matlab,parallel-processing,parfor

MATLAB doesn't clear all variables in each iteration, there is some intelligence behind that: Look at the following parfor loop: parfor k=1:n x = function1(a(k)); y(k) = function2(a(k)); end First it is important to know that the iteration order is not fix, so there is no order in which the...

matlab,if-statement,for-loop,parfor

I'm adding another answer with all the conditionals checked: cond1 = (w >= 0); cond2 = cond1 & (v < -rhow – beta – 1); cond3 = cond1 & ((-rhow – beta – 1 <= v) && (v <= -rhow + beta – 1)); cond4 = cond1 & ((-rhow +...

matlab,parallel-processing,simulink,parfor

I think you need to load the model on the worker (see sim in parfor with Normal Mode in the documentation for more details), so your load_system needs to be within the parfor loop: tic parfor w=1:10 load_system('mymodel'); warning('off', 'Simulink:Engine:UsingDefaultMaxStepSize'); w_str=num2str(w); set_param('mymodel/mysystem','sys', ['tf(',w_str,',[1 ',w_str,'])'] ) sim('mymodel',[],[]) drawnow end toc ...

matlab,parallel-processing,parfor

The problem here is that it we can see that you're not using Dat in a way that is order-dependent, but the static analysis machinery of parfor cannot deduce that because of the way you're assigning into it. I think you can work around this by instead creating a whole...

matlab,matrix,parallel-processing,parfor

You can use another matrix to store the result. Imag2 = zeros(size(Imag1)); % Perform the classification Imag2(find(Imag1 <= 130)) = 4; Imag2(find(Imag1 >= 150)) = 1; Imag2(find(Imag1 > 140)) = 2; Imag2(find(Imag1 > 130)) = 3; so you can use a parfor loop somehow. Since parfor doesn't care about order...

matlab,parallel-processing,parfor

Got an elegant solution on http://www.mathworks.com/matlabcentral/answers/179884-save-inside-parfor-loop-at-a-specific-iteration-step parpool('local',10); % results will be distributed on 10 workers output1=distributed.NaN(1,1e5); %pre-allocate output2=distributed.NaN(1,1e5); spmd for i=drange(1:1e5) [output1(ii), output2(ii)] = MyFunction(~,~,ii); parsave(['output1', num2str(labindex)],... getLocalPart(output1)); parsave(['output2', num2str(labindex)],... getLocalPart(output2)); end end I've used 2 times the parsave function so in case...

Well for the unexpected outputs and number of rows in the output , it's because the wrong use of fprintf . We need to print element by element , which means the fprintf has to be inside the parfor loop, so your code should look like this: ftemp=fopen('temp.csv','w'); march=cell(1,20);tc=0; march={'ab';'cd';'ef';'gh';'ij';'klm';'mn';'op';'qr';'st';'uv';'ABls';'CD';'E3F';'GH';'IJ';'dynaKL';'MN';'OP';'QR'};...

matlab,loops,for-loop,nested,parfor

Your nested loops as they are do not exhibit any dependence on the loop parameters so you might want to reformat your code above. But in general, so long as your iterations are not dependent on each other and the number of iterations is large enough to support the delay...

arrays,matlab,file-io,parallel-processing,parfor

Having the outer parfor loop means that the values of i the function looks at is not guaranteed to be 1, 2, 3, etc. However, the theta1, theta2, theta3 variables INSIDE the parfor loop will be calculated in their order inside their inner parfor loop. So the only thing you...

matlab,parallel-processing,parfor

Distributed arrays are stored on the workers, not the client, and operations on them are carried out in parallel by the workers - that's the point of them. The difference between distributed and codistributed arrays is only one of perspective. From the point of view of the client, they are...

matlab,parallel-processing,parfor

parfor has various constraints that make using scripts inside the loop generally a bad option. The main constraint is that the text of the parfor loop must be analysable to see what new variables might be created - this is referred to as "transparency" in the doc. Try using a...

matlab,parallel-processing,parfor

I don't think there's cause for concern just yet. The MATLAB processes will always use some memory even when idle and 170 MB is not unusual. In fact on my machine, if I start a pool of 4 workers using 'local', and do nothing, each worker uses around 250 MB....

matlab,parallel-processing,parfor

How does parfeval work? Does this function send every (idx) evaluational of the magic function to a specific worker? From the documentation: parfeval requests asynchronous execution on a worker in a parallel pool. You can cancel the execution with cancel. It just sends it to any free worker. Use...

Simple Progress Bar Something like a progress bar could be done similar to this... Before the parfor loop : fprintf('Progress:\n'); fprintf(['\n' repmat('.',1,m) '\n\n']); And during the loop: fprintf('\b|\n'); Here we have m is the total number of iterations, the . shows the total number of iterations and | shows the...

This is part of a batch run. To resolve the issue instead of re-using the pools for every batch iteration. Make sure to "close" it. Then open fresh Matlab pools for every iteration. Seems to be more stable now, although a lot slower than the previous implementation.

would spmd res = zeros(10*100,1); for i = 1:10 for j = 1:100 idx = ((i-1) * 100) + j; res(idx) = 5; end end end solve your problem?...

matlab,parallel-processing,parfor

Try this instead.. s=struct([]); s(5).m = 0; parfor i=1:5 s(i).m=i; s(i) end Your error occurs because you're trying to change the structure in the parfor loop. So each core is independently trying to add the field m. Another issue is trying to lengthen s dynamically in the parfor loop which...

matlab,for-loop,vector,vectorization,parfor

You can use a logical vector to index a matrix, as described in the MATLAB help pages. Let's make a simple example: A = [1 2 3 4]; ind = logical([0 1 0 1]); B = A(ind) B = 2 4 You can use this system to model all different...