Intel MKL performance degrade a lot when I combine it with openMPI

I am using the intel math kernel library to write my algorithm and I set the number of threads to 16. My program can work well. However, when I tried to combine the MKL with MPI and run my program with 

mpirun -n 1 ./MMNET_MPI

I think this will give me the same result as I directly run my program as the following.

./MMNET_MPI

However, the performance of my program degrades a lot when I used 16 threads and the activate threads are only 2 or 3. I am not sure what the problem is. The part of my MKL program is as the following. 

void LMMCPU::multXXTTrace(double *out, const double *vec) const {

  double *snpBlock = ALIGN_ALLOCATE_DOUBLES(Npad * snpsPerBlock);
  double (*workTable)[4] = (double (*)[4]) ALIGN_ALLOCATE_DOUBLES(omp_get_max_threads() * 256 * sizeof(*workTable));

  // store the temp result
  double *temp1 = ALIGN_ALLOCATE_DOUBLES(snpsPerBlock);
  for (uint64 m0 = 0; m0 < M; m0 += snpsPerBlock) {
    uint64 snpsPerBLockCrop = std::min(M, m0 + snpsPerBlock) - m0;
#pragma omp parallel for
    for (uint64 mPlus = 0; mPlus < snpsPerBLockCrop; mPlus++) {
      uint64 m = m0 + mPlus;
      if (projMaskSnps[m])
        buildMaskedSnpCovCompVec(snpBlock + mPlus * Npad, m,
                                 workTable + (omp_get_thread_num() << 8));
      else
        memset(snpBlock + mPlus * Npad, 0, Npad * sizeof(snpBlock[0]));
    }

    for (uint64 iter = 0; iter < estIteration; iter++) {
      // compute A=X^TV
      MKL_INT row = Npad;
      MKL_INT col = snpsPerBLockCrop;
      double alpha = 1.0;
      MKL_INT lda = Npad;
      MKL_INT incx = 1;
      double beta = 0.0;
      MKL_INT incy = 1;
      cblas_dgemv(CblasColMajor,
                  CblasTrans,
                  row,
                  col,
                  alpha,
                  snpBlock,
                  lda,
                  vec + iter * Npad,
                  incx,
                  beta,
                  temp1,
                  incy);

      // compute XA
      double beta1 = 1.0;
      cblas_dgemv(CblasColMajor, CblasNoTrans, row, col, alpha, snpBlock, lda, temp1, incx, beta1, out + iter * Npad,
                  incy);

    }

  }
  ALIGN_FREE(snpBlock);
  ALIGN_FREE(workTable);
  ALIGN_FREE(temp1);
}