eigen/test/cuda_basic.cu

#define EIGEN_TEST_NO_LONGDOUBLE
#define EIGEN_TEST_NO_COMPLEX
#define EIGEN_TEST_FUNC cuda_basic
#include "main.h"
#include "cuda_common.h"

#include <Eigen/Eigenvalues>

// struct Foo{
//   EIGEN_DEVICE_FUNC
//   void operator()(int i, const float* mats, float* vecs) const {
//     using namespace Eigen;
//   //   Matrix3f M(data);
//   //   Vector3f x(data+9);
//   //   Map<Vector3f>(data+9) = M.inverse() * x;
//     Matrix3f M(mats+i/16);
//     Vector3f x(vecs+i*3);
//   //   using std::min;
//   //   using std::sqrt;
//     Map<Vector3f>(vecs+i*3) << x.minCoeff(), 1, 2;// / x.dot(x);//(M.inverse() *  x) / x.x();
//     //x = x*2 + x.y() * x + x * x.maxCoeff() - x / x.sum();
//   }
// };

template<typename T>
struct coeff_wise {
  EIGEN_DEVICE_FUNC
  void operator()(int i, const typename T::Scalar* in, typename T::Scalar* out) const
  {
    using namespace Eigen;
    T x1(in+i);
    T x2(in+i+1);
    T x3(in+i+2);
    Map<T> res(out+i*T::MaxSizeAtCompileTime);
    
    res.array() += (in[0] * x1 + x2).array() * x3.array();
  }
};

template<typename T>
struct redux {
  EIGEN_DEVICE_FUNC
  void operator()(int i, const typename T::Scalar* in, typename T::Scalar* out) const
  {
    using namespace Eigen;
    int N = 6;
    T x1(in+i);
    out[i*N+0] = x1.minCoeff();
    out[i*N+1] = x1.maxCoeff();
    out[i*N+2] = x1.sum();
    out[i*N+3] = x1.prod();
//     out[i*N+4] = x1.colwise().sum().maxCoeff();
//     out[i*N+5] = x1.rowwise().maxCoeff().sum();
  }
};

template<typename T1, typename T2>
struct prod {
  EIGEN_DEVICE_FUNC
  void operator()(int i, const typename T1::Scalar* in, typename T1::Scalar* out) const
  {
    using namespace Eigen;
    typedef Matrix<typename T1::Scalar, T1::RowsAtCompileTime, T2::ColsAtCompileTime> T3;
    T1 x1(in+i);
    T2 x2(in+i+1);
    Map<T3> res(out+i*T3::MaxSizeAtCompileTime);
    res += in[i] * x1 * x2;
  }
};


template<typename T>
struct eigenvalues {
  EIGEN_DEVICE_FUNC
  void operator()(int i, const typename T::Scalar* in, typename T::Scalar* out) const
  {
    using namespace Eigen;
    typedef Matrix<typename T::Scalar, T::RowsAtCompileTime, 1> Vec;
    T M(in+i);
    Map<Vec> res(out+i*Vec::MaxSizeAtCompileTime);
    T A = M*M.adjoint();
    SelfAdjointEigenSolver<T> eig;
    eig.computeDirect(A);
    res = A.eigenvalues();
  }
};


void test_cuda_basic()
{
  ei_test_init_cuda();
  
  int nthreads = 100;
  Eigen::VectorXf in, out;
  
  #ifndef __CUDA_ARCH__
  int data_size = nthreads * 16;
  in.setRandom(data_size);
  out.setRandom(data_size);
  #endif
  
  CALL_SUBTEST( run_and_compare_to_cuda(coeff_wise<Vector3f>(), nthreads, in, out) );
  CALL_SUBTEST( run_and_compare_to_cuda(coeff_wise<Array44f>(), nthreads, in, out) );
  
  CALL_SUBTEST( run_and_compare_to_cuda(redux<Array4f>(), nthreads, in, out) );
  CALL_SUBTEST( run_and_compare_to_cuda(redux<Matrix3f>(), nthreads, in, out) );
  
  CALL_SUBTEST( run_and_compare_to_cuda(prod<Matrix3f,Matrix3f>(), nthreads, in, out) );
  CALL_SUBTEST( run_and_compare_to_cuda(prod<Matrix4f,Vector4f>(), nthreads, in, out) );
  
//   CALL_SUBTEST( run_and_compare_to_cuda(eigenvalues<Matrix3f>(), nthreads, in, out) );
//   CALL_SUBTEST( run_and_compare_to_cuda(eigenvalues<Matrix2f>(), nthreads, in, out) );

}