X-Git-Url: http://info.iut-bm.univ-fcomte.fr/pub/gitweb/simgrid.git/blobdiff_plain/554542681a91f6e2f03abc8e08cba77f487f993d..0eb371ccd29974a375f8c6526b1ba14946997662:/src/surf/lagrange.c diff --git a/src/surf/lagrange.c b/src/surf/lagrange.c index 8190dfba33..cfabb336ee 100644 --- a/src/surf/lagrange.c +++ b/src/surf/lagrange.c @@ -18,60 +18,124 @@ #endif -XBT_LOG_NEW_DEFAULT_SUBCATEGORY(surf_lagrange, surf, "Logging specific to SURF (lagrange)"); +XBT_LOG_NEW_DEFAULT_SUBCATEGORY(surf_lagrange, surf, + "Logging specific to SURF (lagrange)"); +XBT_LOG_NEW_SUBCATEGORY(surf_lagrange_dichotomy, surf, + "Logging specific to SURF (lagrange dichotomy)"); /* - * Local prototypes to implement the lagrangian optimization with optimal step, also called dicotomi. + * Local prototypes to implement the lagrangian optimization with optimal step, also called dichotomy. */ -//solves the proportional fairness using a lagrange optimizition with dicotomi step -void lagrange_solve (lmm_system_t sys); -//computes the value of the dicotomi using a initial values, init, with a specific variable or constraint -double dicotomi(double init, double diff(double, void*), void *var_cnst, double min_error); +//solves the proportional fairness using a lagrange optimizition with dichotomy step +void lagrange_solve(lmm_system_t sys); +//computes the value of the dichotomy using a initial values, init, with a specific variable or constraint +double dichotomy(double init, double diff(double, void *), void *var_cnst, + double min_error); //computes the value of the differential of variable param_var applied to mu -double partial_diff_mu (double mu, void * param_var); +double partial_diff_mu(double mu, void *param_var); //computes the value of the differential of constraint param_cnst applied to lambda -double partial_diff_lambda (double lambda, void * param_cnst); +double partial_diff_lambda(double lambda, void *param_cnst); //auxiliar function to compute the partial_diff double diff_aux(lmm_variable_t var, double x); +static int __check_kkt(xbt_swag_t cnst_list, xbt_swag_t var_list, int warn) +{ + xbt_swag_t elem_list = NULL; + lmm_element_t elem = NULL; + lmm_constraint_t cnst = NULL; + lmm_variable_t var = NULL; + + double tmp; + + //verify the KKT property for each link + xbt_swag_foreach(cnst, cnst_list) { + tmp = 0; + elem_list = &(cnst->element_set); + xbt_swag_foreach(elem, elem_list) { + var = elem->variable; + if (var->weight <= 0) + continue; + tmp += var->value; + } + + if (double_positive(tmp - cnst->bound)) { + if (warn) + WARN3 + ("The link (%p) is over-used. Expected less than %f and got %f", + cnst, cnst->bound, tmp); + return 0; + } + DEBUG3("Checking KKT for constraint (%p): sat = %f, lambda = %f ", + cnst, tmp - cnst->bound, cnst->lambda); + +/* if(!((fabs(tmp - cnst->bound)lambda>=MAXMIN_PRECISION) || */ +/* (fabs(tmp - cnst->bound)>=MAXMIN_PRECISION && cnst->lambdabound < 0 || var->weight <= 0) + continue; + DEBUG3("Checking KKT for variable (%p): sat = %f mu = %f", var, + var->value - var->bound, var->mu); + + if (double_positive(var->value - var->bound)) { + if (warn) + WARN3 + ("The variable (%p) is too large. Expected less than %f and got %f", + var, var->bound, var->value); + return 0; + } + +/* if(!((fabs(var->value - var->bound)mu>=MAXMIN_PRECISION) || */ +/* (fabs(var->value - var->bound)>=MAXMIN_PRECISION && var->mu"); DEBUG1("#### Maximum number of iterations : %d", max_iterations); - DEBUG1("#### Minimum error tolerated : %e", epsilon_min_error); - DEBUG1("#### Minimum error tolerated (dicotomi) : %e", dicotomi_min_error); + DEBUG1("#### Minimum error tolerated : %e", + epsilon_min_error); + DEBUG1("#### Minimum error tolerated (dichotomy) : %e", + dichotomy_min_error); - if ( !(sys->modified)) + if (!(sys->modified)) return; /* @@ -79,46 +143,51 @@ void lagrange_solve(lmm_system_t sys) * Associate an index in the swag variables. Initialize mu. */ var_list = &(sys->variable_set); - i=0; - xbt_swag_foreach(var, var_list) { - if((var->bound > 0.0) || (var->weight <= 0.0)){ - DEBUG1("#### NOTE var(%d) is a boundless variable", i); + i = 0; + xbt_swag_foreach(var, var_list) { + if ((var->bound < 0.0) || (var->weight <= 0.0)) { + DEBUG1("#### NOTE var(%d) is a boundless (or inactive) variable", i); var->mu = -1.0; - } else{ - var->mu = 1.0; + } else { + var->mu = 1.0; var->new_mu = 2.0; } - DEBUG2("#### var(%d)->mu : %e", i, var->mu); - DEBUG2("#### var(%d)->weight: %e", i, var->weight); + DEBUG3("#### var(%d) %p ->mu : %e", i, var, var->mu); + DEBUG3("#### var(%d) %p ->weight: %e", i, var, var->weight); + DEBUG3("#### var(%d) %p ->bound: %e", i, var, var->bound); i++; } /* * Initialize lambda. */ - cnst_list=&(sys->active_constraint_set); - xbt_swag_foreach(cnst, cnst_list){ + cnst_list = &(sys->active_constraint_set); + xbt_swag_foreach(cnst, cnst_list) { cnst->lambda = 1.0; cnst->new_lambda = 2.0; DEBUG2("#### cnst(%p)->lambda : %e", cnst, cnst->lambda); } - + /* * While doesn't reach a minimun error or a number maximum of iterations. */ - while(overall_error > epsilon_min_error && iteration < max_iterations){ - + while (overall_error > epsilon_min_error && iteration < max_iterations) { + iteration++; - DEBUG1("************** ITERATION %d **************", iteration); + DEBUG1("************** ITERATION %d **************", iteration); /* * Compute the value of mu_i */ //forall mu_i in mu_1, mu_2, ..., mu_n xbt_swag_foreach(var, var_list) { - if((var->bound >= 0) && (var->weight > 0) ){ - var->new_mu = dicotomi(var->mu, partial_diff_mu, var, dicotomi_min_error); - if(var->new_mu < 0) var->new_mu = 0; + if ((var->bound >= 0) && (var->weight > 0)) { + DEBUG1("====> Working on var (%p)", var); + var->new_mu = + dichotomy(var->mu, partial_diff_mu, var, dichotomy_min_error); + if (var->new_mu < 0) + var->new_mu = 0; + DEBUG3("====> var->mu (%p) : %g -> %g", var, var->mu, var->new_mu); var->mu = var->new_mu; } } @@ -128,8 +197,11 @@ void lagrange_solve(lmm_system_t sys) */ //forall lambda_i in lambda_1, lambda_2, ..., lambda_n xbt_swag_foreach(cnst, cnst_list) { - cnst->new_lambda = dicotomi(cnst->lambda, partial_diff_lambda, cnst, dicotomi_min_error); - DEBUG2("====> cnst->lambda (%p) = %e", cnst, cnst->new_lambda); + DEBUG1("====> Working on cnst (%p)", cnst); + cnst->new_lambda = + dichotomy(cnst->lambda, partial_diff_lambda, cnst, + dichotomy_min_error); + DEBUG2("====> cnst->lambda (%p) = %e", cnst, cnst->new_lambda); cnst->lambda = cnst->new_lambda; } @@ -137,74 +209,60 @@ void lagrange_solve(lmm_system_t sys) * Now computes the values of each variable (\rho) based on * the values of \lambda and \mu. */ - overall_error=0; + overall_error = 0; xbt_swag_foreach(var, var_list) { - if(var->weight <=0) + if (var->weight <= 0) var->value = 0.0; else { //compute sigma_i + mu_i tmp = 0; - for(i=0; icnsts_number; i++){ + for (i = 0; i < var->cnsts_number; i++) { tmp += (var->cnsts[i].constraint)->lambda; - if(var->bound > 0) - tmp+=var->mu; } + if (var->bound > 0) + tmp += var->mu; + DEBUG3("\t Working on var (%p). cost = %e; Df = %e", var, tmp, + var->df); //uses the partial differential inverse function tmp = var->func_fpi(var, tmp); //computes de overall_error using normalized value - if(overall_error < (fabs(var->value - tmp)/tmp) ){ - overall_error = (fabs(var->value - tmp)/tmp); + if (overall_error < (fabs(var->value - tmp) / tmp)) { + overall_error = (fabs(var->value - tmp) / tmp); } - + var->value = tmp; } - DEBUG4("======> value of var %s (%p) = %e, overall_error = %e", (char *)var->id, var, var->value, overall_error); + DEBUG3("======> value of var (%p) = %e, overall_error = %e", var, + var->value, overall_error); } + + if (!__check_kkt(cnst_list, var_list, 0)) + overall_error = 1.0; + DEBUG2("Iteration %d: Overall_error : %f", iteration, overall_error); } - //verify the KKT property for each link - xbt_swag_foreach(cnst, cnst_list){ - tmp = 0; - elem_list = &(cnst->element_set); - xbt_swag_foreach(elem, elem_list) { - var = elem->variable; - if(var->weight<=0) continue; - tmp += var->value; - } - - tmp = tmp - cnst->bound; + __check_kkt(cnst_list, var_list, 1); - if(tmp > epsilon_min_error){ - WARN4("The link %s(%p) doesn't match the KKT property, expected less than %e and got %e", (char *)cnst->id, cnst, epsilon_min_error, tmp); - } - + if (overall_error <= epsilon_min_error) { + DEBUG1("The method converges in %d iterations.", iteration); } - - //verify the KKT property of each flow - xbt_swag_foreach(var, var_list){ - if(var->bound <= 0 || var->weight <= 0) continue; - tmp = 0; - tmp = (var->value - var->bound); - - - if(tmp != 0 || var->mu != 0){ - WARN4("The flow %s(%p) doesn't match the KKT property, value expected (=0) got (lambda=%e) (sum_rho=%e)", (char *)var->id, var, var->mu, tmp); - } - + if (iteration >= max_iterations) { + WARN1 + ("Method reach %d iterations, which is the maximum number of iterations allowed.", + iteration); } +/* INFO1("Method converged after %d iterations", iteration); */ - if(overall_error <= epsilon_min_error){ - DEBUG1("The method converge in %d iterations.", iteration); - }else{ - WARN1("Method reach %d iterations, which is the maxmimun number of iterations allowed.", iteration); + if (XBT_LOG_ISENABLED(surf_lagrange, xbt_log_priority_debug)) { + lmm_print(sys); } } /* - * Returns a double value corresponding to the result of a dicotomi proccess with + * Returns a double value corresponding to the result of a dichotomy proccess with * respect to a given variable/constraint (\mu in the case of a variable or \lambda in * case of a constraint) and a initial value init. * @@ -213,171 +271,197 @@ void lagrange_solve(lmm_system_t sys) * @param var_cnst a pointer to a variable or constraint * @param min_erro a minimun error tolerated * - * @return a double correponding to the result of the dicotomial process + * @return a double correponding to the result of the dichotomyal process */ -double dicotomi(double init, double diff(double, void*), void *var_cnst, double min_error){ +double dichotomy(double init, double diff(double, void *), void *var_cnst, + double min_error) +{ double min, max; double overall_error; double middle; double min_diff, max_diff, middle_diff; - + double diff_0 = 0.0; min = max = init; - if(init == 0){ - min = max = 1; + XBT_IN; + + if (init == 0.0) { + min = max = 0.5; } min_diff = max_diff = middle_diff = 0.0; overall_error = 1; - if(diff(0.0, var_cnst) > 0){ - DEBUG1("====> returning 0.0 (diff = %e)", diff(0.0, var_cnst)); + if ((diff_0 = diff(1e-16, var_cnst)) >= 0) { + CDEBUG1(surf_lagrange_dichotomy, "====> returning 0.0 (diff = %e)", + diff_0); return 0.0; } - DEBUG0("====> not detected positive diff in 0"); + CDEBUG1(surf_lagrange_dichotomy, + "====> not detected positive diff in 0 (%e)", diff_0); - while(overall_error > min_error){ + while (overall_error > min_error) { min_diff = diff(min, var_cnst); max_diff = diff(max, var_cnst); - DEBUG2("DICOTOMI ===> min = %e , max = %e", min, max); - DEBUG2("DICOTOMI ===> diffmin = %e , diffmax = %e", min_diff, max_diff); + CDEBUG2(surf_lagrange_dichotomy, + "DICHOTOMY ===> min = %1.20f , max = %1.20f", min, max); + CDEBUG2(surf_lagrange_dichotomy, + "DICHOTOMY ===> diffmin = %1.20f , diffmax = %1.20f", min_diff, + max_diff); - if( min_diff > 0 && max_diff > 0 ){ - if(min == max){ + if (min_diff > 0 && max_diff > 0) { + if (min == max) { + CDEBUG0(surf_lagrange_dichotomy, "Decreasing min"); min = min / 2.0; - }else{ + } else { + CDEBUG0(surf_lagrange_dichotomy, "Decreasing max"); max = min; } - }else if( min_diff < 0 && max_diff < 0 ){ - if(min == max){ + } else if (min_diff < 0 && max_diff < 0) { + if (min == max) { + CDEBUG0(surf_lagrange_dichotomy, "Increasing max"); max = max * 2.0; - }else{ + } else { + CDEBUG0(surf_lagrange_dichotomy, "Increasing min"); min = max; } - }else if( min_diff < 0 && max_diff > 0 ){ - middle = (max + min)/2.0; + } else if (min_diff < 0 && max_diff > 0) { + middle = (max + min) / 2.0; middle_diff = diff(middle, var_cnst); - overall_error = fabs(min - max); - if( middle_diff < 0 ){ + if (max != 0.0 && min != 0.0) { + overall_error = fabs(min - max) / max; + } + + if (middle_diff < 0) { min = middle; - }else if( middle_diff > 0 ){ + } else if (middle_diff > 0) { max = middle; - }else{ - WARN0("Found an optimal solution with 0 error!"); + } else { + CWARN0(surf_lagrange_dichotomy, + "Found an optimal solution with 0 error!"); overall_error = 0; return middle; } - }else if(min_diff == 0){ + } else if (min_diff == 0) { return min; - }else if(max_diff == 0){ + } else if (max_diff == 0) { return max; - }else if(min_diff > 0 && max_diff < 0){ - WARN0("The impossible happened, partial_diff(min) > 0 && partial_diff(max) < 0"); + } else if (min_diff > 0 && max_diff < 0) { + CWARN0(surf_lagrange_dichotomy, + "The impossible happened, partial_diff(min) > 0 && partial_diff(max) < 0"); + } else { + CWARN2(surf_lagrange_dichotomy, + "diffmin (%1.20f) or diffmax (%1.20f) are something I don't know, taking no action.", + min_diff, max_diff); + abort(); } } + XBT_OUT; - DEBUG1("====> returning %e", (min+max)/2.0); - return ((min+max)/2.0); + CDEBUG1(surf_lagrange_dichotomy, "====> returning %e", + (min + max) / 2.0); + return ((min + max) / 2.0); } /* * */ -double partial_diff_mu(double mu, void *param_var){ - double mu_partial=0.0; - double sigma_mu=0.0; - lmm_variable_t var = (lmm_variable_t)param_var; +double partial_diff_mu(double mu, void *param_var) +{ + double mu_partial = 0.0; + double sigma_mu = 0.0; + lmm_variable_t var = (lmm_variable_t) param_var; int i; - + XBT_IN; //compute sigma_i - for(i=0; icnsts_number; i++) + for (i = 0; i < var->cnsts_number; i++) sigma_mu += (var->cnsts[i].constraint)->lambda; - + //compute sigma_i + mu_i - sigma_mu += var->mu; - + sigma_mu += mu; + //use auxiliar function passing (sigma_i + mu_i) - mu_partial = diff_aux(var, sigma_mu) ; - + mu_partial = diff_aux(var, sigma_mu); + //add the RTT limit mu_partial += var->bound; + XBT_OUT; return mu_partial; } /* * */ -double partial_diff_lambda(double lambda, void *param_cnst){ +double partial_diff_lambda(double lambda, void *param_cnst) +{ int i; xbt_swag_t elem_list = NULL; lmm_element_t elem = NULL; lmm_variable_t var = NULL; - lmm_constraint_t cnst= (lmm_constraint_t) param_cnst; - double lambda_partial=0.0; - double sigma_mu=0.0; + lmm_constraint_t cnst = (lmm_constraint_t) param_cnst; + double lambda_partial = 0.0; + double sigma_i = 0.0; + XBT_IN; elem_list = &(cnst->element_set); - DEBUG2("Computting diff of cnst (%p) %s", cnst, (char *)cnst->id); - + CDEBUG1(surf_lagrange_dichotomy,"Computting diff of cnst (%p)", cnst); + xbt_swag_foreach(elem, elem_list) { var = elem->variable; - if(var->weight<=0) continue; - + if (var->weight <= 0) + continue; + //initilize de sumation variable - sigma_mu = 0.0; + sigma_i = 0.0; //compute sigma_i of variable var - for(i=0; icnsts_number; i++){ - sigma_mu += (var->cnsts[i].constraint)->lambda; + for (i = 0; i < var->cnsts_number; i++) { + sigma_i += (var->cnsts[i].constraint)->lambda; } - + //add mu_i if this flow has a RTT constraint associated - if(var->bound > 0) sigma_mu += var->mu; + if (var->bound > 0) + sigma_i += var->mu; //replace value of cnst->lambda by the value of parameter lambda - sigma_mu = (sigma_mu - cnst->lambda) + lambda; - + sigma_i = (sigma_i - cnst->lambda) + lambda; + //use the auxiliar function passing (\sigma_i + \mu_i) - lambda_partial += diff_aux(var, sigma_mu); + lambda_partial += diff_aux(var, sigma_i); } + lambda_partial += cnst->bound; + + CDEBUG1(surf_lagrange_dichotomy, "returning = %1.20f", lambda_partial); + + XBT_OUT; return lambda_partial; } -double diff_aux(lmm_variable_t var, double x){ - double tmp_fp, tmp_fpi, tmp_fpip, result; +double diff_aux(lmm_variable_t var, double x) +{ + double tmp_fpi, result; - xbt_assert0(var->func_fp, "Initialize the protocol functions first create variables before."); + XBT_IN2("(var (%p), x (%1.20f))", var, x); + xbt_assert0(var->func_fp, + "Initialize the protocol functions first create variables before."); - tmp_fp = var->func_fp(var, x); tmp_fpi = var->func_fpi(var, x); - tmp_fpip = var->func_fpip(var, x); - - result = tmp_fpip*(var->func_fp(var, tmp_fpi)); - - result = result - tmp_fpi; - - result = result - (tmp_fpip * x); + result = - tmp_fpi; + CDEBUG1(surf_lagrange_dichotomy, "returning %1.20f", result); + XBT_OUT; return result; -} - - - - - - - - +}