Ziele Untersuchung
mit Columbo Integrität von
Datenbanken Interaktion und
Portierbarkeit Ergonomie der
Schnittstellen

Angebot Produkte Projekt Beratung

Mittel Analytik Modellierung Sprachen Algebra Logik Hardware Denken Kreativität

Zusammenhänge Gesellschaft Wirtschaft Branche Firma


products/Sources/formale Sprachen/C/MySQL/unsupported/test/ (MySQL Server Version 8.1-8.4^©) Datei vom 12.11.2025 mit Größe 3 kB

Quelle cxx11_tensor_image_op_sycl.cpp Sprache: C

// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2016
// Mehdi Goli    Codeplay Software Ltd.
// Ralph Potter  Codeplay Software Ltd.
// Luke Iwanski  Codeplay Software Ltd.
// Contact: <eigen@codeplay.com>
// Benoit Steiner <benoit.steiner.goog@gmail.com>
//
// 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/.

#define EIGEN_TEST_NO_LONGDOUBLE
#define EIGEN_TEST_NO_COMPLEX
#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
#define EIGEN_USE_SYCL

#include "main.h"
#include <unsupported/Eigen/CXX11/Tensor>

using Eigen::array;
using Eigen::SyclDevice;
using Eigen::Tensor;
using Eigen::TensorMap;

using Eigen::Tensor;
using Eigen::RowMajor;
template <typename DataType, int DataLayout, typename IndexType>
static void test_image_op_sycl(const Eigen::SyclDevice &sycl_device)
{
  IndexType sizeDim1 = 245;
  IndexType sizeDim2 = 343;
  IndexType sizeDim3 = 577;

  array<IndexType, 3> input_range ={{sizeDim1, sizeDim2, sizeDim3}};
  array<IndexType, 3> slice_range ={{sizeDim1-1, sizeDim2, sizeDim3}};

  Tensor<DataType, 3,DataLayout, IndexType> tensor1(input_range);
  Tensor<DataType, 3,DataLayout, IndexType> tensor2(input_range);
  Tensor<DataType, 3, DataLayout, IndexType> tensor3(slice_range);
  Tensor<DataType, 3, DataLayout, IndexType> tensor3_cpu(slice_range);

  typedef Eigen::DSizes<IndexType, 3> Index3;
  Index3 strides1(1L,1L, 1L);
  Index3 indicesStart1(1L, 0L, 0L);
  Index3 indicesStop1(sizeDim1, sizeDim2, sizeDim3);

  Index3 strides2(1L,1L, 1L);
  Index3 indicesStart2(0L, 0L, 0L);
  Index3 indicesStop2(sizeDim1-1, sizeDim2, sizeDim3);
  Eigen::DSizes<IndexType, 3> sizes(sizeDim1-1,sizeDim2,sizeDim3);

  tensor1.setRandom();
  tensor2.setRandom();

  DataType* gpu_data1  = static_cast<DataType*>(sycl_device.allocate(tensor1.size()*sizeof(DataType)));
  DataType* gpu_data2  = static_cast<DataType*>(sycl_device.allocate(tensor2.size()*sizeof(DataType)));
  DataType* gpu_data3  = static_cast<DataType*>(sycl_device.allocate(tensor3.size()*sizeof(DataType)));

  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu1(gpu_data1, input_range);
  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu2(gpu_data2, input_range);
  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu3(gpu_data3, slice_range);

  sycl_device.memcpyHostToDevice(gpu_data1, tensor1.data(),(tensor1.size())*sizeof(DataType));
  sycl_device.memcpyHostToDevice(gpu_data2, tensor2.data(),(tensor2.size())*sizeof(DataType));
  gpu3.device(sycl_device)= gpu1.slice(indicesStart1, sizes) - gpu2.slice(indicesStart2, sizes);
  sycl_device.memcpyDeviceToHost(tensor3.data(), gpu_data3,(tensor3.size())*sizeof(DataType));

  tensor3_cpu = tensor1.stridedSlice(indicesStart1,indicesStop1,strides1) - tensor2.stridedSlice(indicesStart2,indicesStop2,strides2);

  for (IndexType i = 0; i <slice_range[0] ; ++i) {
    for (IndexType j = 0; j < slice_range[1]; ++j) {
      for (IndexType k = 0; k < slice_range[2]; ++k) {
        VERIFY_IS_EQUAL(tensor3_cpu(i,j,k), tensor3(i,j,k));
      }
    }
  }
  sycl_device.deallocate(gpu_data1);
  sycl_device.deallocate(gpu_data2);
  sycl_device.deallocate(gpu_data3);
}

template<typename DataType, typename dev_Selector> void sycl_computing_test_per_device(dev_Selector s){
  QueueInterface queueInterface(s);
  auto sycl_device = Eigen::SyclDevice(&queueInterface);
  test_image_op_sycl<DataType, RowMajor, int64_t>(sycl_device);
}

EIGEN_DECLARE_TEST(cxx11_tensor_image_op_sycl) {
  for (const auto& device :Eigen::get_sycl_supported_devices()) {
   CALL_SUBTEST(sycl_computing_test_per_device<float>(device));
#ifdef EIGEN_SYCL_DOUBLE_SUPPORT
   CALL_SUBTEST(sycl_computing_test_per_device<double>(device));
#endif
  }
}

quality72%

¤ Dauer der Verarbeitung: 0.9 Sekunden (vorverarbeitet) ¤

Wurzel

Suchen

Beweissystem der NASA

Beweissystem Isabelle

NIST Cobol Testsuite

Cephes Mathematical Library

Wiener Entwicklungsmethode

Haftungshinweis

Die Informationen auf dieser Webseite wurden nach bestem Wissen sorgfältig zusammengestellt. Es wird jedoch weder Vollständigkeit, noch Richtigkeit, noch Qualität der bereit gestellten Informationen zugesichert.