1 files changed, 0 insertions, 170 deletions
diff --git a/eigen/test/cuda_basic.cu b/eigen/test/cuda_basic.cu
deleted file mode 100644
index ce66c2c..0000000
--- a/eigen/test/cuda_basic.cu
+++ /dev/null
@@ -1,170 +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>
-#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) );
-
-}