, 그냥 cblas_dgemm 기능은

Question

내가 Intel MKL 행렬 곱셈을 테스트 할 사용되는, 그래서 내가 포함 난 그냥 cblas_dgemm 기능을 사용하지만, 항상 나는 또한 -lmkl_core -lmkl_blas95_lp64 -lmkl_lapack95_lp64을 연결

undefined reference to `cblas_dgemm' 

을 말한다 ,하지만 난 고궁 $MKLROOT/lib/intel64/ 디렉토리에있는 라이브러리 사이에 많은 조합을 가지고 오류가 여전히 존재합니다. 누군가 제게 조언 해 줄 수 있나요? 감사.

Answer 1

어쩌면 이것은 정답, 우리는 cblas_ 사용할 수있다 :

을 QT 창조주의 프로젝트 파일에서 :

unix { 
     INCLUDEPATH += /opt/intel/mkl/include 
     LIBS += -L/opt/intel/mkl/lib/intel64 \ 
     -lmkl_intel_lp64 -lmkl_intel_thread -lmkl_core \ 
     -L/opt/intel/lib/intel64 \ 
     -liomp5 -lpthread -dl -lm 
    } 

다음은 테스트 cblas_*의 main.cpp에 MKL :

#include <iostream> 
    using namespace std; 
    #include <cmath> 
    #include <cstdio> 
    #include <cstdlib> 
    #include <mkl.h> 
    #include <mkl_cblas.h> 
    #include <mkl_blas.h> 
    #include <mkl_lapack.h> 
    #include <mkl_lapacke.h> 

    template<typename T> 
    void printArray(T *data, char *name, int len){ 
     cout << name << "\n"; 
     for(int i=0;i<len;i++){ 
      cout << data[i] << " "; 
     } 
     cout << "\n"; 
    } 

    template<typename T> 
    void printMatrix(T *data, char *name, int m, int n){ 
     cout << name << "\n"; 
     for(int i=0;i<m;i++){ 
      for(int j=0;j<n;j++){ 
       cout << data[n*i+j] << " "; 
      } 
      cout << "\n"; 
     } 
     cout << "\n"; 
    } 

    int main() 
    { 
     int len=10; 
     double *x=new double[len]; 
     double *y=new double[len]; 
     for(int i=0;i<len;i++){ 
      x[i]=(double)rand()/RAND_MAX; 
      y[i]=(double)rand()/RAND_MAX; 
     } 
     printArray<double>(x, "x", len); 
     printArray<double>(y, "y", len); 

     //sum(x) 
     double x_sum=cblas_dasum(len,x,1); 
     cout<< "sum(x): "<< x_sum <<"\n"; 

     //y=a*x+y 
     double alpha=1; 
     cblas_daxpy(len,alpha,x,1,y,1); 
     printArray<double>(y,"y=a*x+y",len); 

     //y=x 
     cblas_dcopy(len,x,1,y,1); 
     printArray<double>(y,"y=x",len); 

     //x*y'; 
     double xy_dot=cblas_ddot(len,x,1,y,1); 
     cout <<"x*y': "<<xy_dot<<"\n"; 

     //extened x*y', not test 
     //cblas_sdsdot(),not test 
     //cbals_dsddot(),not test 
     //for complex vector 
     //cblas_dotc(),no this function 
     //cblas_dotu(),no this function 

     //norm(x), or ||x||_2 
     double x_norm=cblas_dnrm2(len,x,1); 
     cout<<"x_norm: "<<x_norm<<"\n"; 

     //x(i)=c*x(i)+s*y(i); 
     //y(i)=c*y(i)-s*x(i); 
     //cblas_zrotg(); not test 

     //swap(x,y) 
     cblas_dswap(len,x,1,y,1); 
     printArray<double>(x,"x:",len); 
     printArray<double>(y,"y:",len); 


     //LEVEL 2 BLAS 
     //matrix and vector manipulation 
     int m=len; 
     int n=len; 
     double *A=new double[m*n]; 
     for(int i=0;i<m*n;i++){ 
      A[i]=(double)rand()/RAND_MAX; 
     } 
     printMatrix<double>(A,"A",m,n); 

     //matrix and vector multiplication 
     double alpha_dgemv=1.0; 
     double beta_dgemv=1.0; 
     //y=alpha*A*x+beta*y, if A is a mxn band matrix, then use cblas_dgbmv 
     cblas_dgemv(CblasRowMajor,CblasNoTrans,m,n,alpha_dgemv,A,m,x,1,beta_dgemv,y,1); 
     printArray<double>(x,"x:",len); 
     printArray<double>(y,"y=alpha*A*x+beta*y",len); 
     //y=alpha*A'*x+beta*y 
     cblas_dgemv(CblasRowMajor,CblasTrans,m,n,alpha_dgemv,A,m,x,1,beta_dgemv,y,1); 
     printArray<double>(x,"x:",len); 
     printArray<double>(y,"y=alpha*A'*x+beta*y",len); 

     //A=alpha*x*y'+A; 
     double alpha_dger=1.0; 
     cblas_dger(CblasRowMajor,m,n,alpha_dger,x,1,y,1,A,m); 
     printArray<double>(x,"x:",len); 
     printArray<double>(y,"y:",len); 
     printMatrix<double>(A,"A=alpha1*x*y'+A",m,n); 
     delete[] x;x=NULL; 
     delete[] y;y=NULL; 
     delete[] A;A=NULL; 


     // 
     m=10; 
     n=5; 
     int k=3; 
     double *Amxk=new double[m*k]; 
     double *Bkxn=new double[k*n]; 
     double *Cmxn=new double[m*n]; 
     for(int i=0;i<m*k;i++){ 
      Amxk[i]=(double)rand()/RAND_MAX; 
     } 
     for(int i=0;i<k*n;i++){ 
      Bkxn[i]=(double)rand()/RAND_MAX; 
     } 
     for(int i=0;i<m*n;i++){ 
      Cmxn[i]=0; 
     } 
     printMatrix<double>(Amxk,"Amxk",m,k); 
     printMatrix<double>(Bkxn,"Bkxn",k,n); 
     printMatrix<double>(Cmxn,"Cmxn",m,n); 
     double alpha_dgemm=1.0; 
     double beta_dgemm=1.0; 
     cblas_dgemm(CblasRowMajor, 
        CblasNoTrans, 
        CblasNoTrans, 
        m, 
        n, 
        k, 
        alpha_dgemm, 
        Amxk, 
        k, 
        Bkxn, 
        n, 
        beta_dgemm, 
        Cmxn, 
        n); 
     printMatrix<double>(Cmxn,"Cmxn",m,n); 
     delete[] Amxk; 
     delete[] Bkxn; 
     delete[] Cmxn; 


     //general symmetric matrix eigenvalue decomposition 
     /* Locals */ 
     const MKL_INT N=5; 
     const MKL_INT LDA=5; 
     MKL_INT lda = LDA, info; 
     n=N; 
     /* Local arrays */ 
     double w[N]; 
     double a[LDA*N] = { 
      6.39, 0.13, -8.23, 5.71, -3.18, 
      0.00, 8.37, -4.46, -6.10, 7.21, 
      0.00, 0.00, -9.58, -9.25, -7.42, 
      0.00, 0.00, 0.00, 3.72, 8.54, 
      0.00, 0.00, 0.00, 0.00, 2.51 
     }; 
     /* Executable statements */ 
     printf("LAPACKE_dsyevd (row-major, high-level) Example Program Results\n"); 
     /* Solve eigenproblem */ 
     info = LAPACKE_dsyevd(LAPACK_ROW_MAJOR, 'V', 'U', n, a, lda, w); 
     /* Check for convergence */ 
     if(info > 0) { 
      printf("The algorithm failed to compute eigenvalues.\n"); 
      exit(1); 
     } 
     printArray<double>(w,"w:",N); 
     printMatrix<double>(a,"a:",N,N); 


     int N1=10; 
     int d=3; 
     double *A1=new double[N1*d]; 
     for(int j=0;j<d;j++){ 
     for(int i=0;i<N1;i++){ 

       A1[j*N1+i]=(double)rand()/RAND_MAX; 
      } 
     } 
     printMatrix<double>(A1,"A:",d,N1); 

     double *A_mean=new double[1*d]; 
     for(int i=0;i<d;i++){ 
      A_mean[i]=cblas_dasum(N,A1+i*N1,1); 
     } 
     printArray<double>(A_mean,"A_mean",d); 
     delete[] A1;A1=NULL; 
     delete[] A_mean;A_mean=NULL; 
     return 0; 
    } 

그런 다음, 터미널에서, 환경 설정 값을로드하려면 다음 코드를 입력 :

source /opt/intel/bin/compilervars.sh intel64 

Answer 2

인텔 ® 수학 커널 라이브러리 링크 라인 관리자가 필요한 것입니다. 그것은 당신을 위해 적절한 컴파일/링크 옵션을 생성 할 수 있습니다.

http://software.intel.com/en-us/articles/intel-mkl-link-line-advisor