bin 345208 -> 402082 bytes These_RCE.tex | 198 ++++++++++++- These_RCE.toc | 94 +++--- 15 files changed, 637 insertions(+), 378 deletions(-) create mode 100644 Grid architecture.PNG create mode 100644 Grid5000 sites.PNG create mode 100644 MPI.pdf create mode 100644 Performance Analysis techniques.pdf create mode 100644 Simgrid - In a nutshell.pdf create mode 100644 These_RCE.dvi delete mode 100644 These_RCE.pdf diff --git a/Grid architecture.PNG b/Grid architecture.PNG new file mode 100644 index 0000000000000000000000000000000000000000..1077bc61da7b25058bbd52da8ada1e438013e850 GIT binary patch literal 114152 zcmeEtWmgWLeSu@!QG{C4HgJ4jW@vq0UCD=-ncsicXxMp*M{k5X5KY_ zVm{REs#WV&t?F~n*}Kl(6{e~zgNa6r1_uX+DJLtb4hM$-fbAclBE!Z%NR@)HA9xpa z8F9GsG15cW!8-8jFTa&ih0sm1^hm#)gpQqa;e zo<@O|UAHqmG-+8df&B#0N z0srs9^S^|0hW__8{~Hd7qv|Nl*4|D!AB0b>w^m5iq^$ryBK_TtjTWK2Sw zmTNYB{3ztB)qfZxvyKSwTTfnOdK|Kh)j$6wZmg2^ry0_-AGzm^T{KC}w-bnUm-M)C zd``?Y2OsIb8$|5RGqe6t#|FRb<^9lyk#z%Y)?@KjC@fsq;BuX!rz-_?SN+a^1gyWv zTK=*8cEO}NH;~tj=3PweWa}~z0Si5WP!DQu5!r5M-&5q;|M(>hmH(dlM_KPh;~A;H z?4-oXm{S54Z3OiQenOtc7PxVAXB?0OH9=lMlT5X|{+GH+wW;tlpL9KO`^1Q?PlHT0 zF*jS5a-o75n^KWBdRVS;DHPXbc84)BYA(VQdl>>fSO1o&!63&Q%pgWrECN%y4L}hG zaC#(dz3#KYSU*BeWN8BWJQqHe=~GQcgbcYag(TAw7Ai{_NusWZBAME65N7g{m-NT? zzK4fersyIwa6MoAA~A%o4$x$)fbfKYo-uZ1XaEAYcf|!wDm>()@r!A=7O!J9)qAQT zr9#mxU+)WtuL7;Bw@esUtgF>e4^2X*G5sEzJbU3g)*%vy9DXv@KFu_0JfXX?F5X$` z9slmuz}|M(b>w_Vw*$Ep9}KHh-Eb1nJm%2uc9ZryZEutUD-u+?7pVB&m%~2_(pV0S zcruriEy!CAIVrK8#K>riV|~Jp)1xD`=$^xQNE`dN(2Ny#h~z$`b5$5=@I z2TM3Iq6daKJeMex!hYrIUit~q}7b9t5=<*RRAX>LO56JF9)O( zHIQh4U70v_r?FIkA6J9%&wbuNtD&6w+yqs3ZZ1Sfw4F2Aic_Vrh7Y!a5OYfii=82f zK#cvmYqpHv-6xuw15Lo+yq?CY8h)uF&A9z#7LMh|x6I_e4w6F6!StvAf`7RLV!Y_535gT|?~1EG6Jd+>hx^B!b~UKOF22TO7Fx8~nT6p0B*6HcaFs$GpC zK{Nnam;PT{u6Pfm!hS|d%F9IUMdFhe2c8$f3JVnD7oM`pm}S1W>8i8}cZG6Jc4-)q z5f4F(9qOB=Yq=D(hL7TIe=L$;b4=NUboXm>+<*C%{pM#E95qIwrotj(WNdb8<5ZnZ=Gr6%SbByWnJVToA&e!{F<#&zdWERUfHU| zDV-q@A1m6?t{;?vW$;rfp)QD0379?oqA4`w7Bo$R zbyu}!uh1_-ZF-5$%O|f~vqijO`iq3=n4JCn{$~m)o65~*`~uJ_c0QTJbGhKXI6BXX zXb;1h#GsMz-8J}K02jDjq}gNhZ|yzCKxdKoqA!Q>)mObu?^CwHsJt_ShSK*#@gD~h zrXsL@Kc;zY#K_D?DL&gIx<4dnpS`xgfcU+U_=5%TANDK zi2%8T#GHtJp~;z!c>BH#(i;3J1t z9vsbS#*nq(Kv-m@%#v(HVpx9$R_9^h=Qud__yA1?Kwc)A$THIAaC#_8_HnzF53SJKMwKho)73TWg_? z;TXa8v{f#(1rHFpZ%dCFqcC^^4N$33xli}wicudhY3RbIp)4^u$|Ov8c<1Wc;*;B` z>wbQV?TA9Tp62OpV+QP3t{^zugfuDOor`UF6%;?n>;t zM*rfTKHPhK5Gny%7ew{q_+;tpyP=dld^@?aa6lfj%Rar z(rteBU%)tY@nx_1_0_-!w^~RIBpSrf=TLZ;|rkj)Lwou|Dmba=_629L5;4v=(CTpw^AQ5E+)b+^JjS62FJFWnnx4St@(uvG*=5OO=tPJo6Pe5 zwkrsDqr|K!di#)NJLY}OuN2b~{~E4X;U*1v`GsMwu>FV>EBJEU0|U-s92p2-ojWB& z_1jWBrNVvwU|78Tdwz}2o$oguIQWURS!A}qy4vKmwo4#~O23d{9=}UTZS%6J|MvM# z;peC<{`i&k;faPHFL0Hx9_XHH3oZ2)a_*;E4uQ&FJH!P?{T_k3PW9=u0Z6hJWd%j7Fq@uGw)8MH&52J;lvh%UbOizhrgO{~KHMkL zUfmyr%4ydX$k!1RI#FxzK^)FvMydEYEG*)&md_*#t0|VAGA|GoNz_p73RK7E-YXP| z%1i~z_sIvzhrhIk^96g3}=h@;`oM=D0Kg@@n$H*j6&jlNy0-mYS z&G$n19!cf4|NhxA!E={RpiD>5YQmeZ8~#PDY28+>*ol_e$Av*4z=aBE zKNQKdJSBkieH5}?x_l@Z=yTaW9$Ozhd+>kt-@&Q@rtN!}Z~Fj*U|6-8a*Rj0#!ob! z2_h}tLXKz>jJ23t(}dAX+DqNrA892tq^F#`Od0aUQ@X(;TbA@h63t)db~+Cwt#bHR zy!vudKYcV7FCvcKmhEu7C*uF)q_y>NOx9UF7EL)UEG#xY{he|mr`4D&?hC`gmoHz6 z(wq+ZJ!+@NmwXR#?6()#QqOMuf$qdV8(t&!+oS51Jhd*0PW|}X03K#>^N8*_2oXt7M z=lNsk(xNPeV-9c`$`9k!&;Y6P+K93;5Hwn5IiSd4Sfb8C8amoYqrNPf6ndS-Xi$(6M-BW0{;6Or1 zoS1&~$%IWwd`wezw8>~6&kYo{7l45k7|Jrhwq$prF-EiY{sf(o|oA<(TpYYKEp^>Um?vxKqC{vQAA0PclZVV0k zVPLbsXB0h_NySg7%cyW)a6F*tgsGBjpFrwpzsr9b?L3tBr2+zQmyTSD(YCoP)nHWD z^?4%E->aDR8NMJ`H=cpS>D;%2o_il7u;K!?sm)%q$D_|O z5kFpc@rC#zNlL{CgHyTK`*4Q1ts|^MioZPP8_90sZXOyU2<-AXkk)n$K~k2zGLd

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Evolution de la puissance de calcul mondiale}}{27}{figure.2.6} -\contentsline {figure}{\numberline {2.7}{\ignorespaces M\IeC {\'e}moire MIMD: Architecture UMA}}{28}{figure.2.7} -\contentsline {figure}{\numberline {2.8}{\ignorespaces M\IeC {\'e}moire MIMD: Architecture NUMA}}{29}{figure.2.8} -\contentsline {figure}{\numberline {2.9}{\ignorespaces M\IeC {\'e}moire MIMD: Architecture COMA}}{30}{figure.2.9} -\contentsline {figure}{\numberline {2.10}{\ignorespaces Classification des techniques d'analyse de la performance}}{33}{figure.2.10} +\contentsline {figure}{\numberline {1.5}{\ignorespaces Algorithme it\IeC {\'e}ratif de Jacobi}}{11}{figure.1.5} +\contentsline {figure}{\numberline {1.6}{\ignorespaces Architecture d'une grille de calcul}}{12}{figure.1.6} +\contentsline {figure}{\numberline {1.7}{\ignorespaces Grid'5000 : R\IeC {\'e}partition g\IeC {\'e}ographique}}{13}{figure.1.7} +\contentsline {figure}{\numberline {1.8}{\ignorespaces Groupes et communicateur (a) - MPI - Op\IeC {\'e}rations collectives (b)}}{16}{figure.1.8} +\contentsline {figure}{\numberline {1.9}{\ignorespaces SIMGRID : Les \IeC {\'e}l\IeC {\'e}ments de la plateforme de simulation}}{18}{figure.1.9} +\addvspace {10\p@ } +\contentsline {figure}{\numberline {2.1}{\ignorespaces Weak vs Strong scaling: Temps d'ex\IeC {\'e}cution et Speedup}}{24}{figure.2.1} +\contentsline {figure}{\numberline {2.2}{\ignorespaces Architecture des CPU multicoeurs}}{27}{figure.2.2} +\contentsline {figure}{\numberline {2.3}{\ignorespaces Mod\IeC {\`e}le MIMD Distribu\IeC {\'e}}}{28}{figure.2.3} +\contentsline {figure}{\numberline {2.4}{\ignorespaces Mod\IeC {\`e}le MIMD partag\IeC {\'e}}}{29}{figure.2.4} +\contentsline {figure}{\numberline {2.5}{\ignorespaces Mod\IeC {\`e}le MIMD hybride}}{30}{figure.2.5} +\contentsline {figure}{\numberline {2.6}{\ignorespaces Evolution de la puissance de calcul mondiale}}{31}{figure.2.6} +\contentsline {figure}{\numberline {2.7}{\ignorespaces M\IeC {\'e}moire MIMD: Architecture UMA}}{32}{figure.2.7} +\contentsline {figure}{\numberline {2.8}{\ignorespaces M\IeC {\'e}moire MIMD: Architecture NUMA}}{33}{figure.2.8} +\contentsline {figure}{\numberline {2.9}{\ignorespaces M\IeC {\'e}moire MIMD: Architecture COMA}}{34}{figure.2.9} +\contentsline {figure}{\numberline {2.10}{\ignorespaces Classification des techniques d'analyse de la performance}}{37}{figure.2.10} +\addvspace {10\p@ } \addvspace {10\p@ } \addvspace {10\p@ } \addvspace {10\p@ } diff --git a/These_RCE.log b/These_RCE.log index 9eed2cc..0e4191b 100644 --- a/These_RCE.log +++ b/These_RCE.log @@ -1,4 +1,4 @@ -This is pdfTeX, Version 3.14159265-2.6-1.40.16 (MiKTeX 2.9) (preloaded format=pdflatex 2015.11.9) 12 DEC 2015 10:00 +This is pdfTeX, Version 3.14159265-2.6-1.40.16 (MiKTeX 2.9) (preloaded format=pdflatex 2015.11.9) 8 JAN 2016 11:30 entering extended mode **These_RCE.tex (These_RCE.tex @@ -988,54 +988,75 @@ Now handling font encoding PD1 ... defining Unicode char U+2122 (decimal 8482) defining Unicode 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Un-leash your HPC per- for-mance with [] -Underfull \hbox (badness 10000) in paragraph at lines 1448--1450 +Underfull \hbox (badness 10000) in paragraph at lines 1628--1630 \OT1/phv/m/n/10.95 Bull. \OT1/phv/m/sl/10.95 Maxi-mi-zing com-pu-ting per-for-m ance while re-du-cing po-wer consump-tion\OT1/phv/m/n/10.95 . [] -Underfull \hbox (badness 1286) in paragraph at lines 1453--1454 +Underfull \hbox (badness 1286) in paragraph at lines 1633--1634 []\OT1/phv/m/n/10.95 [15] C. Har-ris et al. HPC Tech-no-logy Up-date. \OT1/phv/ m/sl/10.95 Paw-set Su-per-com-pu-ting Cen-ter - [] -Underfull \hbox (badness 10000) in paragraph at lines 1455--1457 +Underfull \hbox (badness 10000) in paragraph at lines 1635--1637 []\OT1/phv/m/n/10.95 [16] A. J. van der Steen, J. J. Don-garra. Over-view of Re cent Su-per- [] -Underfull \hbox (badness 10000) in paragraph at lines 1455--1457 +Underfull \hbox (badness 10000) in paragraph at lines 1635--1637 \OT1/phv/m/n/10.95 com-pu-ters. \OT1/phv/m/sl/10.95 Aca-de-mic Com-pu-ting Cent re Utrecht, the Ne-ther-lands, De-part- [] -Underfull \hbox (badness 10000) in paragraph at lines 1455--1457 +Underfull \hbox (badness 10000) in paragraph at lines 1635--1637 \OT1/phv/m/sl/10.95 ment of Com-pu-ter Science, Uni-ver-sity of Ten-nes-see, Kn ox-ville, Ma-the-ma-ti- [] -Underfull \hbox (badness 2460) in paragraph at lines 1455--1457 +Underfull \hbox (badness 2460) in paragraph at lines 1635--1637 \OT1/phv/m/sl/10.95 cal Sciences Sec-tion, Oak Ridge, Na-tio-nal La-bo-ra-tory, Oak Ridge\OT1/phv/m/n/10.95 . http ://ci-te- [] -Underfull \hbox (badness 3078) in paragraph at lines 1464--1466 +Underfull \hbox (badness 3078) in paragraph at lines 1644--1646 []\OT1/phv/m/n/10.95 [19] M. Ewan. 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\BOOKMARK [2][]{subsection.1.3.2}{1.3.2 G\351n\351ralit\351s sur la simulation}{section.1.3}% 12 \BOOKMARK [2][]{subsection.1.3.3}{1.3.3 MPI - Message Passing Interface}{section.1.3}% 13 \BOOKMARK [2][]{subsection.1.3.4}{1.3.4 Simulateur SIMGRID - SMPI}{section.1.3}% 14 -\BOOKMARK [1][]{section.1.4}{1.4 Motivations}{chapter.1}% 15 -\BOOKMARK [1][]{section.1.5}{1.5 Conclusion partielle}{chapter.1}% 16 -\BOOKMARK [0][]{chapter.2}{2 Etat de l'art et travaux de recherche associ\351s}{part.1}% 17 -\BOOKMARK [1][]{section.2.1}{2.1 Concepts et d\351finitions}{chapter.2}% 18 -\BOOKMARK [2][]{subsection.2.1.1}{2.1.1 Performance de l'application parall\350le et scalabilit\351}{section.2.1}% 19 -\BOOKMARK [2][]{subsection.2.1.2}{2.1.2 Taux d'erreur lors de la pr\351diction}{section.2.1}% 20 -\BOOKMARK [2][]{subsection.2.1.3}{2.1.3 Weak contre strong scaling}{section.2.1}% 21 -\BOOKMARK [1][]{section.2.2}{2.2 Probl\351matique sur la pr\351diction \340 large \351chelle de la performance des applications}{chapter.2}% 22 -\BOOKMARK [2][]{subsection.2.2.1}{2.2.1 Facteurs li\351s \340 l'\351cosyst\350me}{section.2.2}% 23 -\BOOKMARK [3][]{subsubsection.2.2.1.1}{2.2.1.1 Facteur architecture des processeurs}{subsection.2.2.1}% 24 -\BOOKMARK [3][]{subsubsection.2.2.1.2}{2.2.1.2 Facteur : M\351moire et stockage}{subsection.2.2.1}% 25 -\BOOKMARK [3][]{subsubsection.2.2.1.3}{2.2.1.3 Facteur : R\351seaux de communication}{subsection.2.2.1}% 26 -\BOOKMARK [2][]{subsection.2.2.2}{2.2.2 Facteurs li\351s au code de l'application}{section.2.2}% 27 -\BOOKMARK [3][]{subsubsection.2.2.2.1}{2.2.2.1 Facteur : Taille du probl\350me}{subsection.2.2.2}% 28 -\BOOKMARK [3][]{subsubsection.2.2.2.2}{2.2.2.2 Performance de la parall\351lisation}{subsection.2.2.2}% 29 -\BOOKMARK [1][]{section.2.3}{2.3 Techniques d'analyse de performance des applications parall\350les}{chapter.2}% 30 -\BOOKMARK [2][]{subsection.2.3.1}{2.3.1 G\351n\351ralit\351s et objectifs}{section.2.3}% 31 -\BOOKMARK [2][]{subsection.2.3.2}{2.3.2 Approches et m\351thodologie}{section.2.3}% 32 -\BOOKMARK [2][]{subsection.2.3.3}{2.3.3 Quelques outils d'analyse de performance}{section.2.3}% 33 -\BOOKMARK [1][]{section.2.4}{2.4 M\351thodes de pr\351diction de la performance des applications parall\350les}{chapter.2}% 34 -\BOOKMARK [1][]{section.2.5}{2.5 Conclusion partielle}{chapter.2}% 35 +\BOOKMARK [1][]{section.1.4}{1.4 Conclusion partielle}{chapter.1}% 15 +\BOOKMARK [0][]{chapter.2}{2 Etat de l'art et travaux de recherche associ\351s}{part.1}% 16 +\BOOKMARK [1][]{section.2.1}{2.1 Concepts et d\351finitions}{chapter.2}% 17 +\BOOKMARK [2][]{subsection.2.1.1}{2.1.1 Performance de l'application parall\350le et scalabilit\351}{section.2.1}% 18 +\BOOKMARK [2][]{subsection.2.1.2}{2.1.2 Taux d'erreur lors de la pr\351diction}{section.2.1}% 19 +\BOOKMARK [2][]{subsection.2.1.3}{2.1.3 Weak contre strong scaling}{section.2.1}% 20 +\BOOKMARK [1][]{section.2.2}{2.2 Probl\351matique sur la pr\351diction \340 large \351chelle de la performance des applications}{chapter.2}% 21 +\BOOKMARK [2][]{subsection.2.2.1}{2.2.1 Facteurs li\351s \340 l'\351cosyst\350me}{section.2.2}% 22 +\BOOKMARK [3][]{subsubsection.2.2.1.1}{2.2.1.1 Facteur architecture des processeurs}{subsection.2.2.1}% 23 +\BOOKMARK [3][]{subsubsection.2.2.1.2}{2.2.1.2 Facteur : M\351moire et stockage}{subsection.2.2.1}% 24 +\BOOKMARK [3][]{subsubsection.2.2.1.3}{2.2.1.3 Facteur : R\351seaux de communication}{subsection.2.2.1}% 25 +\BOOKMARK [2][]{subsection.2.2.2}{2.2.2 Facteurs li\351s au code de l'application}{section.2.2}% 26 +\BOOKMARK [3][]{subsubsection.2.2.2.1}{2.2.2.1 Facteur : Taille du probl\350me}{subsection.2.2.2}% 27 +\BOOKMARK [3][]{subsubsection.2.2.2.2}{2.2.2.2 Performance de la parall\351lisation}{subsection.2.2.2}% 28 +\BOOKMARK [1][]{section.2.3}{2.3 Techniques d'analyse de performance des applications parall\350les}{chapter.2}% 29 +\BOOKMARK [2][]{subsection.2.3.1}{2.3.1 G\351n\351ralit\351s et objectifs}{section.2.3}% 30 +\BOOKMARK [2][]{subsection.2.3.2}{2.3.2 Approches et m\351thodologie}{section.2.3}% 31 +\BOOKMARK [2][]{subsection.2.3.3}{2.3.3 Quelques outils d'analyse de performance}{section.2.3}% 32 +\BOOKMARK [1][]{section.2.4}{2.4 M\351thodes de pr\351diction de la performance des applications parall\350les}{chapter.2}% 33 +\BOOKMARK [1][]{section.2.5}{2.5 Conclusion partielle}{chapter.2}% 34 +\BOOKMARK [0][]{chapter.3}{3 Motivations}{part.1}% 35 \BOOKMARK [-1][]{part.2}{II PARTIE II - Travaux de contributions, r\351sultats et perspectives}{}% 36 -\BOOKMARK [0][]{chapter.3}{3 Comparaison par simulation \340 large \351chelle de la performance de deux algorithmes it\351ratifs parall\350les en mode asynchrone}{part.2}% 37 -\BOOKMARK [1][]{section.3.1}{3.1 Protocoles et exp\351rimentations}{chapter.3}% 38 -\BOOKMARK [1][]{section.3.2}{3.2 R\351sultats}{chapter.3}% 39 -\BOOKMARK [1][]{section.3.3}{3.3 Conclusion partielle}{chapter.3}% 40 -\BOOKMARK [0][]{chapter.4}{4 Simulation avec SIMGRID de l\220ex\351cution des solveurs lin\351aires en mode synchrone et asynchrone sur un environnement multi-coeurs simul\351s}{part.2}% 41 -\BOOKMARK [1][]{section.4.1}{4.1 Protocoles et exp\351rimentations}{chapter.4}% 42 -\BOOKMARK [1][]{section.4.2}{4.2 R\351sultats}{chapter.4}% 43 -\BOOKMARK [1][]{section.4.3}{4.3 Conclusion partielle}{chapter.4}% 44 -\BOOKMARK [0][]{chapter.5}{5 Mod\350le de pr\351diction de la performance \340 large \351chelle d'un algorithme it\351ratif parall\350le}{part.2}% 45 -\BOOKMARK [1][]{section.5.1}{5.1 Approche et m\351thodologie}{chapter.5}% 46 -\BOOKMARK [1][]{section.5.2}{5.2 Exp\351rimentations et r\351sultats}{chapter.5}% 47 -\BOOKMARK [1][]{section.5.3}{5.3 Conclusion partielle}{chapter.5}% 48 -\BOOKMARK [0][]{chapter.6}{6 Conclusion g\351n\351rale et perspectives}{part.2}% 49 -\BOOKMARK [1][]{section.6.1}{6.1 Conclusion g\351n\351rale}{chapter.6}% 50 -\BOOKMARK [1][]{section.6.2}{6.2 Travaux futurs et perspectives}{chapter.6}% 51 +\BOOKMARK [0][]{chapter.4}{4 Comparaison par simulation \340 large \351chelle de la performance de deux algorithmes it\351ratifs parall\350les en mode asynchrone}{part.2}% 37 +\BOOKMARK [1][]{section.4.1}{4.1 Protocoles et exp\351rimentations}{chapter.4}% 38 +\BOOKMARK [1][]{section.4.2}{4.2 R\351sultats}{chapter.4}% 39 +\BOOKMARK [1][]{section.4.3}{4.3 Conclusion partielle}{chapter.4}% 40 +\BOOKMARK [0][]{chapter.5}{5 Simulation avec SIMGRID de l\220ex\351cution des solveurs lin\351aires en mode synchrone et asynchrone sur un environnement multi-coeurs simul\351s}{part.2}% 41 +\BOOKMARK [1][]{section.5.1}{5.1 Protocoles et exp\351rimentations}{chapter.5}% 42 +\BOOKMARK [1][]{section.5.2}{5.2 R\351sultats}{chapter.5}% 43 +\BOOKMARK [1][]{section.5.3}{5.3 Conclusion partielle}{chapter.5}% 44 +\BOOKMARK [0][]{chapter.6}{6 Mod\350le de pr\351diction de la performance \340 large \351chelle d'un algorithme it\351ratif parall\350le}{part.2}% 45 +\BOOKMARK [1][]{section.6.1}{6.1 Approche et m\351thodologie}{chapter.6}% 46 +\BOOKMARK [1][]{section.6.2}{6.2 Exp\351rimentations et r\351sultats}{chapter.6}% 47 +\BOOKMARK [1][]{section.6.3}{6.3 Conclusion partielle}{chapter.6}% 48 +\BOOKMARK [0][]{chapter.7}{7 Conclusion g\351n\351rale et perspectives}{part.2}% 49 +\BOOKMARK [1][]{section.7.1}{7.1 Conclusion g\351n\351rale}{chapter.7}% 50 +\BOOKMARK [1][]{section.7.2}{7.2 Travaux futurs et perspectives}{chapter.7}% 51 \BOOKMARK [-1][]{part.3}{III Annexes}{}% 52 \BOOKMARK [0][]{appendix.A}{A Premier chapitre des annexes}{part.3}% 53 \BOOKMARK [0][]{appendix.B}{B Second chapitre des annexes}{part.3}% 54 diff --git a/These_RCE.pdf b/These_RCE.pdf deleted file mode 100644 index 9f4868c5d56798ec89540b1ca08edba9cf56bb80..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 5050903 zcmbSx1z40@7p{Sd2#6pENDLsILrEho-JL^scM3>JcSuNgmvnbGf^>Ix-5Jg~qTb*C z{P&*cIX?5ThyCug*1Ohv*Z#m4!vFzP026h67#0=)r6hoo&qT@u zK*M( 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taille n, \\ + \> et b un vecteur constant.\\ +\end{tabbing} + +Ainsi, \eqref{eq:2} peut s'écrire : + +\begin{equation*} + \left(\begin{array}{ccc} + a_{1,1} & \cdots & a_{1,n} \\ + \vdots & \ddots & \vdots\\ + a_{n,1} & \cdots & a_{n,n} + \end{array} \right) + \times + \left(\begin{array}{c} + x_1 \\ + \vdots\\ + x_n + \end{array} \right) + = + \left(\begin{array}{c} + b_1 \\ + \vdots\\ + b_n + \end{array} \right) +\end{equation*} + +Notons : \\ +D la matrice carrée de taille n formée par la diagonale de A. On suppose qu'aucun élément $a_{i,i}$ n'est égal à 0. \\ +L (resp. U) la matrice carrée de taille n formée par les éléments du bas (resp. haut) de A.\\ +On a donc : + +\begin{equation*} +D=\left( \begin{array}{ccc} +a_{1,1} & \cdots & 0 \\ +\vdots & \ddots & \vdots \\ +0 & \cdots & a_{n,n} +\end{array}\right) +\space +, \hspace{0,1cm}L=\left( \begin{array}{ccc} +0 & \cdots & 0 \\ +\vdots & \ddots & \vdots \\ +a_{n,1} & \cdots & 0 +\end{array}\right) +\space +et \hspace{0,2cm}U=\left( \begin{array}{ccc} +0 & \cdots & a_{1,n} \\ +\vdots & \ddots & \vdots \\ +0 & \cdots & 0 +\end{array}\right) +\end{equation*} + +Comme A = D + (L + U) et si $D^{-1}$ est l'inverse de la matrice diagonale D, on peut écrire : + +\begin{equation*} +Ax = b \Leftrightarrow ( D + L + U )x = b +\end{equation*} + +\begin{equation*} +\Leftrightarrow Dx = -(L+U)x + b +\end{equation*} + +\begin{equation} +\label{eq:3} +\Leftrightarrow ( x = D^{-1} \times [-(L+U)] x + D^{-1} b) +\end{equation} +Cette dernière égalité est l'equation $du point fixe$. L'algorithme itératif de Jacobi Figure~\ref{algo:01} (version séquentielle) et ses variantes découle de cette équation [4]. Si $x^{(k)}$ est la valeur approchée du vecteur inconnu à l'itération $k$, on a d'après \eqref{eq:3} avec un $x^{0}$ initial donné : + +\begin{equation} +x^{(k+1)} = D^{-1} \times [-(L+U)] x^{(k)} + D^{-1} b +\end{equation} + +\begin{figure}[!t] +\begin{algorithmic}[1] +\Input $A_{ij}$ (Matrice d'entrée), $b_{i}$ (Vecteur du membre droit), $n$ (Taille des vecteurs) et des matrices, $xOld_{i}$ (vecteur solution à l'itération précédente) +\Output $x_{i}$ (Vecteur solution)\medskip + +\State Charger $A_{ij}$, $b_{i}$, $n$, +\State Assigner la valeur initiale $x^0$ +\State \textbf{repeat} {jusqu'à l'obtention de la condition de convergence} \textbf{do} +\For {$i=0,1,2,\ldots (n-1)$} +\State $x_i \leftarrow 0$ +\For {$j=0,1,2,\ldots (n-1) \hspace{0.1cm} et \hspace{0.1cm} j \neq i$} +\State $x_{i} \leftarrow x_{i} + A_{ij} \times xOld_{j}$ +\EndFor +\For {$i=0,1,2,\ldots (n-1)$} +\State $xOld_{i} \leftarrow ( b_{i} - x_{i} ) \quad {/} \quad A_{ii}$ +\EndFor +\EndFor +\State \textbf{end repeat} + +\Statex +\end{algorithmic} +\caption{Algorithme itératif de Jacobi} +\label{algo:01} +\end{figure} + +La condition de convergence est déterminée au début du traitement. La méthode permet de passer à large échelle en distribuant l'exécutuion de l'algorithme sur un environnement de grille de calcul. \subsection{Méthode de résolution GMRES} @@ -569,9 +688,42 @@ Contrairement à une communication point à point, une communication dite collec La synchronisation des processus peut être obtenue avec la routine MPI\_Barrier qui, une fois lancée par un processus, bloque ce dernier jusqu'à ce que tous les processus de son groupe atteigne cette barrière comme un point de rendez-vous. \subsection{Simulateur SIMGRID - SMPI} -SimGrid est utilisé pour la simulation et l'étude du comportement d'applications parallèles dans un contexte d'un environnement complexe, hétérogène, distribué et dynamique. Il est conçu sur une simulation basée sur les évenements ("event driven") à un niveau d'abstraction et de fonctionnalités répondant aux applications et aux infrastructures [26]. +SimGrid est utilisé pour la simulation et l'étude du comportement d'applications parallèles dans un contexte d'un environnement complexe, hétérogène, distribué et dynamique. Comme son nom l'indique, développé par la communauté des utilisateurs de grille de calcul, il est utilisé aussi largement sur dans les domaines des applications pair-à-pair,du calcul à haute performance et du cloud computing [5,9]. Le choix de Simgrid comme outil de simulation dans le cadre de ces travaux a été motivé par son efficacité pour la simulation d'applications parallèles à large échelle. En effet, Simgrid rassemble au mieux les caractéristiques requises pour un simulateur dans un environnement de grille de calcul telles que la robustesse, la scalabilité et la justesse des résultats accompagnées d'un temps de réponse correct et d'une tolérance aux pannes de l'exécution [34]. + +Simgrid est conçu sur une simulation basée sur les évenements ("event driven")[26, 35] à un niveau d'abstraction et de fonctionnalités répondant aux applications et aux infrastructures. Cinq composants d'abstraction constitue le fonctionnement de Simgrid : + +\begin{itemize} + +\item[$\bullet$]Un "agent" est une entité qui assure l'ordonnancement de l'application et exécute le code sur une "location"; -\section{Motivations} +\item[$\bullet$]Une "location" est une hôte de l'environnement de simulation sur laquelle l'agent s'exécute. Outre les données propres à la location, des boîtes aux lettres sont conçues pour permettre les échanges de données avec d'autres agents; + +\item[$\bullet$]Une "tâche" est une activité de l'application simulée. Elle se décline sous forme d'un calcul (temps de calcul nécessaire) ou d'un transfert de données (volume de données à échanger; + +\item[$\bullet$]Un "chemin" décrit la liaison entre les locations. Il est utilisé par les agents lors d'un transfert de données à calculer le temps de transfert en tenant compte du routage à appliquer pour une telle liaison. + +\item[$\bullet$]La communication entre agents se fait à travers un "channel". Cette abstraction modélise la communication à travers un port entre des agents dans les locations. + +\end{itemize} + +Simgrid offre pour l'utilisateur plusieurs types d'interfaces de programmation [5,9]: MSG qui simule les "processes séquentiels conccurents", SimDAG qui est utilisé pour simuler des tâches parallèles modélisées en graphe acyclique direct et SMPI qui simule et exécute les applications écrites en MPI sans ou avec des modifications mineures. Outre le langage C natif, Simgrid accepte des applications écrites en C++, Java, Lua ou encore Ruby. + +De point de vue pratique, la figure \figref{simgrid1} présente la structure et les éléments de la plateforme de simulation Simgrid. Elle est composée des trois parties différentes suivantes : + +\begin{itemize} + +\item[$\bullet$] Le scénario de la simulation qui constitue les "modèles de ressources" du système. Evidemment, il comprend le code de l'application à exécuter dans le simulateur avec ses différents paramètres d'entrée mais aussi son modèle de déploiement. Un autre composant important de ce scénario aussi est le fichier, généralement au format XML, modélisant les détails de la topologie et l'architecture de l'environnement d'exécution. Il détermine par exemple pour le cas d'une grille de calcul, le nombre et les caractéristiques des clusters contribuant à cet environnement. Pour chaque cluster, les spécifications des serveurs (nombre de cores ou de processeurs, puissance en Flops, taux de disponibilité, ...)sont définies ainsi que les propriétés des réseaux de liaison entre ces différents composants de la grille (topologie du réseau, débit et latence, table de routage, ...). + +\item[$\bullet$] Le simulateur proprement dit. + +\item[$\bullet$] Les fichiers de sortie comprenant les résultats de la simulation de l'application ainsi que d'autres fichiers de monitoring de l'exécution comme un fichier de logging et de statistiques. Simgrid peut générer aussi des données pouvant être utilisées pour représenter visuellement le déroulement et la trace de la simulation dans le temps. + +\end{itemize} + +\mfigure[h]{width=8cm, height=8cm}{"Simgrid - In a nutshell"} {SIMGRID : Les éléments de la plateforme de simulation} {simgrid1} + +Les applications sous-tendant les expérimentations effectuées dans le cadre de ces travaux ont été ecrites en C et utilise les librairies MPI. Simgrid dispose de l'interface SMPI (Simulated MPI) qui peut exécuter un code MPI parallèles sans aucune ou à la limite très peu de modifications. A titre d'exemple, les variables globales doivent être transférées dans un contexte local dans l'application SMPI. Simgrid/SMPI assure l'implémentation de plus de 80\% des routines de la librairie MPI 2.0. Le code est exécuté réellement dans le simulateur dans l'environnement virtuel spécifié sauf que les communications sont interceptées et le temps de transfert calculé en tenant compte du partage des ressources existantes (par exemple le partage de la bande passante entre processus concurrents sur les réseaux de liaison).La scalabilité de Simgrid peut être obtenu par appel à des routines SMPI qui utilisent des structures de données partagées entre les processus parallèles réduisant ainsi la quantité de mémoire utilisée et permettant une montée en charge non négligeable. Toutefois, dans ce cas, comme tous les processus utilisent la même structure de données, la véracité des résultats obtenus n'est pas importante. + \section{Conclusion partielle} @@ -1337,13 +1489,17 @@ Plusieurs outils d'analyse de la performance parallèle utilisant une ou des com \subsection{Quelques outils d'analyse de performance} +Quelques outils d'analyse de performance sont passés en revue dans cette section. Ils mettent en exergue les différentes approches pour aborder ce problème crucial de performance pour les applications parallèles et distribuées. + +\begin{itemize} - - IPM +\item [$\bullet$] IPM -TAU a été conçu à l'Université d'Oregon comme un outil open source d'évaluation de performance [24]. Il intègre le profiling et le tracing constituant une platerme complète couvrant les trois étapes de l'analyse d'une applicatio parallèle. L'instrumentation du code peut être effectuée d'une façon complètement automatique avec un package fourni ("PDT - Program Database Toolkit - for routines")collectant toutes les informations sur les régions et hotspots du code, l'utilisation mémoire, les boucles, les entrées/sorties,...Selon le paramètrage de lancement, TAU peut collecter des informations les plus fines telles que le temps passé à chaque instruction dans une boucle ou le temps passé dans les communications à une étape du programme particulièrement dans les instructions collectives MPI par exemple. Toutes ces données peuvent par la suite être visualisées sous forme graphique (Paraprof 3D browser) pour une analyse fine afin d'optimiser la performance. +\item [$\bullet$] TAU a été conçu à l'Université d'Oregon comme un outil open source d'évaluation de performance [24]. Il intègre le profiling et le tracing constituant une platerme complète couvrant les trois étapes de l'analyse d'une applicatio parallèle. L'instrumentation du code peut être effectuée d'une façon complètement automatique avec un package fourni ("PDT - Program Database Toolkit - for routines")collectant toutes les informations sur les régions et hotspots du code, l'utilisation mémoire, les boucles, les entrées/sorties,...Selon le paramètrage de lancement, TAU peut collecter des informations les plus fines telles que le temps passé à chaque instruction dans une boucle ou le temps passé dans les communications à une étape du programme particulièrement dans les instructions collectives MPI par exemple. Toutes ces données peuvent par la suite être visualisées sous forme graphique (Paraprof 3D browser) pour une analyse fine afin d'optimiser la performance. - - SCALASCA +\item [$\bullet$] SCALA ou SCAlabity Analyzer est orienté particulèrement dans l'analyse de la performance des applications sur sa scalabilité lors de la montée en charge. Outre la prédiction de la performance, SCALA utilise les fonctionnalités avancées actuelles pour la mise au point (debugging) de la dite performance et d'une éventuelle restructuration du code parallèle d'une part mais aussi d'estimer l'impact des variations sur l'environnement matériel d'exécution. +\end{itemize} \section{Méthodes de prédiction de la performance des applications parallèles} @@ -1351,6 +1507,23 @@ TAU a été conçu à l'Université d'Oregon comme un outil open source d'évalu \section{Conclusion partielle} + +\chapter{Motivations} + +Malgré les grandes avancées dues aux performances des nouveaux processeurs, mémoires mais aussi des réseaux de communication, le milieu académique comme le domaine industriel sont toujours confrontés à des défis et challenges de plus en plus ambitieux. Ce fait est surtout accentué par des besoins de plus en plus variés et importants de calcul scientifique nécessitant de plus en plus de moyens mais aussi de méthodes plus efficientes et performantes. Ces besoins requièrent le traitement de données de plus en plus volumineuses mais aussi l'écriture d'algorithmes donnant des résultats probants dans un laps de temps correct. Le défi actuel serait donc l'exploitation de la puissance de calcul des matériels actuels dans un environnement de calcul optimisé pour traiter un volume de données de plus en plus important. \\ +Dans le cadre de nos travaux, l'objectif final est d'aider les utilisateurs finals (scientifiques, chercheurs, industriels, étudiants, ...) en calcul à haute performance à rentabiliser au maximum l'accès aux infrastructures de calcul physiques existantes, étant donné le côut et la difficulté (même des fois l'impossibilité) d'accès à ces dernières. En effet, la demande d'utilisation de ces infrastructures dépasse largement l'offre établie, entraînant des longues listes d'attente avant de pouvoir y accéder pour une durée très limitées. \\ +Pour atteindre ces objectifs, nous proposons d'utiliser des outils de simulation pour exécuter les applications pour étudier leurs comportements à large échelle mais aussi pour pouvoir déterminer les conditions optimales pour obtenir des résultats optimaux. Le simulateur permet d'étudier le comportement des algorithmes sous différentes conditions et sur des plateformes variées et paramétrables. Plusieurs modes d'exécution peuvent être essayés lors de l'expérimentation. De plus, la flexibilité de l'outil permet l'estimation de la performance des algorithmes lors du passage à l'échelle.\\ +Les questionnements suivants résument les motivations des travaux consignés dans cette thèse. +\begin{itemize} +\item [$\bullet$] a. Quelles solutions pratiques peut-on apporter pour réduire le coût de l’exécution d’applications parallèles et distribuées dans un environnement de grille de calcul durant tout son cycle de vie de développement ? +\item [$\bullet$] b. Quel est le comportement de l’algorithme distribué à large échelle dans cette architecture de grille de clusters en particulier lors de son exécution en mode asynchrone ? +\item [$\bullet$] c. Dans ce contexte, quels sont les facteurs importants identifiés permettant d’avoir un gain de temps d’exécution en mode asynchrone comparativement au mode synchrone ? A quel niveau peut-on estimer le gain obtenu en comparant l'exécution en mode asynchrone par rapport au mode classique synchrone. +\item [$\bullet$] d. Quel est le taux d'erreur de validation obtenue en comparant les résultats du lancement de l'application entre une exécution simulée et une execution sur un environnement réél équivalent. +\end{itemize} + +La partie suivante va exposer la méthodologie adoptée et les travaux de contributions pour apporter des réponses à ces questions. + + \part{PARTIE II - Travaux de contributions, résultats et perspectives} \chapter{Comparaison par simulation à large échelle de la performance de deux algorithmes itératifs parallèles en mode asynchrone} @@ -1431,6 +1604,13 @@ TAU a été conçu à l'Université d'Oregon comme un outil open source d'évalu \part*{BIBLIOGRAPHIE ET REFERENCES} + +{[}3{]} J. M. Bahi, S. Contassot-Vivier, R. Couturier - Parallel Iterative Algorithms: from Sequential to Grid Computing - \textit{CRC PRESS - Boca Raton London New York Washington, D.C.} + +{[}4{]} R. Couturier - Résolution de systèmes linéaires à très large échelle : méthodes classiques versus méthodes à large échelle - \textit{2014 - FEMTO-ST, Université de Franche-Comté} + +{[}5{]} C. E. Ramamonjisoa, L. Z. Khodjav, D. Laiymani, A. Giersch and R. Couturier. - Grid-enabled simulation of large-scale linear iterative solvers - \textit{2014 Femto-ST Institute - DISC Department - Université de Franche-Comté, IUT de Belfort-Montbéliard} + {[}6{]} J.M. Bahi, S. Contassot-Vivier, R. Couturier. Interest of the asynchronism in parallel iterative algorithms on meta-clusters. \textit{LIFC - Université de Belford-Montbéliard}. {[}7{]} T.P. Collignon and M.B. van Gijzen. Fast iterative solution of large sparse linear systems on geographically separated clusters. \textit{The International Journal of High Performance Computing Applications} 25(4) 440\textendash 450. @@ -1492,6 +1672,14 @@ of Sparse Random Matrices". \textit{UC Berkeley, INRIA Paris Rocquencourt, Tel-A {[}32{]} MPICH : www.mpich.org {[}33{]} OpenMPI : www.openmpi.org + +{[}34{]} M. Quinson et Al. - Experimenting HPC Systems with Simulation - \textit{Nancy University, France, Caen, HPCS/IWCMC 2010.} + +{[}35{]} A. Legrand, L. Marchal, H. Casanova - Scheduling Distributed Applications: the SimGrid Simulation Framework - \textit{Laboratoire de l’Informatique du Parallèlisme - Ecole Normale Supérieure de Lyon, Dept. of Computer Science and Engineering San Diego Supercomputer Center - University of California at San Diego} + +{[}36{]} Xian-He Sun, T. Fahringer, M. Pantano - SCALA: A perfformance system for scalable computing - \textit{Department Of Computer Science, Illinois Institute of Technology Chicago, Institute for software technology and parallel systems, University of Vienna Liechtenstein - The International Journal of High Performance Computing Applications,Volume 16, No. 4, Autumn 2002,} + + %%-------------------- %% List of figures and tables diff --git a/These_RCE.toc b/These_RCE.toc index 7e75c89..cd65dbe 100644 --- a/These_RCE.toc +++ b/These_RCE.toc @@ -6,50 +6,50 @@ \contentsline {subsection}{\numberline {1.1.2}Modes d'ex\IeC {\'e}cution synchrone et asynchrone}{7}{subsection.1.1.2} \contentsline {section}{\numberline {1.2}M\IeC {\'e}thodes de r\IeC {\'e}solution parall\IeC {\`e}les du probl\IeC {\`e}me de Poisson et de l'algorithme two-stage multisplitting de Krylov}{9}{section.1.2} \contentsline {subsection}{\numberline {1.2.1}Algorithme de Jacobi}{9}{subsection.1.2.1} -\contentsline {subsection}{\numberline {1.2.2}M\IeC {\'e}thode de r\IeC {\'e}solution GMRES}{9}{subsection.1.2.2} -\contentsline {subsection}{\numberline {1.2.3}Solveur multisplitting}{9}{subsection.1.2.3} -\contentsline {section}{\numberline {1.3}Simulateurs d'ex\IeC {\'e}cution d'algorithmes parall\IeC {\`e}les MPI dans une grille de calcul}{9}{section.1.3} -\contentsline {subsection}{\numberline {1.3.1}Calcul sur grille}{9}{subsection.1.3.1} -\contentsline {subsection}{\numberline {1.3.2}G\IeC {\'e}n\IeC {\'e}ralit\IeC {\'e}s sur la simulation}{11}{subsection.1.3.2} -\contentsline {subsection}{\numberline {1.3.3}MPI - Message Passing Interface}{13}{subsection.1.3.3} -\contentsline {subsection}{\numberline {1.3.4}Simulateur SIMGRID - SMPI}{15}{subsection.1.3.4} -\contentsline {section}{\numberline {1.4}Motivations}{15}{section.1.4} -\contentsline {section}{\numberline {1.5}Conclusion partielle}{15}{section.1.5} -\contentsline {chapter}{\numberline {2}Etat de l'art et travaux de recherche associ\IeC {\'e}s}{17}{chapter.2} -\contentsline {section}{\numberline {2.1}Concepts et d\IeC {\'e}finitions}{17}{section.2.1} -\contentsline {subsection}{\numberline {2.1.1}Performance de l'application parall\IeC {\`e}le et scalabilit\IeC {\'e}}{17}{subsection.2.1.1} -\contentsline {subsection}{\numberline {2.1.2}Taux d'erreur lors de la pr\IeC {\'e}diction}{19}{subsection.2.1.2} -\contentsline {subsection}{\numberline {2.1.3}Weak contre strong scaling}{19}{subsection.2.1.3} -\contentsline {section}{\numberline {2.2}Probl\IeC {\'e}matique sur la pr\IeC {\'e}diction \IeC {\`a} large \IeC {\'e}chelle de la performance des applications}{20}{section.2.2} -\contentsline {subsection}{\numberline {2.2.1}Facteurs li\IeC {\'e}s \IeC {\`a} l'\IeC {\'e}cosyst\IeC {\`e}me}{21}{subsection.2.2.1} -\contentsline {subsubsection}{\numberline {2.2.1.1}Facteur architecture des processeurs}{22}{subsubsection.2.2.1.1} -\contentsline {subsubsection}{\numberline {2.2.1.2}Facteur : M\IeC {\'e}moire et stockage}{24}{subsubsection.2.2.1.2} -\contentsline {subsubsection}{\numberline {2.2.1.3}Facteur : R\IeC {\'e}seaux de communication}{28}{subsubsection.2.2.1.3} -\contentsline {subsection}{\numberline {2.2.2}Facteurs li\IeC {\'e}s au code de l'application}{28}{subsection.2.2.2} -\contentsline {subsubsection}{\numberline {2.2.2.1}Facteur : Taille du probl\IeC {\`e}me}{29}{subsubsection.2.2.2.1} -\contentsline {subsubsection}{\numberline {2.2.2.2}Performance de la parall\IeC {\'e}lisation}{29}{subsubsection.2.2.2.2} -\contentsline {section}{\numberline {2.3}Techniques d'analyse de performance des applications parall\IeC {\`e}les}{32}{section.2.3} -\contentsline {subsection}{\numberline {2.3.1}G\IeC {\'e}n\IeC {\'e}ralit\IeC {\'e}s et objectifs}{32}{subsection.2.3.1} -\contentsline {subsection}{\numberline {2.3.2}Approches et m\IeC {\'e}thodologie}{32}{subsection.2.3.2} -\contentsline {subsection}{\numberline {2.3.3}Quelques outils d'analyse de performance}{34}{subsection.2.3.3} -\contentsline {section}{\numberline {2.4}M\IeC {\'e}thodes de pr\IeC {\'e}diction de la performance des applications parall\IeC {\`e}les}{35}{section.2.4} -\contentsline {section}{\numberline {2.5}Conclusion partielle}{35}{section.2.5} -\contentsline {part}{II\hspace {1em}PARTIE II - Travaux de contributions, r\IeC {\'e}sultats et perspectives}{37}{part.2} -\contentsline {chapter}{\numberline {3}Comparaison par simulation \IeC {\`a} large \IeC {\'e}chelle de la performance de deux algorithmes it\IeC {\'e}ratifs parall\IeC {\`e}les en mode asynchrone}{39}{chapter.3} -\contentsline {section}{\numberline {3.1}Protocoles et exp\IeC {\'e}rimentations}{39}{section.3.1} -\contentsline {section}{\numberline {3.2}R\IeC {\'e}sultats}{39}{section.3.2} -\contentsline {section}{\numberline {3.3}Conclusion partielle}{39}{section.3.3} -\contentsline {chapter}{\numberline {4}Simulation avec SIMGRID de l\textquoteright ex\IeC {\'e}cution des solveurs lin\IeC {\'e}aires en mode synchrone et asynchrone sur un environnement multi-coeurs simul\IeC {\'e}s}{41}{chapter.4} -\contentsline {section}{\numberline {4.1}Protocoles et exp\IeC {\'e}rimentations}{41}{section.4.1} -\contentsline {section}{\numberline {4.2}R\IeC {\'e}sultats}{41}{section.4.2} -\contentsline {section}{\numberline {4.3}Conclusion partielle}{41}{section.4.3} -\contentsline {chapter}{\numberline {5}Mod\IeC {\`e}le de pr\IeC {\'e}diction de la performance \IeC {\`a} large \IeC {\'e}chelle d'un algorithme it\IeC {\'e}ratif parall\IeC {\`e}le}{43}{chapter.5} -\contentsline {section}{\numberline {5.1}Approche et m\IeC {\'e}thodologie}{43}{section.5.1} -\contentsline {section}{\numberline {5.2}Exp\IeC {\'e}rimentations et r\IeC {\'e}sultats}{43}{section.5.2} -\contentsline {section}{\numberline {5.3}Conclusion partielle}{43}{section.5.3} -\contentsline {chapter}{\numberline {6}Conclusion g\IeC {\'e}n\IeC {\'e}rale et perspectives}{45}{chapter.6} -\contentsline {section}{\numberline {6.1}Conclusion g\IeC {\'e}n\IeC {\'e}rale}{45}{section.6.1} -\contentsline {section}{\numberline {6.2}Travaux futurs et perspectives}{45}{section.6.2} -\contentsline {part}{III\hspace {1em}Annexes}{57}{part.3} -\contentsline {chapter}{\numberline {A}Premier chapitre des annexes}{59}{appendix.A} -\contentsline {chapter}{\numberline {B}Second chapitre des annexes}{61}{appendix.B} +\contentsline {subsection}{\numberline {1.2.2}M\IeC {\'e}thode de r\IeC {\'e}solution GMRES}{10}{subsection.1.2.2} +\contentsline {subsection}{\numberline {1.2.3}Solveur multisplitting}{11}{subsection.1.2.3} +\contentsline {section}{\numberline {1.3}Simulateurs d'ex\IeC {\'e}cution d'algorithmes parall\IeC {\`e}les MPI dans une grille de calcul}{11}{section.1.3} +\contentsline {subsection}{\numberline {1.3.1}Calcul sur grille}{11}{subsection.1.3.1} +\contentsline {subsection}{\numberline {1.3.2}G\IeC {\'e}n\IeC {\'e}ralit\IeC {\'e}s sur la simulation}{13}{subsection.1.3.2} +\contentsline {subsection}{\numberline {1.3.3}MPI - Message Passing Interface}{15}{subsection.1.3.3} +\contentsline {subsection}{\numberline {1.3.4}Simulateur SIMGRID - SMPI}{17}{subsection.1.3.4} +\contentsline {section}{\numberline {1.4}Conclusion partielle}{19}{section.1.4} +\contentsline {chapter}{\numberline {2}Etat de l'art et travaux de recherche associ\IeC {\'e}s}{21}{chapter.2} +\contentsline {section}{\numberline {2.1}Concepts et d\IeC {\'e}finitions}{21}{section.2.1} +\contentsline {subsection}{\numberline {2.1.1}Performance de l'application parall\IeC {\`e}le et scalabilit\IeC {\'e}}{21}{subsection.2.1.1} +\contentsline {subsection}{\numberline {2.1.2}Taux d'erreur lors de la pr\IeC {\'e}diction}{23}{subsection.2.1.2} +\contentsline {subsection}{\numberline {2.1.3}Weak contre strong scaling}{23}{subsection.2.1.3} +\contentsline {section}{\numberline {2.2}Probl\IeC {\'e}matique sur la pr\IeC {\'e}diction \IeC {\`a} large \IeC {\'e}chelle de la performance des applications}{24}{section.2.2} +\contentsline {subsection}{\numberline {2.2.1}Facteurs li\IeC {\'e}s \IeC {\`a} l'\IeC {\'e}cosyst\IeC {\`e}me}{25}{subsection.2.2.1} +\contentsline {subsubsection}{\numberline {2.2.1.1}Facteur architecture des processeurs}{26}{subsubsection.2.2.1.1} +\contentsline {subsubsection}{\numberline {2.2.1.2}Facteur : M\IeC {\'e}moire et stockage}{28}{subsubsection.2.2.1.2} +\contentsline {subsubsection}{\numberline {2.2.1.3}Facteur : R\IeC {\'e}seaux de communication}{32}{subsubsection.2.2.1.3} +\contentsline {subsection}{\numberline {2.2.2}Facteurs li\IeC {\'e}s au code de l'application}{32}{subsection.2.2.2} +\contentsline {subsubsection}{\numberline {2.2.2.1}Facteur : Taille du probl\IeC {\`e}me}{33}{subsubsection.2.2.2.1} +\contentsline {subsubsection}{\numberline {2.2.2.2}Performance de la parall\IeC {\'e}lisation}{33}{subsubsection.2.2.2.2} +\contentsline {section}{\numberline {2.3}Techniques d'analyse de performance des applications parall\IeC {\`e}les}{36}{section.2.3} +\contentsline {subsection}{\numberline {2.3.1}G\IeC {\'e}n\IeC {\'e}ralit\IeC {\'e}s et objectifs}{36}{subsection.2.3.1} +\contentsline {subsection}{\numberline {2.3.2}Approches et m\IeC {\'e}thodologie}{36}{subsection.2.3.2} +\contentsline {subsection}{\numberline {2.3.3}Quelques outils d'analyse de performance}{38}{subsection.2.3.3} +\contentsline {section}{\numberline {2.4}M\IeC {\'e}thodes de pr\IeC {\'e}diction de la performance des applications parall\IeC {\`e}les}{39}{section.2.4} +\contentsline {section}{\numberline {2.5}Conclusion partielle}{39}{section.2.5} +\contentsline {chapter}{\numberline {3}Motivations}{41}{chapter.3} +\contentsline {part}{II\hspace {1em}PARTIE II - Travaux de contributions, r\IeC {\'e}sultats et perspectives}{43}{part.2} +\contentsline {chapter}{\numberline {4}Comparaison par simulation \IeC {\`a} large \IeC {\'e}chelle de la performance de deux algorithmes it\IeC {\'e}ratifs parall\IeC {\`e}les en mode asynchrone}{45}{chapter.4} +\contentsline {section}{\numberline {4.1}Protocoles et exp\IeC {\'e}rimentations}{45}{section.4.1} +\contentsline {section}{\numberline {4.2}R\IeC {\'e}sultats}{45}{section.4.2} +\contentsline {section}{\numberline {4.3}Conclusion partielle}{45}{section.4.3} +\contentsline {chapter}{\numberline {5}Simulation avec SIMGRID de l\textquoteright ex\IeC {\'e}cution des solveurs lin\IeC {\'e}aires en mode synchrone et asynchrone sur un environnement multi-coeurs simul\IeC {\'e}s}{47}{chapter.5} +\contentsline {section}{\numberline {5.1}Protocoles et exp\IeC {\'e}rimentations}{47}{section.5.1} +\contentsline {section}{\numberline {5.2}R\IeC {\'e}sultats}{47}{section.5.2} +\contentsline {section}{\numberline {5.3}Conclusion partielle}{47}{section.5.3} +\contentsline {chapter}{\numberline {6}Mod\IeC {\`e}le de pr\IeC {\'e}diction de la performance \IeC {\`a} large \IeC {\'e}chelle d'un algorithme it\IeC {\'e}ratif parall\IeC {\`e}le}{49}{chapter.6} +\contentsline {section}{\numberline {6.1}Approche et m\IeC {\'e}thodologie}{49}{section.6.1} +\contentsline {section}{\numberline {6.2}Exp\IeC {\'e}rimentations et r\IeC {\'e}sultats}{49}{section.6.2} +\contentsline {section}{\numberline {6.3}Conclusion partielle}{49}{section.6.3} +\contentsline {chapter}{\numberline {7}Conclusion g\IeC {\'e}n\IeC {\'e}rale et perspectives}{51}{chapter.7} +\contentsline {section}{\numberline {7.1}Conclusion g\IeC {\'e}n\IeC {\'e}rale}{51}{section.7.1} +\contentsline {section}{\numberline {7.2}Travaux futurs et perspectives}{51}{section.7.2} +\contentsline {part}{III\hspace {1em}Annexes}{65}{part.3} +\contentsline {chapter}{\numberline {A}Premier chapitre des annexes}{67}{appendix.A} +\contentsline {chapter}{\numberline {B}Second chapitre des annexes}{69}{appendix.B} -- 2.39.5

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