Hello, On another way, see bugzilla #16359 in Xcos
I've provided the program to ESI Results : Xcos Scilab 5.5.2 : Durée de la simulation : 258.3 s Xcos Sciab 6.1.0 : Durée de la simulation : 4353.4 s Stack? Mallloc ? Regards Before printing, think about ENVIRONMENTAL responsabity -----Message d'origine----- De : users <[email protected]> De la part de Antoine Monmayrant Envoyé : mardi 31 mars 2020 11:55 À : Users mailing list for Scilab <[email protected]> Objet : Re: [Scilab-users] ?==?utf-8?q? Scilab 6.1 too slow to list large vectors Hello, I also ran some tests and it seems that the regression is clearly line-based: ie the regression is worse for vector than for matrix because there more lines to print to display a vector than a matrix of similar length. See my test code below. n1=300; n2=100; mat=rand(n1,n2); tn=[]; /* Ctrl+E the code below several times*/ tic mat // matrix //mat(:) // vector t=toc() tn=[tn,t]; disp('---------') disp('N_run = '+string(length(tn))) disp('T='+string(mean(tn))+'+/-'+string(stdev(tn))); /* 6.0.2 matrix N_run = 20 T=0.6392742+/-0.056698 1.6x slower but plotting 4 values per line ie 4x less lines to plot than for the matrix version 6.1.0 matrix N_run = 10 T=1.0335109+/-0.0317414 6.0.2 vector mat(:) N_run = 20 T=0.4943449+/-0.0662727 6.1.0 vector mat(:) N_run = 10 T=3.800698+/-0.1121251 7.7x slower ie same slow down per line than for the vector version as we plot 4 values per line so 4x more lines */ Le Mardi, Mars 31, 2020 11:36 CEST, Federico Miyara <[email protected]> a écrit: > > Stéphane, > > I simplified and diversified the test: > > tic > u = rand(100,1000) > toc > > takes > 16 s in 6.1 > 10 s in 6.0.2 > > tic > u = u(:)' > toc > > takes > 66 s in 6.1 > 1.29 s in 6.0.2 > > tic > u = u(:) > toc > > takes > 107 s in 6.1 > 1.52 s in 6.0.2 > > tic > u = matrix(u,1000,100) > toc > > takes > 16 s in 6.1 > 10 s in 6.0.2 > > tic > u = matrix(u,100, 100, 10) > toc > > takes > 0.5 s in 6.1 > 0.5 s in 6.0.2 > > Seems as if vertical (many line feeds) print took more time than > horizontal print. > > Regards, > > Federico Miyara > > > > On 31/03/2020 05:23, Stéphane Mottelet wrote: > > > > Hi Frederico, > > > > Thanks for reporting. Can you test if the regression also holds for > > matrices ? > > > > S. > > > > Le 30/03/2020 à 11:56, Federico Miyara a écrit : > >> > >> Fs = 44100 > >> T = 2.5 > >> t = [0:T*Fs]/Fs; > >> ximp = exp(-t/0.3).*rand(t,"normal") + 0.004*rand(t,"normal"); > >> > >> tic > >> ximp = ximp(:) > >> toc > > -- > > Stéphane Mottelet > > Ingénieur de recherche > > EA 4297 Transformations Intégrées de la Matière Renouvelable > > Département Génie des Procédés Industriels Sorbonne Universités - > > Université de Technologie de Compiègne CS 60319, 60203 Compiègne > > cedex Tel : +33(0)344234688 http://www.utc.fr/~mottelet > > > > _______________________________________________ > > users mailing list > > [email protected] > > http://lists.scilab.org/mailman/listinfo/users > _______________________________________________ users mailing list [email protected] http://lists.scilab.org/mailman/listinfo/users _______________________________________________ users mailing list [email protected] http://lists.scilab.org/mailman/listinfo/users
