From dec47a64930b2a5b68c55706cbdba7f945781d0e Mon Sep 17 00:00:00 2001 From: Deven Desai Date: Wed, 11 Jul 2018 09:26:54 -0400 Subject: renaming CUDA* to GPU* for some header files --- test/gpu_basic.cu | 203 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 203 insertions(+) create mode 100644 test/gpu_basic.cu (limited to 'test/gpu_basic.cu') diff --git a/test/gpu_basic.cu b/test/gpu_basic.cu new file mode 100644 index 000000000..33e5fd119 --- /dev/null +++ b/test/gpu_basic.cu @@ -0,0 +1,203 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2015-2016 Gael Guennebaud +// +// 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 +#include +#include "main.h" +#include "cuda_common.h" + +// Check that dense modules can be properly parsed by nvcc +#include + +// 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(data+9) = M.inverse() * x; +// Matrix3f M(mats+i/16); +// Vector3f x(vecs+i*3); +// // using std::min; +// // using std::sqrt; +// Map(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 +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 res(out+i*T::MaxSizeAtCompileTime); + + res.array() += (in[0] * x1 + x2).array() * x3.array(); + } +}; + +template +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 > 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 +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 +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 T3; + T1 x1(in+i); + T2 x2(in+i+1); + Map res(out+i*T3::MaxSizeAtCompileTime); + res += in[i] * x1 * x2; + } +}; + +template +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 res(out+i*T2::MaxSizeAtCompileTime); + res += x1.diagonal(); + } +}; + +template +struct eigenvalues_direct { + EIGEN_DEVICE_FUNC + void operator()(int i, const typename T::Scalar* in, typename T::Scalar* out) const + { + using namespace Eigen; + typedef Matrix Vec; + T M(in+i); + Map res(out+i*Vec::MaxSizeAtCompileTime); + T A = M*M.adjoint(); + SelfAdjointEigenSolver eig; + eig.computeDirect(M); + res = eig.eigenvalues(); + } +}; + +template +struct eigenvalues { + EIGEN_DEVICE_FUNC + void operator()(int i, const typename T::Scalar* in, typename T::Scalar* out) const + { + using namespace Eigen; + typedef Matrix Vec; + T M(in+i); + Map res(out+i*Vec::MaxSizeAtCompileTime); + T A = M*M.adjoint(); + SelfAdjointEigenSolver eig; + eig.compute(M); + res = eig.eigenvalues(); + } +}; + +template +struct matrix_inverse { + EIGEN_DEVICE_FUNC + void operator()(int i, const typename T::Scalar* in, typename T::Scalar* out) const + { + using namespace Eigen; + T M(in+i); + Map res(out+i*T::MaxSizeAtCompileTime); + res = M.inverse(); + } +}; + +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(), nthreads, in, out) ); + CALL_SUBTEST( run_and_compare_to_cuda(coeff_wise(), nthreads, in, out) ); + + CALL_SUBTEST( run_and_compare_to_cuda(replicate(), nthreads, in, out) ); + CALL_SUBTEST( run_and_compare_to_cuda(replicate(), nthreads, in, out) ); + + CALL_SUBTEST( run_and_compare_to_cuda(redux(), nthreads, in, out) ); + CALL_SUBTEST( run_and_compare_to_cuda(redux(), nthreads, in, out) ); + + CALL_SUBTEST( run_and_compare_to_cuda(prod_test(), nthreads, in, out) ); + CALL_SUBTEST( run_and_compare_to_cuda(prod_test(), nthreads, in, out) ); + + CALL_SUBTEST( run_and_compare_to_cuda(diagonal(), nthreads, in, out) ); + CALL_SUBTEST( run_and_compare_to_cuda(diagonal(), nthreads, in, out) ); + + CALL_SUBTEST( run_and_compare_to_cuda(matrix_inverse(), nthreads, in, out) ); + CALL_SUBTEST( run_and_compare_to_cuda(matrix_inverse(), nthreads, in, out) ); + CALL_SUBTEST( run_and_compare_to_cuda(matrix_inverse(), nthreads, in, out) ); + + CALL_SUBTEST( run_and_compare_to_cuda(eigenvalues_direct(), nthreads, in, out) ); + CALL_SUBTEST( run_and_compare_to_cuda(eigenvalues_direct(), nthreads, in, out) ); + CALL_SUBTEST( run_and_compare_to_cuda(eigenvalues(), nthreads, in, out) ); + +} -- cgit v1.2.3