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// workaround issue between gcc >= 4.7 and cuda 5.5 #if (defined __GNUC__) && (__GNUC__>4 || __GNUC_MINOR__>=7) #undef _GLIBCXX_ATOMIC_BUILTINS #undef _GLIBCXX_USE_INT128 #endif
#define EIGEN_TEST_NO_LONGDOUBLE #define EIGEN_TEST_NO_COMPLEX #define EIGEN_TEST_FUNC cuda_basic #define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
#include <math_constants.h> #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 StormEigen; // // 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 StormEigen; 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 replicate { EIGEN_DEVICE_FUNC void operator()(int i, const typename T::Scalar* in, typename T::Scalar* out) const { using namespace StormEigen; T x1(in+i); int step = x1.size() * 4; int stride = 3 * step; typedef Map<Array<typename T::Scalar,Dynamic,Dynamic> > MapType; MapType(out+i*stride+0*step, x1.rows()*2, x1.cols()*2) = x1.replicate(2,2); MapType(out+i*stride+1*step, x1.rows()*3, x1.cols()) = in[i] * x1.colwise().replicate(3); MapType(out+i*stride+2*step, x1.rows(), x1.cols()*3) = in[i] * x1.rowwise().replicate(3); } };
template<typename T> struct redux { EIGEN_DEVICE_FUNC void operator()(int i, const typename T::Scalar* in, typename T::Scalar* out) const { using namespace StormEigen; int N = 10; 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.matrix().squaredNorm(); out[i*N+5] = x1.matrix().norm(); out[i*N+6] = x1.colwise().sum().maxCoeff(); out[i*N+7] = x1.rowwise().maxCoeff().sum(); out[i*N+8] = x1.matrix().colwise().squaredNorm().sum(); } };
template<typename T1, typename T2> struct prod_test { EIGEN_DEVICE_FUNC void operator()(int i, const typename T1::Scalar* in, typename T1::Scalar* out) const { using namespace StormEigen; 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 T1, typename T2> struct diagonal { EIGEN_DEVICE_FUNC void operator()(int i, const typename T1::Scalar* in, typename T1::Scalar* out) const { using namespace StormEigen; T1 x1(in+i); Map<T2> res(out+i*T2::MaxSizeAtCompileTime); res += x1.diagonal(); } };
template<typename T> struct eigenvalues { EIGEN_DEVICE_FUNC void operator()(int i, const typename T::Scalar* in, typename T::Scalar* out) const { using namespace StormEigen; 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(M); res = eig.eigenvalues(); } };
void test_cuda_basic() { ei_test_init_cuda(); int nthreads = 100; StormEigen::VectorXf in, out; #ifndef __CUDA_ARCH__ int data_size = nthreads * 512; 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(replicate<Array4f>(), nthreads, in, out) ); CALL_SUBTEST( run_and_compare_to_cuda(replicate<Array33f>(), 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_test<Matrix3f,Matrix3f>(), nthreads, in, out) ); CALL_SUBTEST( run_and_compare_to_cuda(prod_test<Matrix4f,Vector4f>(), nthreads, in, out) ); CALL_SUBTEST( run_and_compare_to_cuda(diagonal<Matrix3f,Vector3f>(), nthreads, in, out) ); CALL_SUBTEST( run_and_compare_to_cuda(diagonal<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) );
}
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