Question

我在我的cuda函数中调用JacobiSVD时出错了。

这是导致错误的代码的一部分。

Eigen::JacobiSVD<Eigen::Matrix3d> svd( cov_e, Eigen::ComputeThinU | Eigen::ComputeThinV);

这是错误信息。

CUDA_voxel_building.cu（43）：错误：调用__host__ 函数（＆＃34; Eigen :: JacobiSVD，（int）2＆gt; :: JacobiSVD＆＃34;）来自__global__ 函数（＆＃34;内核＆＃34;）是不允许的

我已使用以下命令进行编译。

nvcc -std=c++11 -D_MWAITXINTRIN_H_INCLUDED -D__STRICT_ANSI__ -ptx CUDA_voxel_building.cu

我在ubuntu 16.04上使用代码8.0和eigen3。似乎其他函数如特征值分解也会产生相同的误差。

有谁知道解决方案？我在下面附上我的代码。

//nvcc -ptx CUDA_voxel_building.cu
#include </usr/include/eigen3/Eigen/Core>
#include </usr/include/eigen3/Eigen/SVD>
/*
#include </usr/include/eigen3/Eigen/Sparse>

#include </usr/include/eigen3/Eigen/Dense>
#include </usr/include/eigen3/Eigen/Eigenvalues> 

*/





__global__ void kernel(double *p, double *breaks,double *ind,  double *mu, double *cov,  double *e,double *v, int *n, char *isgood,  int minpts, int maxgpu){
    bool debuginfo = false;
    int idx = threadIdx.x + blockIdx.x * blockDim.x;
    if(debuginfo)printf("Thread %d got pointer\n",idx);
    if( idx < maxgpu){


        int s_ind = breaks[idx];
        int e_ind = breaks[idx+1];
        int diff = e_ind-s_ind;

        if(diff >minpts){
            int cnt = 0;
            Eigen::MatrixXd local_p(3,diff) ;
            for(int k = s_ind;k<e_ind;k++){
                int temp_ind=ind[k];

                //Eigen::Matrix<double, 3, diff> local_p;   
                local_p(1,cnt) =  p[temp_ind*3];
                local_p(2,cnt) =  p[temp_ind*3+1];
                local_p(3,cnt) =  p[temp_ind*3+2];
                cnt++;
            }

            Eigen::Matrix3d centered = local_p.rowwise() - local_p.colwise().mean();
            Eigen::Matrix3d cov_e = (centered.adjoint() * centered) / double(local_p.rows() - 1);

            Eigen::JacobiSVD<Eigen::Matrix3d> svd( cov_e, Eigen::ComputeThinU | Eigen::ComputeThinV);
     /*         Eigen::Matrix3d Cp = svd.matrixU() * svd.singularValues().asDiagonal() * svd.matrixV().transpose();


            mu[idx]=p[ind[s_ind]*3];
            mu[idx+1]=p[ind[s_ind+1]*3];
            mu[idx+2]=p[ind[s_ind+2]*3];

            e[idx]=svd.singularValues()(0);
            e[idx+1]=svd.singularValues()(1);
            e[idx+2]=svd.singularValues()(2);

            n[idx] = diff;
            isgood[idx] = 1;

            for(int x = 0; x < 3; x++)
            {
                for(int y = 0; y < 3; y++)
                {
                    v[x+ 3*y +idx*9]=svd.matrixV()(x, y);
                    cov[x+ 3*y +idx*9]=cov_e(x, y);
                    //if(debuginfo)printf("%f ",R[x+ 3*y +i*9]);
                    if(debuginfo)printf("%f ",Rm(x, y));
                }
            }
*/

        } else {
            mu[idx]=0;
            mu[idx+1]=0;
            mu[idx+2]=0;

            e[idx]=0;
            e[idx+1]=0;
            e[idx+2]=0;

            n[idx] = 0;
            isgood[idx] = 0;

            for(int x = 0; x < 3; x++)
            {
                for(int y = 0; y < 3; y++)
                {
                    v[x+ 3*y +idx*9]=0;
                    cov[x+ 3*y +idx*9]=0;
                }
            }
        }




    }
}

Eigen JacobiSVD cuda编译错误

0 个答案: