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// example.cpp source code //
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#include <algorithm> // for std::min
#include <stddef.h>  // for size_t
#include <vector>
#include <hip/hip_runtime_api.h> // for hip functions
#include <rocsolver/rocsolver.h> // for all the rocsolver C interfaces and type declarations
int main() {
  rocblas_int M;
  rocblas_int N;
  rocblas_int lda;
  // here is where you would initialize M, N and lda with desired values
  rocblas_handle handle;
  rocblas_create_handle(&handle);
  size_t size_A = size_t(lda) * N;          // the size of the array for the matrix
  size_t size_piv = size_t(std::min(M, N)); // the size of array for the Householder scalars
  std::vector<double> hA(size_A);      // creates array for matrix in CPU
  std::vector<double> hIpiv(size_piv); // creates array for householder scalars in CPU
  double *dA, *dIpiv;
  hipMalloc(&dA, sizeof(double)*size_A);      // allocates memory for matrix in GPU
  hipMalloc(&dIpiv, sizeof(double)*size_piv); // allocates memory for scalars in GPU
  // here is where you would initialize matrix A (array hA) with input data
  // note: matrices must be stored in column major format,
  //       i.e. entry (i,j) should be accessed by hA[i + j*lda]
  // copy data to GPU
  hipMemcpy(dA, hA.data(), sizeof(double)*size_A, hipMemcpyHostToDevice);
  // compute the QR factorization on the GPU
  rocsolver_dgeqrf(handle, M, N, dA, lda, dIpiv);
  // copy the results back to CPU
  hipMemcpy(hA.data(), dA, sizeof(double)*size_A, hipMemcpyDeviceToHost);
  hipMemcpy(hIpiv.data(), dIpiv, sizeof(double)*size_piv, hipMemcpyDeviceToHost);
  // the results are now in hA and hIpiv, so you can use them here
  hipFree(dA);                        // de-allocate GPU memory
  hipFree(dIpiv);
  rocblas_destroy_handle(handle);     // destroy handle
}