Commit 5c893f50 by Michel Juillard

### making extended path ready for parallel computing with parfor

parent 020328a8
 ... ... @@ -80,18 +80,22 @@ function [flag,endo_simul,err] = solve_stochastic_perfect_foresight_model(endo_s % each block of Y with ny rows are unfolded column wise dimension = ny*(sum(nnodes.^(0:order-1),2)+(periods-order)*world_nbr); if order == 0 i_upd = ny+(1:ny*periods); i_upd_r = (1:ny*periods); i_upd_y = i_upd_r + ny; else i_upd = zeros(dimension,1); i_upd(1:ny) = ny+(1:ny); i_upd_r = zeros(dimension,1); i_upd_y = i_upd_r; i_upd_r(1:ny) = (1:ny); i_upd_y(1:ny) = ny+(1:ny); i1 = ny+1; i2 = 2*ny; n1 = 2*ny+1; n2 = 3*ny; n1 = ny+1; n2 = 2*ny; for i=2:periods k = n1:n2; for j=1:nnodes^min(i-1,order) i_upd(i1:i2) = (n1:n2)+(j-1)*ny*(periods+2); i_upd_r(i1:i2) = (n1:n2)+(j-1)*ny*periods; i_upd_y(i1:i2) = (n1:n2)+ny+(j-1)*ny*(periods+2); i1 = i2+1; i2 = i2+ny; end ... ... @@ -99,12 +103,13 @@ function [flag,endo_simul,err] = solve_stochastic_perfect_foresight_model(endo_s n2 = n2+ny; end end icA = [find(lead_lag_incidence(1,:)) find(lead_lag_incidence(2,:))+world_nbr*ny ... find(lead_lag_incidence(3,:))+2*world_nbr*ny]'; h1 = clock; for iter = 1:maxit h2 = clock; A = sparse([],[],[],dimension,dimension,(periods+2)*world_nbr*nnz(jacobian)); res = zeros(dimension,1); A1 = sparse([],[],[],ny*(sum(nnodes.^(0:order-1),2)+1),dimension,(order+1)*world_nbr*nnz(jacobian)); res = zeros(ny,periods,world_nbr); i_rows = 1:ny; i_cols = find(lead_lag_incidence'); i_cols_p = i_cols(1:nyp); ... ... @@ -114,91 +119,85 @@ function [flag,endo_simul,err] = solve_stochastic_perfect_foresight_model(endo_s i_cols_Ap = i_cols_p; i_cols_As = i_cols_s; i_cols_Af = i_cols_f - ny; for i = 1:periods if i <= order+1 i_w_p = 1; for j = 1:nnodes^(i-1) innovation = exo_simul; if i > 1 innovation(i+1,:) = nodes(mod(j-1,nnodes)+1,:); end if i <= order for k=1:nnodes y = [Y(i_cols_p,i_w_p); Y(i_cols_s,j); Y(i_cols_f,(j-1)*nnodes+k)]; [d1,jacobian] = dynamic_model(y,innovation,params,steady_state,i+1); if i == 1 % in first period we don't keep track of % predetermined variables i_cols_A = [i_cols_As - ny; i_cols_Af]; A(i_rows,i_cols_A) = A(i_rows,i_cols_A) + weights(k)*jacobian(:,i_cols_1); else i_cols_A = [i_cols_Ap; i_cols_As; i_cols_Af]; A(i_rows,i_cols_A) = A(i_rows,i_cols_A) + weights(k)*jacobian(:,i_cols_j); end res(i_rows) = res(i_rows)+weights(k)*d1; i_cols_Af = i_cols_Af + ny; end else for i = 1:order+1 i_w_p = 1; for j = 1:nnodes^(i-1) innovation = exo_simul; if i > 1 innovation(i+1,:) = nodes(mod(j-1,nnodes)+1,:); end if i <= order for k=1:nnodes y = [Y(i_cols_p,i_w_p); Y(i_cols_s,j); Y(i_cols_f,j)]; Y(i_cols_f,(j-1)*nnodes+k)]; [d1,jacobian] = dynamic_model(y,innovation,params,steady_state,i+1); if i == 1 % in first period we don't keep track of % predetermined variables i_cols_A = [i_cols_As - ny; i_cols_Af]; A(i_rows,i_cols_A) = jacobian(:,i_cols_1); A1(i_rows,i_cols_A) = A1(i_rows,i_cols_A) + weights(k)*jacobian(:,i_cols_1); else i_cols_A = [i_cols_Ap; i_cols_As; i_cols_Af]; A(i_rows,i_cols_A) = jacobian(:,i_cols_j); A1(i_rows,i_cols_A) = A1(i_rows,i_cols_A) + weights(k)*jacobian(:,i_cols_j); end res(i_rows) = d1; res(:,i,j) = res(:,j,i)+weights(k)*d1; i_cols_Af = i_cols_Af + ny; end i_rows = i_rows + ny; if mod(j,nnodes) == 0 i_w_p = i_w_p + 1; end if i > 1 if mod(j,nnodes) == 0 i_cols_Ap = i_cols_Ap + ny; end i_cols_As = i_cols_As + ny; else y = [Y(i_cols_p,i_w_p); Y(i_cols_s,j); Y(i_cols_f,j)]; [d1,jacobian] = dynamic_model(y,innovation,params,steady_state,i+1); if i == 1 % in first period we don't keep track of % predetermined variables i_cols_A = [i_cols_As - ny; i_cols_Af]; A1(i_rows,i_cols_A) = jacobian(:,i_cols_1); else i_cols_A = [i_cols_Ap; i_cols_As; i_cols_Af]; A1(i_rows,i_cols_A) = jacobian(:,i_cols_j); end res(:,i,j) = d1; i_cols_Af = i_cols_Af + ny; end i_cols_p = i_cols_p + ny; i_cols_s = i_cols_s + ny; i_cols_f = i_cols_f + ny; elseif i == periods if i == order+2 i_cols_A = [i_cols_Ap; i_cols_As; i_cols_Af]; i_rows = i_rows + ny; if mod(j,nnodes) == 0 i_w_p = i_w_p + 1; end for j=1:world_nbr [d1,jacobian] = dynamic_model(Y(i_cols,j),exo_simul, ... params,steady_state,i+1); A(i_rows,i_cols_A(i_cols_T)) = jacobian(:,i_cols_T); res(i_rows) = d1; i_rows = i_rows + ny; i_cols_A = i_cols_A + ny; end else if i == order+2 i_cols_A = [i_cols_Ap; i_cols_As; i_cols_Af]; if i > 1 if mod(j,nnodes) == 0 i_cols_Ap = i_cols_Ap + ny; end i_cols_As = i_cols_As + ny; end for j=1:world_nbr [d1,jacobian] = dynamic_model(Y(i_cols,j), ... exo_simul,params,steady_state,i+1); A(i_rows,i_cols_A) = jacobian(:,i_cols_j); res(i_rows) = d1; i_rows = i_rows + ny; i_cols_A = i_cols_A + ny; end i_cols_p = i_cols_p + ny; i_cols_s = i_cols_s + ny; i_cols_f = i_cols_f + ny; end nzA = cell(periods,world_nbr); parfor j=1:world_nbr i_rows_y = find(lead_lag_incidence')+(order+1)*ny; offset_c = ny*(sum(nnodes.^(0:order-1),2)+j-1); offset_r = (j-1)*ny; for i=order+2:periods [d1,jacobian] = dynamic_model(Y(i_rows_y,j), ... exo_simul,params, ... steady_state,i+1); if i == periods [ir,ic,v] = find(jacobian(:,i_cols_T)); else [ir,ic,v] = find(jacobian(:,i_cols_j)); end nzA{i,j} = [offset_r+ir,offset_c+icA(ic), v]'; res(:,i,j) = d1; i_rows_y = i_rows_y + ny; offset_c = offset_c + world_nbr*ny; offset_r = offset_r + world_nbr*ny; end i_cols = i_cols + ny; end err = max(abs(res)); err = max(abs(res(i_upd_r))); if err < tolerance stop = 1; if verbose ... ... @@ -214,8 +213,10 @@ function [flag,endo_simul,err] = solve_stochastic_perfect_foresight_model(endo_s % pause break end dy = -A\res; Y(i_upd) = Y(i_upd) + dy; A2 = [nzA{:}]'; A = [A1; sparse(A2(:,1),A2(:,2),A2(:,3),ny*(periods-order-1)*world_nbr,dimension)]; dy = -A\res(i_upd_r); Y(i_upd_y) = Y(i_upd_y) + dy; end if ~stop ... ...
 ... ... @@ -79,10 +79,10 @@ copyfile('rbcii_steady_state.m','rbcii_steadystate2.m'); ts = extended_path([],100); options_.ep.stochastic.order = 1; profile on // profile on ts1_4 = extended_path([],100); profile off profile viewer // profile off // profile viewer @#else shocks; ... ...
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