parfor

I seem to be one of few people using the Matlab coder (codegen command) to get speedup, judging by the fact that there is so little discussion or help on-line. I've gotten incredible speedups from it in some cases. I've never seen it documented, but when I make a MEX file using codegen from a Matlab script with a parfor loop, it often will thread the resulting MEX. Parfor in functions spawns multiple processes which is often less efficient than just threading (I'm inferring all this from watching top in linux and seeing multiple 100% processes in Matlab functions, but a single e.g. 1000%

I'm working on a project in which parallel computing would be a huge advantage. The project simulates multiple Simulink models. I did the simulation with a normal for-Loop, but since it takes days to simulate I decided to try the "parfor"-Loop . But that's where the problem begins. First I'll give you pictures of my code, the workspace and the Simulink-part which is causing me problems: Here's my code: apool = gcp('nocreate'); if isempty(apool) apool = parpool('local'); end wpath = pwd; parfor k = 1:number_of_models load_system(strcat(wpath,'\Models_Folder\',House(k).model_name)); set_param

I know how to change the maximum number of workers using the Parallel preferences window in Matlab, but I cannot find any documentation about how to make changes on the preferences from console/code, and specifically about how to change the maximum number of workers I can use in a forloop. Any help will be greatly appreciated. You want the parpool function. With no arguments it creates a default number of workers, with an integer argument it creates that many workers. If you just use a parfor loop without calling it, you get the default number for your profile, but I'm not sure where that is

In the MATLAB documentation we have a code example for the parfeval function. I have some questions about it. This is the code: p = gcp(); %// To request multiple evaluations, use a loop. for idx = 1:10 f(idx) = parfeval(p,@magic,1,idx); % Square size determined by idx end %// Collect the results as they become available. magicResults = cell(1,10); for idx = 1:10 %// fetchNext blocks until next results are available. [completedIdx,value] = fetchNext(f); magicResults{completedIdx} = value; fprintf('Got result with index: %d.\n', completedIdx); end How does parfeval work? Does this function send

So I am trying to call this function using a parfor (basically curve fitting using Fourier series through a vector in a parfor loop): function[coefnames,coef] = fourier_regression(vect_waves,n) coef = zeros(length(vect_waves)-n,18); current_coef = zeros(18,1); % All the terms of the fourier series x = 1:n; parpool_obj = parpool; parfor i=n:length(vect_waves) take_fourier = vect_waves(i-n+1:i); f = fit(x,take_fourier,'fourier8'); current_coef = coeffvalues(f); coef(i,1:length(current_coef)) = current_coef; end coefnames = coeffnames(f); delete(parpool_obj); end But I am just getting the error

I have this (quite long) Matlab code with nested loops where I want to parallelize the main time-consuming iteration. The only variable that (apparently) gives me problems is DMax , where I get the error: Error: The variable DMax in a `parfor` cannot be classified. See Parallel for Loops in MATLAB, "Overview". This is a draft of my code: t0=matrix (Maxiter,1); % This is a big matrix whose dimensions are reported in brachets Maxiter = 1E6; DMax = zeros(Maxiter,40); % Other Stuff for j=1:269 % Do more stuff for soil=1:4 parfor i =1:Maxiter k(i,soil) = a %k is a real number a(i,soil) = b %similar

When an out-of-memory error is raised in a parfor , is there any way to kill only one Matlab slave to free some memory instead of having the entire script terminate? Here is what happens by default when an out-of-memory error occurs in a parfor : the script terminated, as shown in the screenshot below. I wish there was a way to just kill one slave (i.e. removing a worker from parpool ) or stop using it to release as much memory as possible from it: Daniel If you get a out of memory in the master process there is no chance to fix this. For out of memory on the slave, this should do it: The