-
Notifications
You must be signed in to change notification settings - Fork 358
/
Copy pathsimple_fft_block_cub_io.cu
118 lines (97 loc) · 4.37 KB
/
simple_fft_block_cub_io.cu
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
#include <iostream>
#include <vector>
#include <cuda_runtime_api.h>
#include <cufftdx.hpp>
#include <cub/cub.cuh>
#include <cub/version.cuh>
#include <cub/block/block_load.cuh>
#include <cub/block/block_store.cuh>
#include "block_io.hpp"
#include "common.hpp"
#if CUB_VERSION < 101300
int main(int, char**) {
std::cout << "Example disabled, BLOCK_LOAD_STRIPED/BLOCK_STORE_STRIPED is only supported since CUB 1.13 (CUDA 11.5)" << std::endl;
return 0;
}
#else
template<class FFT>
__launch_bounds__(FFT::max_threads_per_block) __global__ void block_fft_kernel(typename FFT::value_type* data) {
using complex_type = typename FFT::value_type;
// FFT::stride == FFT::block_dim.x in most cases
using BlockLoad = cub::BlockLoad <complex_type, FFT::stride /* BLOCK_DIM_X */, FFT::storage_size, cub::BLOCK_LOAD_STRIPED>;
using BlockStore = cub::BlockStore<complex_type, FFT::stride, FFT::storage_size, cub::BLOCK_STORE_STRIPED>;
// Local array for thread
complex_type thread_data[FFT::storage_size];
// ID of FFT in CUDA block, in range [0; FFT::ffts_per_block)
const unsigned int local_fft_id = threadIdx.y;
// ID of FFT in CUDA grid
unsigned int global_fft_id =
(FFT::ffts_per_block == 1) ? local_fft_id : ( blockIdx.x * FFT::ffts_per_block + local_fft_id);
// Load data from global memory to registers
auto fft_data = data + (global_fft_id * cufftdx::size_of<FFT>::value);
BlockLoad().Load(fft_data, thread_data, cufftdx::size_of<FFT>::value, complex_type { 0.0, 0.0 });
// Execute FFT
extern __shared__ complex_type shared_mem[];
FFT().execute(thread_data, shared_mem);
// Save results
BlockStore().Store(fft_data, thread_data, cufftdx::size_of<FFT>::value);
}
// In this example a one-dimensional complex-to-complex transform is performed by a CUDA block. CUB
// library is used for IO in kernel.
//
// One block is run, it calculates two 128-point C2C float precision FFTs.
// Data is generated on host, copied to device buffer, and then results are copied back to host.
template<unsigned int Arch>
void simple_block_fft() {
using namespace cufftdx;
// FFT is defined, its: size, type, direction, precision. Block() operator informs that FFT
// will be executed on block level. Shared memory is required for co-operation between threads.
// Additionally,
using FFT = decltype(Block() + Size<128>() + Type<fft_type::c2c>() + Direction<fft_direction::forward>() +
Precision<float>() + ElementsPerThread<8>() + FFTsPerBlock<2>() + SM<Arch>());
#if CUFFTDX_EXAMPLE_DETAIL_NVCC_12_2_BUG_WORKAROUND
using complex_type = example::value_type_t<FFT>;
#else
using complex_type = typename FFT::value_type;
#endif
// Allocate managed memory for input/output
complex_type* data;
auto size = FFT::ffts_per_block * cufftdx::size_of<FFT>::value;
auto size_bytes = size * sizeof(complex_type);
CUDA_CHECK_AND_EXIT(cudaMallocManaged(&data, size_bytes));
for (size_t i = 0; i < size; i++) {
data[i] = complex_type {float(i), -float(i)};
}
std::cout << "input [1st FFT]:\n";
for (size_t i = 0; i < cufftdx::size_of<FFT>::value; i++) {
std::cout << data[i].x << " " << data[i].y << std::endl;
}
// Increase max shared memory if needed
CUDA_CHECK_AND_EXIT(cudaFuncSetAttribute(
block_fft_kernel<FFT>,
cudaFuncAttributeMaxDynamicSharedMemorySize,
FFT::shared_memory_size));
// Invokes kernel with FFT::block_dim threads in CUDA block
block_fft_kernel<FFT><<<1, FFT::block_dim, FFT::shared_memory_size>>>(data);
CUDA_CHECK_AND_EXIT(cudaPeekAtLastError());
CUDA_CHECK_AND_EXIT(cudaDeviceSynchronize());
std::cout << "output [1st FFT]:\n";
for (size_t i = 0; i < cufftdx::size_of<FFT>::value; i++) {
std::cout << data[i].x << " " << data[i].y << std::endl;
}
auto sum = data[0].x;
CUDA_CHECK_AND_EXIT(cudaFree(data));
if(std::abs(sum - ((cufftdx::size_of<FFT>::value-1) * cufftdx::size_of<FFT>::value / 2)) > 0.1) {
std::cout << "Failed" << std::endl;
return;
}
std::cout << "Success" << std::endl;
}
template<unsigned int Arch>
struct simple_block_fft_functor {
void operator()() { return simple_block_fft<Arch>(); }
};
int main(int, char**) {
return example::sm_runner<simple_block_fft_functor>();
}
#endif // CUB_VERSION < 101300