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Add minimalistic unit tests for NVCC support

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Gael Guennebaud
2013-11-05 15:41:45 +01:00
parent 87aee5fda1
commit 4f572e4c14
6 changed files with 272 additions and 6 deletions
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116
test/cuda_basic.cu Normal file
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#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) );
}