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56 lines
1.8 KiB
C++
56 lines
1.8 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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#define EIGEN_TEST_NO_LONGDOUBLE
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#define EIGEN_TEST_NO_COMPLEX
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#define EIGEN_TEST_FUNC cxx11_tensor_reduction_cuda
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#define EIGEN_USE_GPU
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#include "main.h"
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#include <unsupported/Eigen/CXX11/Tensor>
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template<int DataLayout>
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static void test_full_reductions() {
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Eigen::GpuDevice gpu_device;
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const int num_rows = internal::random<int>(1024, 5*1024);
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const int num_cols = internal::random<int>(1024, 5*1024);
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Tensor<float, 2, DataLayout> in(num_rows, num_cols);
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in.setRandom();
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Tensor<float, 1, DataLayout> full_redux(1);
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full_redux = in.sum();
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std::size_t in_bytes = in.size() * sizeof(float);
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std::size_t out_bytes = full_redux.size() * sizeof(float);
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float* gpu_in_ptr = static_cast<float*>(gpu_device.allocate(in_bytes));
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float* gpu_out_ptr = static_cast<float*>(gpu_device.allocate(out_bytes));
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gpu_device.memcpyHostToDevice(gpu_in_ptr, in.data(), in_bytes);
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TensorMap<Tensor<float, 2, DataLayout> > in_gpu(gpu_in_ptr, num_rows, num_cols);
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TensorMap<Tensor<float, 1, DataLayout> > out_gpu(gpu_out_ptr, 1);
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out_gpu.device(gpu_device) = in_gpu.sum();
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Tensor<float, 1, DataLayout> full_redux_gpu(1);
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gpu_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_ptr, out_bytes);
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gpu_device.synchronize();
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// Check that the CPU and GPU reductions return the same result.
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VERIFY_IS_APPROX(full_redux(0), full_redux_gpu(0));
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}
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void test_cxx11_tensor_reduction_cuda() {
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CALL_SUBTEST(test_full_reductions<ColMajor>());
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CALL_SUBTEST(test_full_reductions<RowMajor>());
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}
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