In order to speed up the calculating, how to combine the use of command parallel and command tuples?

Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input float(iv1 iv2) byte firmAge float(lnEmplNum lnEmplNumRD) byte dv int firmID float mod
 -1.0632818  -.3917133 4  3.135494  2.944439 0 284 .
 -2.0044324   -2.97516 4  3.135494  2.944439 0 284 .
  -.7495648  -.3917133 4  3.135494  2.944439 0 284 .
  -.5236522   -.855022 4  3.135494  2.944439 0 284 .
 -.27984554   -.855022 4  3.135494  2.944439 0 284 .
   .6621613  .12497605 2 4.4886365   3.73767 1 787 .
  -.5236522  .13863796 2 4.4886365   3.73767 1 787 .
  -.4358479   .6416654 2 4.4886365   3.73767 1 787 .
   .4515744   -.358192 2 4.4886365   3.73767 1 787 .
   .6621613   .6416654 2 4.4886365   3.73767 1 787 .
 -.20056024  -.3917133 5 3.8286414   1.94591 0 426 .
 .034727458  -.9084026 5 3.8286414   1.94591 0 426 .
  -.7674588   -.855022 5 3.8286414   1.94591 0 426 .
  -2.945583 -3.4918494 5 3.8286414   1.94591 0 426 .
  -1.255072 -1.8486818 5 3.8286414   1.94591 0 426 .
   .9391876   .6354679 4  2.833213 2.6390574 0 621 .
   .3484444  1.1583548 4  2.833213 2.6390574 0 621 .
   -.122131  -.3917133 4  2.833213 2.6390574 0 621 .
  -.7674588 -1.8486818 4  2.833213 2.6390574 0 621 .
  1.0543075  1.1583548 4  2.833213 2.6390574 0 621 .
  .20776772  .38705295 1  5.214936  4.406719 0 930 .
 -1.0632818  -.3917133 1  5.214936  4.406719 0 930 .
 .034727458  .12497605 1  5.214936  4.406719 0 930 .
  -1.255072 -1.3518518 1  5.214936  4.406719 0 930 .
   .8190198  1.1583548 1  5.214936  4.406719 0 930 .
   .4515744   -.358192 1  3.218876  3.178054 0 783 .
  .20776772   -.855022 1  3.218876  3.178054 0 783 .
 -1.6907157 -1.9417813 1  3.218876  3.178054 0 783 .
  .50530285   .6416654 1  3.218876  3.178054 0 783 .
 -1.2201402  -.3917133 1  3.218876  3.178054 0 783 .
 -1.0632818  -1.425092 5 3.0445225  2.890372 0 986 .
 -1.7426853  .13863796 5 3.0445225  2.890372 0 986 .
 -.27984554   -.855022 5 3.0445225  2.890372 0 986 .
  -.4358479  .12497605 5 3.0445225  2.890372 0 986 .
   .3484444  .12497605 5 3.0445225  2.890372 0 986 .
   .9758782  -.9084026 2 2.1972246  1.609438 1 276 .
   1.426801  .13863796 2 2.1972246  1.609438 1 276 .
  1.4464536   .6416654 2 2.1972246  1.609438 1 276 .
   .9391876   -.358192 2 2.1972246  1.609438 1 276 .
   1.289595   .6416654 2 2.1972246  1.609438 1 276 .
 -1.0632818  -1.425092 4 1.7917595  1.609438 0 857 .
   .4515744   -.358192 4 1.7917595  1.609438 0 857 .
 -1.3769987  -.9084026 4 1.7917595  1.609438 0 857 .
    .329671   -.855022 4 1.7917595  1.609438 0 857 .
  1.1327367   .6416654 4 1.7917595  1.609438 0 857 .
   .6621613  1.1583548 4  2.772589   1.94591 1 440 .
   .4515744  .13863796 4  2.772589   1.94591 1 440 .
  1.4621392  1.6233754 4  2.772589   1.94591 1 440 .
  .20776772   .6354679 4  2.772589   1.94591 1 440 .
  1.4464536   .6416654 4  2.772589   1.94591 1 440 .
   1.426801   .6354679 2  3.713572  3.583519 0 430 .
   .6621613   .6416654 2  3.713572  3.583519 0 430 .
   .6621613  .12497605 2  3.713572  3.583519 0 430 .
  -.9064233  -.3917133 2  3.713572  3.583519 0 430 .
 -.27984554   -.855022 2  3.713572  3.583519 0 430 .
 -.27898946  .12497605 3 4.3438053  3.583519 1 866 .
   .6621613  .12497605 3 4.3438053  3.583519 1 866 .
   .8190198   .6416654 3 4.3438053  3.583519 1 866 .
   1.426801  2.1259577 3 4.3438053  3.583519 1 866 .
    .329671  .38705295 3 4.3438053  3.583519 1 866 .
   -.122131  1.1583548 2  4.934474   3.73767 1 292 2
  .20776772  2.1259577 2  4.934474   3.73767 1 292 2
  1.6706076  1.1322979 2  4.934474   3.73767 1 292 2
 -1.2201402 -1.9417813 2  4.934474   3.73767 1 292 2
   .9758782   .6416654 2  4.934474   3.73767 1 292 2
 -.27984554   -.855022 2 3.4011974   2.70805 0 374 .
  -1.986492 -1.8486818 2 3.4011974   2.70805 0 374 .
 -2.3181496  -.9084026 2 3.4011974   2.70805 0 374 .
 -2.1612911  -.9084026 2 3.4011974   2.70805 0 374 .
  -.9064233 -1.9417813 2 3.4011974   2.70805 0 374 .
  -.7495648   .6416654 2   4.59512 3.6888795 0 950 .
 -1.5338572  -1.425092 2   4.59512 3.6888795 0 950 .
-.036038913   .6354679 2   4.59512 3.6888795 0 950 .
   .3484444  -.9084026 2   4.59512 3.6888795 0 950 .
  -.5236522   -.855022 2   4.59512 3.6888795 0 950 .
 -.27898946   .6416654 2 3.3322046  3.218876 0 414 .
  1.1327367   .6416654 2 3.3322046  3.218876 0 414 .
 -1.0632818  -1.425092 2 3.3322046  3.218876 0 414 .
-.036038913  .13863796 2 3.3322046  3.218876 0 414 .
  -.5236522   .6354679 2 3.3322046  3.218876 0 414 .
-.036038913   .6354679 2  3.970292  3.433987 0  89 .
 .034727458   .6416654 2  3.970292  3.433987 0  89 .
  1.1829942  1.1322979 2  3.970292  3.433987 0  89 .
   -.122131  -1.425092 2  3.970292  3.433987 0  89 .
 -1.2201402 -1.9417813 2  3.970292  3.433987 0  89 .
 -.15794224   -.855022 3  4.304065  3.610918 0 333 .
  -.7495648  -.3917133 3  4.304065  3.610918 0 333 .
-.036038913  .13863796 3  4.304065  3.610918 0 333 .
   .1915859   .3833207 3  4.304065  3.610918 0 333 .
  -.4358479  .12497605 3  4.304065  3.610918 0 333 .
 -.27898946  -.9084026 5  2.995732 1.3862944 0  14 .
   .3484444 -1.9417813 5  2.995732 1.3862944 0  14 .
 -.27984554   .6354679 5  2.995732 1.3862944 0  14 .
   .9391876  1.1322979 5  2.995732 1.3862944 0  14 .
 -2.0044324  -.3917133 5  2.995732 1.3862944 0  14 .
  -.5236522   .6354679 2  3.496508 3.0910425 0 504 .
   .1915859  .12497605 2  3.496508 3.0910425 0 504 .
   .1915859  -.3917133 2  3.496508 3.0910425 0 504 .
  -.9064233  -.9084026 2  3.496508 3.0910425 0 504 .
  -.7674588 -2.3455117 2  3.496508 3.0910425 0 504 .
end


generate pp = .
generate pvalue = .
set obs 5000
tuples firmAge lnEmplNum lnEmplNumRD
cap prog drop parfor
prog def parfor
args pvalue
quietly forvalues i = 1/`ntuples' {
    logit dv c.iv1 c.iv2##c.mod  `tuple`i''     , clust(firmID) iterate(20)
    
     replace `pvalue' = el(r(table),4,4) in `i'
     }
end
timer on 1
parallel setclusters 2, f
parallel, prog(parfor): parfor pp
timer off 1
timer list 1