Parfor loop with complex structure

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Pol Cardona Rubio
Pol Cardona Rubio 2022-4-20
评论： Pol Cardona Rubio 2024-4-8
My idea is to run multiple independent simulations only changing parameters, but I want to keep all the results stored in 1 unique workspace that is also loaded so when I stop and rerun the code it checks from which iteration it should continue.
For this purpose I have 3 nested loops, the inner one being a parfor loop and the outer ones a for loop. The problem is that for all this data to be organized I want to dynamically create sublevels on the workspace structure named after the iteration indexes and I want to store 12 different variables with such different dimensions and types.
I tried to index the first level of the output structure as in examples, also creating an empty structure inside the loop but it does not work. Also, it seems save() cannot be executed inside a parfor loop.
Is there some workaround or method to accomplish my idea?
version_of_mpc='v5_2';
for days_horizon=1:7
days_horizon
for minutes_samplingtime=3:6
minutes_samplingtime
parfor cost_of_benefit_index=1:30
cost_of_benefit=cost_of_benefit_index*10;
outputfile_name1=['results_Hp' num2str(days_horizon,'%.2f') 'days_Ts' num2str(minutes_samplingtime,'%.2f') 'min_weightCost' num2str(cost_of_benefit,'%.2f') '_FEAS.fig'];
outputfile_name2=['results_Hp' num2str(days_horizon,'%.2f') 'days_Ts' num2str(minutes_samplingtime,'%.2f') 'min_weightCost' num2str(cost_of_benefit,'%.2f') '_INFEAS.fig'];
outputfile_name3='results.mat';
full_name1=fullfile(['figures directory...' version_of_mpc],outputfile_name1);
full_name2=fullfile(['same figures directory...' version_of_mpc],outputfile_name2);
full_name3=fullfile(['Workspace output file directory...' version_of_mpc],outputfile_name3);
cost_of_benefit
if not (isfile(full_name1) || isfile(full_name2)) % checking what simulations are done
[Xhist, Uhist_corrected, Uhist_controller, cost_prod_hist, pgrid_hist, i, percent, irradiation_plt, P_pv_plt, Price_energy_plt, CPU_time, total_simulation_time]=mpc_sim(minutes_samplingtime,days_horizon,cost_of_benefit);
plot_and_save_v4(Xhist, Uhist_corrected, Uhist_controller, cost_prod_hist, pgrid_hist, i, percent, irradiation_plt, P_pv_plt, Price_energy_plt, CPU_time, total_simulation_time,minutes_samplingtime,days_horizon,cost_of_benefit,version_of_mpc,full_name3);
first_lvl_name='FEAS'
if percent < 100
first_lvl_name='INFEAS'
end
results.(version_of_mpc).('ALL').(['Hp_' num2str(days_horizon,'%.0f') 'days']).(['Ts_' num2str(minutes_samplingtime,'%.0f') 'min']).(['weightCost_' num2str(cost_of_benefit,'%.0f')]).('Xhist')=Xhist;
results.(version_of_mpc).('ALL').(['Hp_' num2str(days_horizon,'%.0f') 'days']).(['Ts_' num2str(minutes_samplingtime,'%.0f') 'min']).(['weightCost_' num2str(cost_of_benefit,'%.0f')]).('Uhist_corrected')=Uhist_corrected;
results.(version_of_mpc).('ALL').(['Hp_' num2str(days_horizon,'%.0f') 'days']).(['Ts_' num2str(minutes_samplingtime,'%.0f') 'min']).(['weightCost_' num2str(cost_of_benefit,'%.0f')]).('Uhist_controller')=Uhist_controller;
results.(version_of_mpc).('ALL').(['Hp_' num2str(days_horizon,'%.0f') 'days']).(['Ts_' num2str(minutes_samplingtime,'%.0f') 'min']).(['weightCost_' num2str(cost_of_benefit,'%.0f')]).('cost_prod_hist')=cost_prod_hist;
results.(version_of_mpc).('ALL').(['Hp_' num2str(days_horizon,'%.0f') 'days']).(['Ts_' num2str(minutes_samplingtime,'%.0f') 'min']).(['weightCost_' num2str(cost_of_benefit,'%.0f')]).('pgrid_hist')=pgrid_hist;
results.(version_of_mpc).('ALL').(['Hp_' num2str(days_horizon,'%.0f') 'days']).(['Ts_' num2str(minutes_samplingtime,'%.0f') 'min']).(['weightCost_' num2str(cost_of_benefit,'%.0f')]).('i')=i;
results.(version_of_mpc).('ALL').(['Hp_' num2str(days_horizon,'%.0f') 'days']).(['Ts_' num2str(minutes_samplingtime,'%.0f') 'min']).(['weightCost_' num2str(cost_of_benefit,'%.0f')]).('percent')=percent;
results.(version_of_mpc).('ALL').(['Hp_' num2str(days_horizon,'%.0f') 'days']).(['Ts_' num2str(minutes_samplingtime,'%.0f') 'min']).(['weightCost_' num2str(cost_of_benefit,'%.0f')]).('irradiation_plt')=irradiation_plt;
results.(version_of_mpc).('ALL').(['Hp_' num2str(days_horizon,'%.0f') 'days']).(['Ts_' num2str(minutes_samplingtime,'%.0f') 'min']).(['weightCost_' num2str(cost_of_benefit,'%.0f')]).('P_pv_plt')=P_pv_plt;
results.(version_of_mpc).('ALL').(['Hp_' num2str(days_horizon,'%.0f') 'days']).(['Ts_' num2str(minutes_samplingtime,'%.0f') 'min']).(['weightCost_' num2str(cost_of_benefit,'%.0f')]).('Price_energy_plt')=Price_energy_plt;
results.(version_of_mpc).('ALL').(['Hp_' num2str(days_horizon,'%.0f') 'days']).(['Ts_' num2str(minutes_samplingtime,'%.0f') 'min']).(['weightCost_' num2str(cost_of_benefit,'%.0f')]).('CPU_time')=CPU_time;
results.(version_of_mpc).('ALL').(['Hp_' num2str(days_horizon,'%.0f') 'days']).(['Ts_' num2str(minutes_samplingtime,'%.0f') 'min']).(['weightCost_' num2str(cost_of_benefit,'%.0f')]).('total_simulation_time')=total_simulation_time;
results.(version_of_mpc).(first_lvl_name).(['Hp_' num2str(days_horizon,'%.0f') 'days']).(['Ts_' num2str(minutes_samplingtime,'%.0f') 'min']).(['weightCost_' num2str(cost_of_benefit,'%.0f')]).('Xhist')=Xhist;
results.(version_of_mpc).(first_lvl_name).(['Hp_' num2str(days_horizon,'%.0f') 'days']).(['Ts_' num2str(minutes_samplingtime,'%.0f') 'min']).(['weightCost_' num2str(cost_of_benefit,'%.0f')]).('Uhist_corrected')=Uhist_corrected;
results.(version_of_mpc).(first_lvl_name).(['Hp_' num2str(days_horizon,'%.0f') 'days']).(['Ts_' num2str(minutes_samplingtime,'%.0f') 'min']).(['weightCost_' num2str(cost_of_benefit,'%.0f')]).('Uhist_controller')=Uhist_controller;
results.(version_of_mpc).(first_lvl_name).(['Hp_' num2str(days_horizon,'%.0f') 'days']).(['Ts_' num2str(minutes_samplingtime,'%.0f') 'min']).(['weightCost_' num2str(cost_of_benefit,'%.0f')]).('cost_prod_hist')=cost_prod_hist;
results.(version_of_mpc).(first_lvl_name).(['Hp_' num2str(days_horizon,'%.0f') 'days']).(['Ts_' num2str(minutes_samplingtime,'%.0f') 'min']).(['weightCost_' num2str(cost_of_benefit,'%.0f')]).('pgrid_hist')=pgrid_hist;
results.(version_of_mpc).(first_lvl_name).(['Hp_' num2str(days_horizon,'%.0f') 'days']).(['Ts_' num2str(minutes_samplingtime,'%.0f') 'min']).(['weightCost_' num2str(cost_of_benefit,'%.0f')]).('i')=i;
results.(version_of_mpc).(first_lvl_name).(['Hp_' num2str(days_horizon,'%.0f') 'days']).(['Ts_' num2str(minutes_samplingtime,'%.0f') 'min']).(['weightCost_' num2str(cost_of_benefit,'%.0f')]).('percent')=percent;
results.(version_of_mpc).(first_lvl_name).(['Hp_' num2str(days_horizon,'%.0f') 'days']).(['Ts_' num2str(minutes_samplingtime,'%.0f') 'min']).(['weightCost_' num2str(cost_of_benefit,'%.0f')]).('irradiation_plt')=irradiation_plt;
results.(version_of_mpc).(first_lvl_name).(['Hp_' num2str(days_horizon,'%.0f') 'days']).(['Ts_' num2str(minutes_samplingtime,'%.0f') 'min']).(['weightCost_' num2str(cost_of_benefit,'%.0f')]).('P_pv_plt')=P_pv_plt;
results.(version_of_mpc).(first_lvl_name).(['Hp_' num2str(days_horizon,'%.0f') 'days']).(['Ts_' num2str(minutes_samplingtime,'%.0f') 'min']).(['weightCost_' num2str(cost_of_benefit,'%.0f')]).('Price_energy_plt')=Price_energy_plt;
results.(version_of_mpc).(first_lvl_name).(['Hp_' num2str(days_horizon,'%.0f') 'days']).(['Ts_' num2str(minutes_samplingtime,'%.0f') 'min']).(['weightCost_' num2str(cost_of_benefit,'%.0f')]).('CPU_time')=CPU_time;
results.(version_of_mpc).(first_lvl_name).(['Hp_' num2str(days_horizon,'%.0f') 'days']).(['Ts_' num2str(minutes_samplingtime,'%.0f') 'min']).(['weightCost_' num2str(cost_of_benefit,'%.0f')]).('total_simulation_time')=total_simulation_time;
% i don't save any .mat file, i don't know how.
% i even tried evalin inside of the plot_and_save function in
% wich saving the figure works, but neither the creation of the
% structure nor saving the .mat file worked
end
end
end
end
%system('shutdown -s');
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Pol Cardona Rubio
Pol Cardona Rubio 2024-4-8
For anyone interested. The best approach I found that works very well with parallel simulations is to create an exportAndPlot(results) function that is called at each independent iteration of the parfor loop. Nevertheless, the path or the name of the .mat and figures/pdfs/etc exported is a variable that is assigned at each iteration of the parfor (with some specific name code for you to understand later, but strictly different and unique at each iteration), this variable is passed to exportAndPlot(results,specificName). where you organize each simulation data and figures as needed and export results with the specific naming. This way you are able to generate in a parallel manner files with independent and custom organization of results inside a parfor loop. Then when the parfor loop is finalized you just go through all .mat independent test results and unify them in one unique .mat file to be able to analyze easily. When unifying, add columns to the results table that respresent the specific set of parameters of each test to always keep track of the specific test setup. Also, to not repeat already finalized test runs if the overall tests crashes or for whatever other reason you have to repeat the main.m execution, only run the simulation.m file inside the parfor loop if and only if the corresponding .mat results file at the specific path does not exist yet, this is implemented with an if statement. This methodology could be a very useful to be integrated by Matlab, where a simulation.m file, an exportAndPlot.m, a customNaming.m and customPath.m files are selected and hence complex simulations can be executed in parallel easily with modularity and custom results output organization.

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