CwiseMul.cpp
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1 #include <iostream>
2 #define EIGEN_USE_SYCL
3 #include <unsupported/Eigen/CXX11/Tensor>
4 
5 using Eigen::array;
6 using Eigen::SyclDevice;
7 using Eigen::Tensor;
8 using Eigen::TensorMap;
9 
10 int main()
11 {
12  using DataType = float;
13  using IndexType = int64_t;
14  constexpr auto DataLayout = Eigen::RowMajor;
15 
16  auto devices = Eigen::get_sycl_supported_devices();
17  const auto device_selector = *devices.begin();
18  Eigen::QueueInterface queueInterface(device_selector);
19  auto sycl_device = Eigen::SyclDevice(&queueInterface);
20 
21  // create the tensors to be used in the operation
22  IndexType sizeDim1 = 3;
23  IndexType sizeDim2 = 3;
24  IndexType sizeDim3 = 3;
25  array<IndexType, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
26 
27  // initialize the tensors with the data we want manipulate to
31 
32  // set up some random data in the tensors to be multiplied
33  in1 = in1.random();
34  in2 = in2.random();
35 
36  // allocate memory for the tensors
37  DataType * gpu_in1_data = static_cast<DataType*>(sycl_device.allocate(in1.size()*sizeof(DataType)));
38  DataType * gpu_in2_data = static_cast<DataType*>(sycl_device.allocate(in2.size()*sizeof(DataType)));
39  DataType * gpu_out_data = static_cast<DataType*>(sycl_device.allocate(out.size()*sizeof(DataType)));
40 
41  //
42  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_in1(gpu_in1_data, tensorRange);
43  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_in2(gpu_in2_data, tensorRange);
44  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_out(gpu_out_data, tensorRange);
45 
46  // copy the memory to the device and do the c=a*b calculation
47  sycl_device.memcpyHostToDevice(gpu_in1_data, in1.data(),(in1.size())*sizeof(DataType));
48  sycl_device.memcpyHostToDevice(gpu_in2_data, in2.data(),(in2.size())*sizeof(DataType));
49  gpu_out.device(sycl_device) = gpu_in1 * gpu_in2;
50  sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.size())*sizeof(DataType));
51  sycl_device.synchronize();
52 
53  // print out the results
54  for (IndexType i = 0; i < sizeDim1; ++i) {
55  for (IndexType j = 0; j < sizeDim2; ++j) {
56  for (IndexType k = 0; k < sizeDim3; ++k) {
57  std::cout << "device_out" << "(" << i << ", " << j << ", " << k << ") : " << out(i,j,k)
58  << " vs host_out" << "(" << i << ", " << j << ", " << k << ") : " << in1(i,j,k) * in2(i,j,k) << "\n";
59  }
60  }
61  }
62  printf("c=a*b Done\n");
63 }
Eigen::Tensor
The tensor class.
Definition: Tensor.h:63
array
int array[24]
Definition: Map_general_stride.cpp:1
Eigen::array
Definition: EmulateArray.h:21
DataLayout
static const int DataLayout
Definition: cxx11_tensor_image_patch_sycl.cpp:24
Eigen::RowMajor
@ RowMajor
Definition: Constants.h:321
j
std::ptrdiff_t j
Definition: tut_arithmetic_redux_minmax.cpp:2
int64_t
signed __int64 int64_t
Definition: ms_stdint.h:94
Eigen::TensorMap
A tensor expression mapping an existing array of data.
Definition: TensorForwardDeclarations.h:52
out
std::ofstream out("Result.txt")
main
int main()
Definition: CwiseMul.cpp:10
Eigen::Tensor::data
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar * data()
Definition: Tensor.h:104
gtsam.examples.DogLegOptimizerExample.float
float
Definition: DogLegOptimizerExample.py:113
Eigen::Tensor::size
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index size() const
Definition: Tensor.h:103
i
int i
Definition: BiCGSTAB_step_by_step.cpp:9


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autogenerated on Sat Nov 16 2024 04:02:07