mirror of
https://gitlab.com/libeigen/eigen.git
synced 2024-12-27 07:29:52 +08:00
117 lines
3.2 KiB
Plaintext
117 lines
3.2 KiB
Plaintext
|
|
|
|
#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) );
|
|
|
|
}
|