How to save and load data in parallel while optimizing?

Hello every one,

I am optimizing 30 equations with Genetic Algorithm and need to save data in each time step and load it in the next time step. This makes my optimization too long, which is not efficient. I ran it in parallel, but it messed up the data. Would you help me with solving this issue if you have any similar experiences?

here is the part of the code that shows how I save data and reload them:

              Data = [t ; y(15) ; y(22) ; y(29) ;  y(30)];    
      if t ==0
      fileID = fopen('Data.txt','w');        fprintf(fileID, '%f %f %f %f %f \n', Data);       fclose(fileID);
      else
      fileID = fopen('Data.txt','a');        fprintf(fileID, '%f %f %f %f %f \n', Data);       fclose(fileID);
      end 
                   if t >Tau_D
                   Data = load ('Data.txt','Data');    
                  for i = 1 : size (Data,1)-1
                      if t - Tau_D  < Data(i+1,1) && t - Tau_D  >= Data(i,1)
                          CD = Data(i,2);  
                      end
                  end
                end
           if t > Tau_t
                for i = 1 : size (Data,1)-1
                      if t - Tau_t  < Data(i+1,1) && t - Tau_t  >= Data(i,1)
                  CD8T = Data(i,3);
                  IgM = Data(i,4);
                  IgG = Data(i,5);
                       end
                  end
            end 

[Raymond Norris]

I'd be best to post your code, using the code formatting, which i've done here for you.

Data = [t ; y(15) ; y(22) ; y(29) ;  y(30)];
if t == 0
    fileID = fopen('Data.txt','w');        fprintf(fileID, '%f %f %f %f %f \n', Data);       fclose(fileID);
else
    fileID = fopen('Data.txt','a');        fprintf(fileID, '%f %f %f %f %f \n', Data);       fclose(fileID);
end
if t > Tau_D
    Data = load ('Data.txt','Data');
    for i = 1 : size (Data,1)-1
        if t - Tau_D  < Data(i+1,1) && t - Tau_D  >= Data(i,1)
            CD = Data(i,2);
        end
    end
end
if t > Tau_t
    for i = 1 : size (Data,1)-1
        if t - Tau_t  < Data(i+1,1) && t - Tau_t  >= Data(i,1)
            CD8T = Data(i,3);
            IgM = Data(i,4);
            IgG = Data(i,5);
        end
    end
end

Assigning t, y, Tau_D, and Tau_t to random valaues, doesn't show any issues. You should post what issue you're running into as well.

[Mohammad Sami]

If your processing of timestep t+1 is dependent on the output of t, i don't see how you can parallelize your code.

To parallelize ideally you will need steps that can be undertaken independently of each other.

I also do not understand why you need to save and load data on every iteration.

[Hamideh Hayati]

Thanks Mohammad for your response.

I need to save my data since I need them later. The entire time for my calculation is 10 days and after day 2 I need to use data of 2 days ago. That's why I need to save them and load them. If I didn't need to save data, parallelization would work fine. But I need to save them and feed the code with them later on.

[Hamideh Hayati]

Reymond,

Yes, you are right. The code doesn't have any issue. The problem is when I want to optimize it. Since I need to load data in each time step, it takes about 10 hours for each iteration to be done (in series). When I run it in parallel, the data in .txt file get messed up and disorganized. My question was; is there anyway I can run the optimization in parallel without data in data.txt getting messed up?

[Hamideh Hayati]

Here is my code.

When I want to optimize it using GA each time step takes more than 10hrs! Is there anyway I can run all the optimization in parallel, but the part that I save the data?

% clear
% clc
% x = [0.0000300000000000000;0.100000000000000;0.100000000000000;4.00000000000000e-05;3;0.0300000000000000;0.00100000000000000;3.00000000000000e-05;0.300000000000000;3.00000000000000e-05;0.0200000000000000;0.000400000000000000;0.00400000000000000;4.00000000000000e-05;0.0100000000000000;0.00200000000000000;0.00200000000000000;0.0300000000000000;0.00100000000000000;10;250;0.750000000000000;1.00000000000000e-05;1.75000000000000;150;2;5000;2.50000000000000;2;4;125;0.0100000000000000;3;4500;2.25000000000000;1.25000000000000;4;15000;0.00300000000000000;3;1;5000;1;0.0100000000000000;0.00100000000000000;0.150000000000000;0.0400000000000000;0.0800000000000000;2;0.400000000000000;1];
% %  
% Funcs1(x)
function OBJs = Funcs(x)
%% Experimental values
V_titer = [
0.9835	37726.56787
1.01926	6062.2564
1.05501	17790.83495
1.12976	632.66712
1.12976	3354.94803
1.20126	350.12793
1.20126	3538.77773
1.27276	13559.7835
1.27276	58088.92462
1.41577	1856.68101
1.41577	17790.83495
1.41577	587110.51785
1.45152	342719.52373
1.45152	169648.19036
1.88703	342719.52373
1.89678	3413869.87967
1.89678	2.03378E7
1.91628	6138882.6509
2.32579	5.39061E7
2.3648	5653078.70558
2.40055	3236528.79993
2.77756	6.20463E7
2.77756	3.27119E7
2.81331	1.92813E7
2.83281	2249672.66094
3.20657	3315966.63643
3.24232	3.27119E7
3.26182	5791828.69661
3.63558	2811910.88586
3.67458	1.87284E7
3.67458	2.20856E7
3.71034	5962831.26565
3.71034	2185156.36199
4.1036	318674.13567
4.1231	640663.96252
4.1426	3618433.43002
4.15885	1.92813E7
4.57161	1852993.60195
4.57161	3236528.79993
4.98112	1748236.95593
5.02012	3315966.63643
5.03962	1.0415E7
5.41339	366796.1254
5.46864	2185156.36199
5.48814	589964.65805
5.89765	193381.48179
5.89765	61569.68899
6.36566	58088.92462
6.36566	39409.64429
6.36566	19040.66992
6.75892	6425.51473
7.22694	617.51084
7.26269	186.39282
8.16297	582.60065
8.16297	353.54039
9.00474	0.50228
9.00474	1.07029
9.05999	16.90021
9.95702	0.57813
9.99602	3.54582];

E_I = [0.12054	112.36703
0.76203	397.13355
1.36102	1403.57055
1.78996	4616.88557
2.43145	13393.49947
2.90291	38622.50622
3.45938	145788.19554
4.01586	393626.6632
4.57233	931704.47044
5.34521	2515592.70917
6.11422	3756162.10501
7.01463	2886738.01875
7.65612	1586904.00524
8.46764	547023.14531
8.9816	177613.13371
9.62309	57325.24033
10.17957	18612.95205
10.86743	6007.3933
11.4239	1826.29614
11.934	589.44326
12.49048	218.31313
13.09332	57.83596
13.6498	22.8781
13.81983	12.5766];

M = [
    3 165e3
    6 224e3];

NK = [0.11893	99925.13276
2.10808	193851.60036
4.05151	283357.9989
6.07496	585303.9668
7.94217	475631.00316];

CD4_T = [
1.14024	1603582.83354
3.05396	838440.05876
4.93698	357632.72734
6.69719	676308.22237
8.5802	748776.76697
10.43252	613160.67874
12.20296	107027.21498];

CD8_T_E = [
1.45309515856432	284.835318872487
1.93755389144806	284.835318872487
2.66424199077368	357.688556440946
3.73408835922528	211.982081303999
4.62226270284547	211.982081303999
5.4498797048552	211.982081303999
6.09582468203352	357.688556440946
7.00418480619054	19973.4227717537
7.08492792833783	649.101506714869
7.24641417263241	5147.78892656841
7.65012978336886	66.275606167037
7.65012978336886	5384.56194866596
7.73087290551616	11868.5000922605
7.73087290551616	12742.7389430822
8.05384539410532	1541.55366692872
8.47774678537859	10976.0479320466
8.47774678537859	3381.09791553281
8.55848990752588	12979.5119651798
8.96220551826234	37822.4659760309
9.12369176255692	42321.1533958844
9.60815049544066	51901.3541361393
9.68889361758795	57875.3196167545
10.3550243753031	30446.0756722225
10.6779968638923	11121.7544071836
10.8394831081868	18133.8785231496
11.0009693524814	21448.7008325154
11.0817124746287	6404.50727462464
11.162455596776	10320.3687939303
11.5863569880493	19827.7162966168
11.6671001101966	20938.7281695361
11.8285863544912	12087.0598049659
12.070815720933	9136.50368344258
12.5552744538168	25218.8558766841
12.9589900645532	7078.39972213308
13.1204763088478	6258.80079948768
14.7555245323304	4127.84360060969
14.8362676544777	5384.56194866596
16.5520590001076	2124.37956747654
16.9759603913809	1541.55366692872
20.5690293269354	1395.84719179176
20.9727449376718	2798.27201498498
28.3203690530752	139.128843735525
28.7442704443485	66.275606167037
35.4459495825736	60
79.8950383246571	503.395031577908];

D_m = [
1.00468	256170.3902
2.72837	312452.41906
4.28293	696193.52496
5.8858	1446620.57649
7.31953	726892.81343
8.99491	651850.10828];

IgM_E = [0.25689	15.12029
1.36648	15.12029
1.70145	26.18941
1.95268	13.97025
2.89479	14.54527
4.00438	3.47615
4.00438	26.76443
4.92555	13.97025
5.09304	19.14543
5.26053	27.91447
5.42801	59.39679
5.61643	38.40858
5.78392	97.20432
5.86766	32.51463
5.86766	19.14543
5.86766	53.50284
6.11889	83.26009
6.20263	121.64264
6.20263	7.50128
6.20263	111.72356
6.20263	162.90029
6.20263	169.36926
6.20263	74.49105
6.20263	13.97025
6.28638	176.84451
6.28638	25.61439
6.37012	192.08253
6.37012	214.22078
6.37012	41.28367
6.45386	157.72511
6.45386	64.57196
6.53761	58.82177
6.53761	69.17212
6.62135	136.7369
6.62135	129.83667
6.62135	152.40618
6.62135	113.44862
6.87258	146.08097
6.87258	220.54599
6.95632	176.26949
6.95632	105.97336
7.04006	77.36615
7.04006	95.47926
7.04006	190.35747
7.04006	196.10766
7.12381	89.58531
7.20755	206.60177
7.31223	225.86492
7.31223	246.13436
7.31223	253.75336
7.39597	154.275
7.39597	185.03854
7.39597	167.6442
7.39597	146.08097
7.47971	154.85002
7.56346	211.77694
7.56346	301.47998
7.56346	140.18702
7.56346	220.54599
7.6472	310.82404
7.6472	200.70782
7.73094	278.19169
7.81469	229.31504
7.89843	136.16188
7.98217	190.35747
7.98217	211.77694
8.06591	291.56089
8.14966	287.53576
8.14966	222.84607
8.14966	259.50356
8.2334	296.16105
8.40089	128.54288
8.40089	183.8885
8.48463	247.85941
8.48463	237.36531
8.48463	154.85002
8.48463	144.35591
8.56837	284.5169
8.56837	200.70782
8.56837	210.05188
8.65211	118.76755
8.65211	114.02364
8.73586	187.33862
8.8196	231.04009
8.8196	266.54755
8.90334	168.21922
8.90334	161.75025
8.98709	225.14615
8.98709	243.25926
8.98709	173.39439
9.09176	308.38021
9.09176	176.26949
9.09176	179.86336
9.25925	105.97336
9.25925	116.32371
9.25925	127.39284
9.25925	192.08253
9.34299	125.09276
9.34299	187.91364
9.34299	150.10611
9.42674	139.612
9.42674	179.28834
9.42674	198.98276
9.51048	144.35591
9.51048	101.80447
9.59422	172.81937
9.59422	120.4926
9.59422	158.87515
9.59422	192.65755
9.92919	185.61356
9.92919	208.90185
10.01294	247.28439];

IgG_E = [1.1848	5.72282
1.81757	5.72282
2.34124	5.72282
2.86491	5.72282
3.41039	4.24916
3.84679	4.24916
4.45773	4.24916
5.26506	2.77551
5.35234	2.77551
6.22512	5.72282
6.57423	17.51208
6.74879	24.88037
6.74879	37.03805
7.46884	57.66926
7.9925	80.14255
8.4289	141.29935
8.4289	125.08911
8.51617	99.66851
8.51617	227.87675
8.79983	265.08662
8.88711	153.45703
8.88711	205.40347
9.23622	280.19161
9.23622	174.08824
9.3235	238.19236
9.49806	492.02994
9.58533	221.98212
9.58533	463.66202
9.75989	238.19236
9.93445	436.76777
9.93445	187.71958
10.02173	351.66402];
%% Parameters
    beta_E = x(1);  
                         
                             
    phi = x(2);
    delta_ER = x(3);
    %----------------------ODE #2-----------------------------------------%
    %----------------------ODE #3-----------------------------------------%

      c_K = x(4);
      kappa_E = x(5);
    %----------------------ODE #4-----------------------------------------%
   %epsilon_1 = 0;              
    pi_V = x(6);                  
        %               
           %            
    pi_M_I = x(7);              
   c_M = x(8);
    c_IgM = x(9);7.8e-2; 
    c_IgG = x(10);4.5e-8;
    %---------------------ODE #5------------------------------------------%
    % epsilon_2 = 0;                 
    alpha =x(11);%1.8e-2;              
     V_50 = x(12);%3.9e-4;           
%     delta_M_R = 0.04;       
%     ---------------------ODE #6------------------------------------------%
    lambda = x(13);%2.2e-3;       
%     delta_M_A = 0.04;      
    %---------------------ODE #7------------------------------------------%
%     delta_M_I = 0.04;         
   % ---------------------ODE #8------------------------------------------%
    pi_F = x(14);                  
    delta_F = x(15);              
    %---------------------ODE #9------------------------------------------%
%     S_k  = 5e5;
   delta_k = x(16);
    phi_k = x(17);
 
     %---------------------ODE #11------------------------------------------%
%     S_k  = 5e5;
    beta_D = x(18);
    delta_D = x(19);
      %---------------------ODE #12------------------------------------------%
    delta_D_I = x(20);
    % ---------------------ODE #13------------------------------------------%
    K_D = x(21);
%     tau_D = 3;                  %x(29);
    delta_DM = x(22);
     %---------------------ODE #14------------------------------------------%
     delta_HN = x(23);
     pi_H1 = x(24);  pi_H2 = x(25);
     %---------------------ODE #15------------------------------------------%
     rho_H1 = x(26);  rho_H2 = x(27);
     delta_H1 = x(28); delta_H2 = x(29);
     %---------------------ODE #16------------------------------------------%
     delta_TN = x(30);
     pi_T1 = x(31); pi_T2 = x(32);
     %---------------------ODE #17------------------------------------------%
     rho_T1 = x(33); rho_T2 = x(34);
     delta_T1 = x(35); delta_T2 = x(36);
     pi_B1 =  x(37);3; pi_B2 = x(38);1e4;
     delta_B = x(39);2e-3;
     rho_B1 = x(40);1.5;2.6;h = x(41);0; rho_B2 = x(42);4e3;
     delta_BA = x(43);0.1;0.9; pi_S = x(44);1e-3; pi_L = x(45);1e-3;8e-9;
     delta_S = x(46);0.1;
     delta_L = x(47);3e-2;
      pi_AS = x(48);6e-1; pi_AL = x(49);2e-1; delta_AS =x(50);2; delta_AL =x(51);2;
    
    
%% ICs
initial_val(1) = 14e7;    
E0 = initial_val(1);
initial_val(2) = 0;        
initial_val(3) = 0;        
initial_val(4) = 1e2;      
initial_val(5) = 1e3;171e3;
initial_val(6) = 0;        
initial_val(7) = 0;        
initial_val(8) = 0;       
initial_val(9) = 0;        
initial_val(10) = 1e3;99925.13276;     
initial_val(11) = 0;      
initial_val(12) = 1e3;99925.13276;     
initial_val(13) = 0;       
initial_val(14) = 1e3;2e5;     
initial_val(15) = 0; 
initial_val(16) = 0;       
initial_val(17) = 1e3;
initial_val(18) = 1e3;
initial_val(19) = 0;      
initial_val(20) = 1e3;   
initial_val(21) = 1e3;
initial_val(22) = 0;        
initial_val(23) = 1e3;
initial_val(24) = 1e3;
initial_val(25) = 0                    ;  
initial_val(26) = 1e3;
initial_val(27) = 0                     ; 
initial_val(28) = 0                      ;     
initial_val(29) = 0;       
initial_val(30) = 0;       
initial_val(31) = 0;       
initial_val(32) = 0;       
initial_val(33) = 0;       


%% Main body
j = 1;
[t,y] = ode23t(@f,[0 10],initial_val,j);

plot(t,y)


%% ODEs
function dydt = f(t,y)
    Tau_t = 5;  Tau_D = 2;

    dydt = zeros(33,1);      

                    if t >Tau_D
                   Data = load ('Data.txt','Data');    
                  for i = 1 : size (Data,1)-1
                      if t - Tau_D  < Data(i+1,1) && t - Tau_D  >= Data(i,1)
                          CD = Data(i,2);  
                      end
                  end
                end
%              
            if t > Tau_t
                for i = 1 : size (Data,1)-1
                      if t - Tau_t  < Data(i+1,1) && t - Tau_t  >= Data(i,1)
                  CD8T = Data(i,3);
                  IgM = Data(i,4);
                  IgG = Data(i,5);
                       end
                  end
            end
            
                 Data = [t ; y(15) ; y(22) ; y(29) ;  y(30)];    
      if t ==0
      fileID = fopen('Data.txt','w');        fprintf(fileID, '%f %f %f %f %f \n', Data);       fclose(fileID);

      else
      fileID = fopen('Data.txt','a');        fprintf(fileID, '%f %f %f %f %f \n', Data);       fclose(fileID);

      end 
         
     delta_E_IL = 4;  
     delta_E_I = 2;
     epsilon_1 = 0;            c_V =1.5;     
     epsilon_2 = 0;          
     delta_M_A = 0.04;     
     delta_M_R = 0.04;     MR0=171e3;
     delta_M_I = 0.04;   
     NK0 = 99925.13276;  CD0 =0;2e5; HN0 = 1e3;357632.72734; TN0 = 1e3;95289.89712; BN0 = 1e3;95289.89712;
     
    dydt(1) = -beta_E*y(1)*y(4) -phi*y(1)*y(9) +delta_ER*y(13);     %d/dt E=-?_E VE -?FE+?_(E_R ) E_R-?_E E
    dydt(2) = beta_E*y(1)*y(4) -delta_E_IL*y(2);                   %d/dt E_IL=?_E VE -?_(E_IL ) E_IL-?_k E_IL K 
    if t>Tau_t
    dydt(3) = delta_E_IL*y(2) -delta_E_I*y(3) -c_K*y(11)*y(3) -kappa_E*CD8T*y(3);%;                                      %d/dt E_I=?_(E_IL ) E_IL-?_(E_I ) E_I-?_E E_I ?T_E (t-?_T)-?_k E_I K
    dydt(4) = (1-epsilon_1)*(pi_V*y(3)+ pi_M_I*y(7)) -c_V*y(4)  -c_M*y(6)*y(4) -c_IgM*IgM - c_IgG*IgG;         %d/dt V=(1-?_1)(?_V E_I+?_(M_I ) M_I)-c_V V-?_V VA(t)-?_E VE
    dydt(31) = CD8T;   dydt(32) = IgM;     dydt(33) = IgG;
    else
    dydt(3) =  delta_E_IL*y(2) -delta_E_I*y(3) -c_K*y(11)*y(3);                                  %d/dt E_I=?_(E_IL ) E_IL-?_(E_I ) E_I-?_E E_I ?T_E (t-?_T)-?_k E_I K
     dydt(4) = (1-epsilon_1)*(pi_V*y(3)+ pi_M_I*y(7)) -c_V*y(4)  -c_M*y(6)*y(4);        %d/dt V=(1-?_1)(?_V E_I+?_(M_I ) M_I)-c_V V-?_V VA(t)-?_E VE
    end
    %---------------------------------------------------------------%
     dydt(5) = delta_M_R*(MR0-y(5)) -(1-epsilon_2)*alpha*y(4)*y(5)/(V_50+y(4));                       %d/dt M_R=s-((1-?_2)?VM_R)/(V_50+V)-?_(M_R ) M_R
     dydt(6) = (1-epsilon_2)*alpha*y(4)*y(5)/(V_50+y(4)) -lambda*y(4)*y(6) -delta_M_A*y(6);      %d/dt M_A=((1-?_2)?VM_R)/(V_50+V)-?M_A V-?_(M_A ) M_A 
     dydt(7) = lambda*y(4)*y(6) -delta_M_I*y(7);                %d/dt M_I=?M_A V-?_(M_I ) M_I
     dydt(8) = y(6) + y(7);
     dydt(9) = pi_F*y(3) -delta_F*y(9);                                 %d/dt F=?_F M_A-?_F F
     dydt(10) =delta_k*(NK0-y(10)) -phi_k*y(9)*y(10);         %d/dt K=S_k+?_k E_I-?_k K
     dydt(11) = phi_k*y(9)*y(10) - delta_k*y(11);
     dydt(12) = y(10)+y(11);
     dydt(13) = phi*y(1)*y(9) -delta_ER*y(13);                      %d/dt E_R=?FE-?_ER E_R
     dydt(14) = -beta_D*y(4)*y(14) -delta_D*(y(14)-CD0);             %d/dt D=?_D D_0-?_D D-?_D DV
     dydt(15) = beta_D*y(4)*y(14) -delta_D_I*y(15);               %d/dt D_I=?_D DV-?_(D_I ) D_I
          if t > Tau_D
       K_D = 200; delta_DM = 5e-1;
     dydt(16) = K_D*CD -delta_DM*y(16);                         %d/dt D_M=k_D D_I (t-?_D)-?_DM D_M
     dydt(17) = y(15) +y(16);
      pi_H1 = 1.5;    pi_H2 = 1e2;
     pi_H = pi_H1*y(16)/(y(16)+pi_H2);
      delta_HN = 2e-3; %---------delet
     dydt(18) = delta_HN*(HN0-y(18)) -pi_H*y(18);            %d/dt H_N=?_HN H_N0-?_HN H_N-?_H ?(D?_M)H_N
      rho_H1 = 1.51;    rho_H2 = 4e3;
     rho_H = rho_H1*y(16)/(y(16)+rho_H2);
      delta_H1 = 0.4;   delta_H2 = 1;
     delta_H = delta_H1*y(16)/(y(16)+delta_H2);
     dydt(19) = pi_H*y(18) +rho_H*y(19) -delta_H*y(19);     %d/dt H_E=?_H (D_M ) H_N+?_H (D_M)H_E-?_H (D_M)H_E
     dydt(20) = y(18)+y(19);
      pi_T1 = 3;     pi_T2 = 1e2;
     pi_T = pi_T1*y(16)/(y(16)+pi_T2);
       delta_TN = 2e-3;
     dydt(21) = delta_TN*(TN0-y(21)) -pi_T*y(21);                %d/dt T_N=?_(T_N ) T_N0-?_(T_N ) T_N-?_T ?(D?_M)T_N
      rho_T1 = 2.6;    rho_T2 = 4e3;
     rho_T =  rho_T1*y(16)/(y(16)+rho_T2);
      delta_T1 = 0.75;  delta_T2 = 1;
     delta_T = delta_T1*y(16)/(y(16)+delta_T2);
     dydt(22) = pi_T*y(21) +rho_T*y(22) -delta_T*y(22);        %d/dt T_E=?_T (D_M ) T_N+?_T (D_M)T_E-?_T (D_M)T_E
     dydt(23) = y(21) + y(22);
      pi_B1 =  3; pi_B2 = 1e4;
     pi_B = pi_B1*y(16)/(y(16)+pi_B2);
      delta_B = 2e-3;
     dydt(24) = delta_B*(BN0-y(24)) -pi_B*y(24);                   %d/dt B_N=?_B B_N0-?_B H_N-?_B ?(D?_M)B_N  
      rho_B1 = 1.5;2.6;h = 0; rho_B2 = 4e3;
     rho_BA = rho_B1*(y(16)+h*y(19))/(y(16)+h*y(19)+rho_B2);
      delta_BA = 0.1;0.9; pi_S = 1e-3; pi_L = 1e-3;8e-9;
     dydt(25) = pi_B*y(24) +rho_BA*y(25) -delta_BA*y(25)- pi_S*y(25) - pi_L*y(19)*y(25); 
     dydt(26) = y(24) +y(25);
      delta_S = 0.1;
     dydt(27) = pi_S*y(25) -delta_S*y(27);
      delta_L = 3e-2;
     dydt(28) = pi_L*y(19)*y(25) -delta_L*y(28);
      pi_AS = 6e-1; pi_AL = 2e-1; delta_AS =2; delta_AL =2;
     dydt(29) = pi_AS*y(27) -delta_AS*y(29);
     dydt(30) = pi_AL*y(28) -delta_AL*y(30);
     end
end
%% objectives
    DIFF1 = 1e+10;
    DIFF2 = 1e+10;
    DIFF3 = 1e+10;
    DIFF4 = 1e+10;
    
    tspan = 1e-4;
    
    for ii = 1: size(y,1)
        for iii = 1 : size(V_titer,1)
            if abs(V_titer(iii,1) - t(ii)) <= tspan
                DIFF1(iii,1) = V_titer(iii,2) - y(ii,4); 
            end
        end
       for iii = 1 : size(CD8_T_E,1)
            if abs(CD8_T_E(iii,1) - t(ii)) <= tspan
                DIFF2(iii,1) = CD8_T_E(iii,2) - y(ii,31); 
            end
       end
             for iii = 1 : size(IgM_E,1)
            if abs(IgM_E(iii,1) - t(ii)) <= tspan
                DIFF3(iii,1) = IgM_E(iii,2) - y(ii,32); 
            end
             end
                   for iii = 1 : size(IgG_E,1)
            if abs(IgG_E(iii,1) - t(ii)) <= tspan
                DIFF4(iii,1) = IgG_E(iii,2) - y(ii,33); 
            end
      end
         end

 OBJs (1)= sqrt(sum(DIFF1.^2)/size(V_titer,1)+sum(DIFF2.^2)/size(CD8_T_E,1))
OBJs(2) = sqrt(sum(DIFF3.^2)/size(IgM_E,1)+sum(DIFF4.^2)/size(IgG_E,1))
end

[Mohammad Sami]

I would suggest you need to centralise the reading and writing of the data to the file.

Multiple threads trying to read and write from the same file will cause errors.

Use the function parfeval to call the processing in the parallel and pass in all the data the function needs to work. Ideally your function will not read nor write to the file.

The functions will then compute the values on parallel processes. You can then fetch the results on the main thread and update your files.

More information is available in the documentation on how to use parfeval.

https://www.mathworks.com/help/releases/R2020a/parallel-computing/parallel.pool.parfeval.html

Answer 1

[Rohith Nomula]

Use parfor - parallel for loop in MATLAB

Then use a cell array to store all the values assigned in the looping state

% declare variables outside parallel for loop
parfor i=1:fileLength
%     your condition
    CD8T{i} = Data(i,3);
    IgM{i} = Data(i,4);
    IgG{i} = Data(i,5);
 end

This works well for optimization

You can use it in both for loops

[Hamideh Hayati]

Thanks Rohith,

But it is the loading data that is taking too long, not assigning data to the variables.