Skip to main content

Stretch the dynamic range of the given 8-bit grayscale image using MATL...

Consecutive call of parsim constantly increases memory usage (Ubuntu)

 Hi,

 
I am trying to perform a parameter sweep on my simulink model using parsim. However, after parsim has been called for several times (after ~24h) Matlab gets killed (I am using Ubuntu 20.04). The ubuntu terminal output from where I started Matlab just shows "Killed".
 
Taking a closer look I noticed that memory usage constantly increases. I stopped the program and shut down the parallel pool (delete(gcp('nocreate'))) which released some memory. I cleared the workspace and nothing is running anymore, however Matlab still takes 40% of the memory  and until now I could not find out where this comes from.
 
I tried to abstract the important commands to clarify the structure of my program and my usage of the commands (the original script has much more parameters etc). In short, in nested for-loops combining my parameter values I create an array of Simulink.SimulationInput objects containing batch_size objects, run the simulations using parsim, create another array, run parsim again and so on:
 
start_vals = [0.1, 0.08, 0.06, 0.04, 0.03, 0.02];
stop_vals = [0.04, 0.03, 0.02, 0.01, 0.0, -0.05];
batch_size = 50;
model = 'modelName';

cnt_exp = 0;
cnt_file = 0;
cnt_batch = 0;

for start_val = start_vals
    for stop_val = stop_vals
        cnt_exp = cnt_exp +1;
        
        MP = setParam(MP,start_val, stop_val)
        simulation_length = 12;
        cnt_batch = cnt_batch +1;
        sim_input(cnt_batch) = Simulink.SimulationInput(model);
        sim_input(cnt_batch) = sim_input(cnt_batch).setVariable('simulation_length',simulation_length);
        sim_input(cnt_batch) = sim_input(cnt_batch).setVariable('MP',MP);
        
        if cnt_batch < batch_size
            total_runs = cnt_file * batch_size + cnt_exp;
            if total_runs ~= sum_exp % last incomplete batch has to be processed
                continue;
            end
        end
        
        total_runs = cnt_file * batch_size + cnt_exp;
        try
            simOut = parsim(sim_input);
            for res = 1:length(simOut)
                % Process result
            end
            % Save results
            cnt_file = cnt_file +1;
            
            % Reset counters and arrays
            cnt_exp = 0;
            cnt_batch = 0;
            clear sim_input;
            
        catch error
            warning ("Something went wrong")
        end
    end
end

I'd greatly appreciate any help or hint on what is going wrong here!


NOTE:-

Matlabsolutions.com provide latest MatLab Homework Help,MatLab Assignment Help for students, engineers and researchers in Multiple Branches like ECE, EEE, CSE, Mechanical, Civil with 100% output.Matlab Code for B.E, B.Tech,M.E,M.Tech, Ph.D. Scholars with 100% privacy guaranteed. Get MATLAB projects with source code for your learning and research.

Expert Answer

 John Williams answered . 2021-10-20 09:53:17

I managed to solve the issue by adapting the usage of sim_input and simOut. As clear apparently does not free the memory in Unix systems and delete somehow didn't work with these arrays, I avoided using clear and I initialized sim_input outside of the for-loop. Furthermore, I put all calculations concerning simOut into a function, hoping that this additional scope would clear the memory. I didn't check the effect of both measures separately, but I think mainly sim_input was causing the problem.
 
So this is approximately how the code without memory leak looks like now:
 
 
start_vals = [0.1, 0.08, 0.06, 0.04, 0.03, 0.02];
stop_vals = [0.04, 0.03, 0.02, 0.01, 0.0, -0.05];
batch_size = 50;
model = 'modelName';

cnt_exp = 0;
cnt_file = 0;
cnt_batch = 0;

for i=1:batch_size
    sim_input(i) = Simulink.SimulationInput(model);
end

Comments

Popular posts from this blog

https://journals.worldnomads.com/scholarships/story/70330/Worldwide/Dat-shares-his-photos-from-Bhutan https://www.blogger.com/comment.g?blogID=441349916452722960&postID=9118208214656837886&page=2&token=1554200958385 https://todaysinspiration.blogspot.com/2016/08/lp-have-look-at-this-this-is-from.html?showComment=1554201056566#c578424769512920148 https://behaviorpsych.blogspot.com/p/goal-bank.html?showComment=1554201200695 https://billlumaye.blogspot.com/2012/10/tagg-romney-drops-by-bill-show.html?showComment=1550657710334#c7928008051819098612 http://blog.phdays.com/2014/07/review-of-waf-bypass-tasks.html?showComment=1554201301305#c6351671948289526101 http://www.readyshelby.org/blog/gifts-of-preparedness/#comment_form http://www.hanabilkova.svet-stranek.cz/nakup/ http://www.23hq.com/shailendrasingh/photo/21681053 http://blogs.stlawu.edu/jbpcultureandmedia/2013/11/18/blog-entry-10-guns-as-free-speech/comment-page-1443/#comment-198345 https://journals.worldnomads.com

USING MACHINE LEARNING CLASSIFICATION ALGORITHMS FOR DETECTING SPAM AND NON-SPAM EMAILS

    ABSTRACT We know the increasing volume of unwanted volume of emails as spam. As per statistical analysis 40% of all messages are spam which about 15.4 billion email for every day and that cost web clients about $355 million every year. Spammers to use a few dubious techniques to defeat the filtering strategies like utilizing irregular sender addresses or potentially add irregular characters to the start or the finish of the message subject line. A particular calculation is at that point used to take in the order rules from these email messages. Machine learning has been contemplated and there are loads of calculations can be used in email filtering. To classify these mails as spam and non-spam mails implementation of machine learning algorithm  such as KNN, SVM, Bayesian classification  and ANN  to develop better filtering tool.   Contents ABSTRACT 2 1. INTRODUCTION 4 1.1 Objective : 5 2. Literature Review 5 2.1. Existing Machine learning technique. 6 2.2 Existing

Why are Fourier series important? Are there any real life applications of Fourier series?

A  Fourier series  is a way of representing a periodic function as a (possibly infinite) sum of sine and cosine functions. It is analogous to a Taylor series, which represents functions as possibly infinite sums of monomial terms. A sawtooth wave represented by a successively larger sum of trigonometric terms. For functions that are not periodic, the Fourier series is replaced by the Fourier transform. For functions of two variables that are periodic in both variables, the trigonometric basis in the Fourier series is replaced by the spherical harmonics. The Fourier series, as well as its generalizations, are essential throughout the physical sciences since the trigonometric functions are eigenfunctions of the Laplacian, which appears in many physical equations. Real-life applications: Signal Processing . It may be the best application of Fourier analysis. Approximation Theory . We use Fourier series to write a function as a trigonometric polynomial. Control Theory . The F