Difference between revisions of "Parallelize msmf corr coeff.m"
| Line 25: | Line 25: | ||
end | end | ||
| − | For large R, monte_M (hence large M), and mktsize, computing epsimR12 is taking more than 50% time of msmf_corr_coeff.m. | + | For large R, monte_M (hence large M), and mktsize, computing epsimR12 is taking more than 50% time of msmf_corr_coeff.m. Each call is taking no more than 2 seconds, but for a large M it will take a long time in total. To improve this, we use a parfor to paralellize this part. |
| + | <br> | ||
[[FILE:epsimR12.png]] | [[FILE:epsimR12.png]] | ||
| + | <br> | ||
| + | New code: | ||
| + | epsim_cell = cell(M,1); | ||
| + | parfor m = 1 : M | ||
| + | cholA = chol(exchsig(N1(m)-1, N2(m)-1, th)); | ||
| + | epsim12 = zeros(N1(m), N2(m), R); | ||
| + | epsimR12 = reshape(mtimesx(repmat(cholA', [1 1 R]), reshape(epsimR(psa(m)+1 : psa(m)+(N1(m)-1)*(N2(m)-1), :, :), [(N1(m)-1)*(N2(m)-1) 1 R])), [N1(m)-1 N2(m)-1 R]); | ||
| + | epsim12(2:end, 2:end, :) = epsimR12; | ||
| + | epsim_cell{m} = reshape(epsim12, [N1(m)*N2(m) R]); | ||
| + | end | ||
| + | for m = 1 : M | ||
| + | f(psa(m)+1 : psa(m+1), :) = f(psa(m)+1 : psa(m+1), :) - epsim_cell{m}; | ||
| + | end | ||
| + | |||
| + | Using parfor on a 12-cores machine gives a four time speed up for computing msmf_corr_coeff.m (apology for not having a profiling screenshot here). Note that to avoid data-racing, in each iteration m, we stored epsim12 into epsim_cell, and compute f using another for loop. | ||
Revision as of 11:26, 20 July 2018
| Parallelize msmf corr coeff.m | |
|---|---|
| Project Information | |
| Project Title | Parallelize msmf corr coeff.m |
| Owner | Wei Wu |
| Start Date | 2018-07-09 |
| Deadline | |
| Keywords | Matlab, parallel computing, CPU |
| Primary Billing | |
| Notes | |
| Has project status | Active |
| Is dependent on | Estimating Unobserved Complementarities between Entrepreneurs and Venture Capitalists Matlab Code |
| Copyright © 2016 edegan.com. All Rights Reserved. | |
This page documents all changes that Wei made to the Matlab code for Matching Entrepreneurs to VC. It also explains the decisions Wei made, and where further improvement might be achieved in the future.
Changes made to the code
To speed up the valuation of the objective function used by ga, Wei parallelized a couple of lines in msmf_corr_coeff.m.
Around line 80 of msmf_corr_coeff
Original code:
for m = 1 : M cholA = chol(exchsig(N1(m)-1, N2(m)-1, th)); epsim12 = zeros(N1(m), N2(m), R); epsimR12 = reshape(mtimesx(repmat(cholA', [1 1 R]), reshape(epsimR(psa(m)+1: psa(m)+(N1(m)-1)*(N2(m)-1), :, :), [(N1(m)-1)*(N2(m)-1) 1 R])), [N1(m)-1 N2(m)-1 R]); epsim12(2:end, 2:end, :) = epsimR12; f(psa(m)+1 : psa(m+1), :) = f(psa(m)+1 : psa(m+1), :) - reshape(epsim12, [N1(m)*N2(m) R]) end
For large R, monte_M (hence large M), and mktsize, computing epsimR12 is taking more than 50% time of msmf_corr_coeff.m. Each call is taking no more than 2 seconds, but for a large M it will take a long time in total. To improve this, we use a parfor to paralellize this part.
New code:
epsim_cell = cell(M,1);
parfor m = 1 : M
cholA = chol(exchsig(N1(m)-1, N2(m)-1, th));
epsim12 = zeros(N1(m), N2(m), R);
epsimR12 = reshape(mtimesx(repmat(cholA', [1 1 R]), reshape(epsimR(psa(m)+1 : psa(m)+(N1(m)-1)*(N2(m)-1), :, :), [(N1(m)-1)*(N2(m)-1) 1 R])), [N1(m)-1 N2(m)-1 R]);
epsim12(2:end, 2:end, :) = epsimR12;
epsim_cell{m} = reshape(epsim12, [N1(m)*N2(m) R]);
end
for m = 1 : M
f(psa(m)+1 : psa(m+1), :) = f(psa(m)+1 : psa(m+1), :) - epsim_cell{m};
end
Using parfor on a 12-cores machine gives a four time speed up for computing msmf_corr_coeff.m (apology for not having a profiling screenshot here). Note that to avoid data-racing, in each iteration m, we stored epsim12 into epsim_cell, and compute f using another for loop.
