-
Notifications
You must be signed in to change notification settings - Fork 14
/
Copy pathj_simple_with_buffer.cpp
233 lines (197 loc) · 9.1 KB
/
j_simple_with_buffer.cpp
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
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
#include <sycl/sycl.hpp>
#include <ctime>
#include <chrono>
#include <getopt.h>
using namespace sycl;
int main(int argc, char *argv[]) {
size_t N = 1024;
size_t M = 16;
int VERIFY = 0;
int PRINT_OUTPUT_MATRIX = 0;
int arg;
while ((arg = getopt (argc, argv, "n:m:vp")) != -1)
switch (arg){
case 'n':
N = std::atoi(optarg);
break;
case 'm':
M = std::atoi(optarg);
break;
case 'v':
VERIFY = 1;
break;
case 'p':
PRINT_OUTPUT_MATRIX = 1;
break;
case 'h':
std::cout << std::endl;
std::cout << "Usage : ./a.out -n <MATRIX_SIZE> -m <WORK_GROUP_SIZE> -v -p\n\n";
std::cout << " [-n] size for matrix, eg: 1024\n";
std::cout << " [-m] size of work_group, eg: 8/16\n";
std::cout << " [-v] verify output with linear computation on cpu\n";
std::cout << " [-p] print output matrix\n";
std::cout << "Example : ./a.out -n 1024 -m 16 -v -p\n\n";
std::exit(0);
}
//# Define vectors for matrices
const int nx=N, ny=N;
const int niter=100;
const float factor =0.5;
std::vector<float> matrix_u(nx*ny);
std::vector<float> matrix_unew(nx*ny);
// Initialize u
for (int i = 0; i < nx; i++) {
for (int j = 0; j < ny; j++) {
int ind = i * ny + j;
matrix_u[ind] = ((i - nx / 2) * (i - nx / 2)) / nx +
((j - ny / 2) * (j - ny / 2)) / ny;
}
}
//# Define queue with default device for offloading computation
sycl::property_list q_prof{property::queue::enable_profiling{}, sycl::property::queue::in_order{}}; // we enable profiling and ensure that the kernels are executed in the order of submission
//queue q{property::queue::enable_profiling{}};
queue q{default_selector_v,q_prof}; // selects automatically the best device available
//queue q{property::queue::enable_profiling{}};
//queue q();
//queue q{property::queue::enable_profiling{}, property::queue::in_order()};
std::cout << "Offload Device : " << q.get_device().get_info<info::device::name>() << "\n";
std::cout << "max_work_group_size : " << q.get_device().get_info<info::device::max_work_group_size>() << "\n";
std::cout << "Configuration : MATRIX_SIZE= " << nx << "x" << ny << "\n";
std::cout << " [0][0] = " << matrix_u[0] << "\n";
std::cout << "Warm up the device \n";
{
//# Create buffers for matrices
buffer<float, 1> u(matrix_u.data(), range<1>(nx*ny));
buffer<float, 1> unew(matrix_unew.data(), range<1>(nx*ny));
//# Submit command groups to execute on device
q.submit([&](handler &h){
//# Create accessors to copy buffers to the device
auto U = u.get_access<access::mode::read>(h);
auto UNEW= unew.get_access<access::mode::write>(h);
//# Define size for ND-Range and work-group size
range<2> global_size(nx,ny);
range<2> work_group_size(M,M);
//# Parallel Compute
h.parallel_for(nd_range<2>{global_size, work_group_size}, [=](nd_item<2> item){
const int i = item.get_global_id(0);
const int j = item.get_global_id(1);
int ind = i * ny + j;
int ip = (i + 1) * ny + j;
int im = (i - 1) * ny + j;
int jp = i * ny + j + 1;
int jm = i * ny + j - 1;
if(i>0 && i<nx-1 && j>0 && j< ny-1){
UNEW[ind] = factor * (U[ip] - 2.0 * U[ind] + U[im] +
U[jp] - 2.0 * U[ind] + U[jm]);
}
});
});
}
{
//# Create buffers for matrices
buffer<float, 1> u(matrix_u.data(), range<1>(nx*ny));
buffer<float, 1> unew(matrix_unew.data(), range<1>(nx*ny));
//# Submit command groups to execute on device
q.submit([&](handler &h){
//# Create accessors to copy buffers to the device
auto U = unew.get_access<access::mode::read>(h);
auto UNEW= u.get_access<access::mode::write>(h);
//# Define size for ND-Range and work-group size
range<2> global_size(nx,ny);
range<2> work_group_size(M,M);
//# Parallel Compute
h.parallel_for(nd_range<2>{global_size, work_group_size}, [=](nd_item<2> item){
const int i = item.get_global_id(0);
const int j = item.get_global_id(1);
int ind = i * ny + j;
int ip = (i + 1) * ny + j;
int im = (i - 1) * ny + j;
int jp = i * ny + j + 1;
int jm = i * ny + j - 1;
if(i>0 && i<nx-1 && j>0 && j< ny-1){
UNEW[ind] = factor * (U[ip] - 2.0 * U[ind] + U[im] +
U[jp] - 2.0 * U[ind] + U[jm]);
}
});
});
}
q.wait();
auto start = std::chrono::high_resolution_clock::now().time_since_epoch().count();
event e;
auto kernel_duration=0.0;
for(int iter=0;iter<niter; iter++)
{
{
//# Create buffers for matrices
buffer<float, 1> u(matrix_u.data(), range<1>(nx*ny));
buffer<float, 1> unew(matrix_unew.data(), range<1>(nx*ny));
//# Submit command groups to execute on device
e = q.submit([&](handler &h){
//# Create accessors to copy buffers to the device
auto U = u.get_access<access::mode::read>(h);
auto UNEW= unew.get_access<access::mode::write>(h);
//# Define size for ND-Range and work-group size
range<2> global_size(nx,ny);
range<2> work_group_size(M,M);
//# Parallel Compute
h.parallel_for(nd_range<2>{global_size, work_group_size}, [=](nd_item<2> item){
const int i = item.get_global_id(0);
const int j = item.get_global_id(1);
int ind = i * ny + j;
int ip = (i + 1) * ny + j;
int im = (i - 1) * ny + j;
int jp = i * ny + j + 1;
int jm = i * ny + j - 1;
if(i>0 && i<nx-1 && j>0 && j< ny-1){
UNEW[ind] = factor * (U[ip] - 2.0 * U[ind] + U[im] +
U[jp] - 2.0 * U[ind] + U[jm]);
}
});
});
}
kernel_duration += (e.get_profiling_info<info::event_profiling::command_end>() - e.get_profiling_info<info::event_profiling::command_start>());
{
//# Create buffers for matrices
buffer<float, 1> u(matrix_u.data(), range<1>(nx*ny));
buffer<float, 1> unew(matrix_unew.data(), range<1>(nx*ny));
//# Submit command groups to execute on device
e = q.submit([&](handler &h){
//# Create accessors to copy buffers to the device
auto U = unew.get_access<access::mode::read>(h);
auto UNEW= u.get_access<access::mode::write>(h);
//# Define size for ND-Range and work-group size
range<2> global_size(nx,ny);
range<2> work_group_size(M,M);
//# Parallel Compute
h.parallel_for(nd_range<2>{global_size, work_group_size}, [=](nd_item<2> item){
const int i = item.get_global_id(0);
const int j = item.get_global_id(1);
int ind = i * ny + j;
int ip = (i + 1) * ny + j;
int im = (i - 1) * ny + j;
int jp = i * ny + j + 1;
int jm = i * ny + j - 1;
if(i>0 && i<nx-1 && j>0 && j< ny-1){
UNEW[ind] = factor * (U[ip] - 2.0 * U[ind] + U[im] +
U[jp] - 2.0 * U[ind] + U[jm]);
}
});
});
}
kernel_duration += (e.get_profiling_info<info::event_profiling::command_end>() - e.get_profiling_info<info::event_profiling::command_start>());
}
std::cout << "Kernel Execution Time : " << kernel_duration / 1e+9 << " seconds" << "\n";
auto duration = std::chrono::high_resolution_clock::now().time_since_epoch().count() - start;
std::cout << "Compute Duration : " << duration / 1e+9 << " seconds\n";
//# Print Output
if (PRINT_OUTPUT_MATRIX){
/*for (int i=0; i<N; i++){
for (int j=0; j<N; j++){
std::cout << matrix_c[i*N+j] << " ";
}
std::cout << "\n";
}*/
} else {
std::cout << " [0][0] = " << matrix_u[0] << "\n";
}
}