1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
|
#ifndef EIGEN_TEST_GPU_COMMON_H
#define EIGEN_TEST_GPU_COMMON_H
#ifdef EIGEN_USE_HIP
#include <hip/hip_runtime.h>
#include <hip/hip_runtime_api.h>
#else
#include <cuda.h>
#include <cuda_runtime.h>
#include <cuda_runtime_api.h>
#endif
#include <iostream>
#define EIGEN_USE_GPU
#include <unsupported/Eigen/CXX11/src/Tensor/TensorGpuHipCudaDefines.h>
#if !defined(__CUDACC__) && !defined(__HIPCC__)
dim3 threadIdx, blockDim, blockIdx;
#endif
template<typename Kernel, typename Input, typename Output>
void run_on_cpu(const Kernel& ker, int n, const Input& in, Output& out)
{
for(int i=0; i<n; i++)
ker(i, in.data(), out.data());
}
template<typename Kernel, typename Input, typename Output>
__global__
void run_on_gpu_meta_kernel(const Kernel ker, int n, const Input* in, Output* out)
{
int i = threadIdx.x + blockIdx.x*blockDim.x;
if(i<n) {
ker(i, in, out);
}
}
template<typename Kernel, typename Input, typename Output>
void run_on_gpu(const Kernel& ker, int n, const Input& in, Output& out)
{
typename Input::Scalar* d_in;
typename Output::Scalar* d_out;
std::ptrdiff_t in_bytes = in.size() * sizeof(typename Input::Scalar);
std::ptrdiff_t out_bytes = out.size() * sizeof(typename Output::Scalar);
gpuMalloc((void**)(&d_in), in_bytes);
gpuMalloc((void**)(&d_out), out_bytes);
gpuMemcpy(d_in, in.data(), in_bytes, gpuMemcpyHostToDevice);
gpuMemcpy(d_out, out.data(), out_bytes, gpuMemcpyHostToDevice);
// Simple and non-optimal 1D mapping assuming n is not too large
// That's only for unit testing!
dim3 Blocks(128);
dim3 Grids( (n+int(Blocks.x)-1)/int(Blocks.x) );
gpuDeviceSynchronize();
#ifdef EIGEN_USE_HIP
hipLaunchKernelGGL(run_on_gpu_meta_kernel<Kernel,
typename std::decay<decltype(*d_in)>::type,
typename std::decay<decltype(*d_out)>::type>,
dim3(Grids), dim3(Blocks), 0, 0, ker, n, d_in, d_out);
#else
run_on_gpu_meta_kernel<<<Grids,Blocks>>>(ker, n, d_in, d_out);
#endif
gpuDeviceSynchronize();
// check inputs have not been modified
gpuMemcpy(const_cast<typename Input::Scalar*>(in.data()), d_in, in_bytes, gpuMemcpyDeviceToHost);
gpuMemcpy(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost);
gpuFree(d_in);
gpuFree(d_out);
}
template<typename Kernel, typename Input, typename Output>
void run_and_compare_to_gpu(const Kernel& ker, int n, const Input& in, Output& out)
{
Input in_ref, in_gpu;
Output out_ref, out_gpu;
#if !defined(__CUDA_ARCH__) && !defined(__HIP_DEVICE_COMPILE__)
in_ref = in_gpu = in;
out_ref = out_gpu = out;
#else
EIGEN_UNUSED_VARIABLE(in);
EIGEN_UNUSED_VARIABLE(out);
#endif
run_on_cpu (ker, n, in_ref, out_ref);
run_on_gpu(ker, n, in_gpu, out_gpu);
#if !defined(__CUDA_ARCH__) && !defined(__HIP_DEVICE_COMPILE__)
VERIFY_IS_APPROX(in_ref, in_gpu);
VERIFY_IS_APPROX(out_ref, out_gpu);
#endif
}
void ei_test_init_gpu()
{
int device = 0;
gpuDeviceProp_t deviceProp;
gpuGetDeviceProperties(&deviceProp, device);
std::cout << "GPU device info:\n";
std::cout << " name: " << deviceProp.name << "\n";
std::cout << " capability: " << deviceProp.major << "." << deviceProp.minor << "\n";
std::cout << " multiProcessorCount: " << deviceProp.multiProcessorCount << "\n";
std::cout << " maxThreadsPerMultiProcessor: " << deviceProp.maxThreadsPerMultiProcessor << "\n";
std::cout << " warpSize: " << deviceProp.warpSize << "\n";
std::cout << " regsPerBlock: " << deviceProp.regsPerBlock << "\n";
std::cout << " concurrentKernels: " << deviceProp.concurrentKernels << "\n";
std::cout << " clockRate: " << deviceProp.clockRate << "\n";
std::cout << " canMapHostMemory: " << deviceProp.canMapHostMemory << "\n";
std::cout << " computeMode: " << deviceProp.computeMode << "\n";
}
#endif // EIGEN_TEST_GPU_COMMON_H
|
