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Diffstat (limited to 'test/cuda_basic.cu')
| -rw-r--r-- | test/cuda_basic.cu | 173 |
1 files changed, 0 insertions, 173 deletions
diff --git a/test/cuda_basic.cu b/test/cuda_basic.cu deleted file mode 100644 index cb2e4167a..000000000 --- a/test/cuda_basic.cu +++ /dev/null @@ -1,173 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2015-2016 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -// 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 <cuda.h> -#if defined __CUDACC_VER__ && __CUDACC_VER__ >= 70500 -#include <cuda_fp16.h> -#endif -#include "main.h" -#include "cuda_common.h" - -// Check that dense modules can be properly parsed by nvcc -#include <Eigen/Dense> - -// 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 replicate { - EIGEN_DEVICE_FUNC - void operator()(int i, const typename T::Scalar* in, typename T::Scalar* out) const - { - using namespace Eigen; - 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 Eigen; - 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 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 T1, typename T2> -struct diagonal { - EIGEN_DEVICE_FUNC - void operator()(int i, const typename T1::Scalar* in, typename T1::Scalar* out) const - { - using namespace Eigen; - 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 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(M); - res = eig.eigenvalues(); - } -}; - -void test_cuda_basic() -{ - ei_test_init_cuda(); - - int nthreads = 100; - Eigen::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) ); - -} |