*** glibc detected *** /home/tim/test/debug/test: double free or corruption (!prev): 0x0000000002527260 ***

这是否意味着地址0x0000000002527260的内存被释放多次？

错误发生在“_search_struct-> annkSearch(queryPt,k_max,nnIdx,dists,_eps);”内部函数classify_varIoUs_k(),它在函数tune_complexity()内部的OpenMP for循环中.

请注意,当OpenMP有多个线程时会发生错误,并且在单线程情况下不会发生.不知道为什么.

以下是我的代码.如果它不足以进行诊断,请告诉我.谢谢你的帮助！

void KNNClassifier::train(int nb_examples,int dim,double **features,int * labels) {                         
      _nPts = nb_examples;  
 
      _labels = labels;  
      _dataPts = features;  
 
      setting_ANN(_dist_type,1);   
 
    delete _search_struct;  
    if(strcmp(_search_neighbors,"brutal") == 0) {                                                                 
      _search_struct = new ANNbruteForce(_dataPts,_nPts,dim);  
    }else if(strcmp(_search_neighbors,"kdtree") == 0) {  
      _search_struct = new ANNkd_tree(_dataPts,dim);  
      }  
 
  }  
 
 
      void KNNClassifier::classify_varIoUs_k(int dim,double *feature,int label,int *ks,double * errors,int nb_ks,int k_max) {            
        ANNpoint      queryPt = 0;                                                                                                                
        ANNidxArray   nnIdx = 0;                                                                                                         
        ANNdistArray  dists = 0;                                                                                                         
 
        queryPt = feature;     
        nnIdx = new ANNidx[k_max];                                                               
        dists = new ANNdist[k_max];                                                                                
 
        if(strcmp(_search_neighbors,"brutal") == 0) {                                                                               
          _search_struct->annkSearch(queryPt,_eps);    
        }else if(strcmp(_search_neighbors,"kdtree") == 0) {    
          _search_struct->annkSearch(queryPt,_eps); // where error occurs    
        }    
 
        for (int j = 0; j < nb_ks; j++)    
        {    
          scalar_t result = 0.0;    
          for (int i = 0; i < ks[j]; i++) {                                                                                      
              result+=_labels[ nnIdx[i] ];    
          }    
          if (result*label<0) errors[j]++;    
        }    
 
        delete [] nnIdx;    
        delete [] dists;    
 
      }    
 
      void KNNClassifier::tune_complexity(int nb_examples,int *labels,int fold,char *method,int nb_examples_test,double **features_test,int *labels_test) {    
          int nb_try = (_k_max - _k_min) / scalar_t(_k_step);    
          scalar_t *error_validation = new scalar_t [nb_try];    
          int *ks = new int [nb_try];    
 
          for(int i=0; i < nb_try; i ++){    
            ks[i] = _k_min + _k_step * i;    
          }    
 
          if (strcmp(method,"ct")==0)                                                                                                                     
          {    
 
            train(nb_examples,dim,features,labels );// train once for all nb of nbs in ks                                                                                                
 
            for(int i=0; i < nb_try; i ++){    
              if (ks[i] > nb_examples){nb_try=i; break;}    
              error_validation[i] = 0;    
            }    
 
            int i = 0;    
      #pragma omp parallel shared(nb_examples_test,error_validation,features_test,labels_test,nb_try,ks) private(i)    
            {    
      #pragma omp for schedule(dynamic) nowait    
              for (i=0; i < nb_examples_test; i++)         
              {    
                classify_varIoUs_k(dim,features_test[i],labels_test[i],ks,ks[nb_try - 1]); // where error occurs    
              }    
            }    
            for (i=0; i < nb_try; i++)    
            {    
              error_validation[i]/=nb_examples_test;    
            }    
          }
 
          ......
     }

更新：

谢谢！我现在正试图通过使用“#pragma omp critical”来纠正classify_varIoUs_k()中写入相同内存问题的冲突：

void KNNClassifier::classify_varIoUs_k(int dim,int k_max) {   
  ANNpoint      queryPt = 0;    
  ANNidxArray   nnIdx = 0;      
  ANNdistArray  dists = 0;     
 
  queryPt = feature; //for (int i = 0; i < Vignette::size; i++){ queryPt[i] = vignette->content[i];}         
  nnIdx = new ANNidx[k_max];                
  dists = new ANNdist[k_max];               
 
  if(strcmp(_search_neighbors,"brutal") == 0) {// search  
    _search_struct->annkSearch(queryPt,_eps);  
  }else if(strcmp(_search_neighbors,"kdtree") == 0) {  
    _search_struct->annkSearch(queryPt,_eps);  
  }  
 
  for (int j = 0; j < nb_ks; j++)  
  {  
    scalar_t result = 0.0;  
    for (int i = 0; i < ks[j]; i++) {          
        result+=_labels[ nnIdx[i] ];  // Program received signal SIGSEGV,Segmentation fault
    }  
    if (result*label<0)  
    {  
    #pragma omp critical  
    {  
      errors[j]++;  
    }  
    }  
 
  }  
 
  delete [] nnIdx;  
  delete [] dists;  
 
}

但是,“result = _labels [nnIdx [i]];”中存在新的段错误错误.有些想法？谢谢！