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
|
/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of NVIDIA CORPORATION nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
/*
* Example of integrating CUDA functions into an existing
* application / framework.
* Host part of the device code.
* Compiled with Cuda compiler.
*/
// System includes
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <math.h>
#include <assert.h>
// CUDA runtime
#include <cuda_runtime.h>
// helper functions and utilities to work with CUDA
#include <helper_cuda.h>
#include <helper_functions.h>
#ifndef MAX
#define MAX(a, b) (a > b ? a : b)
#endif
////////////////////////////////////////////////////////////////////////////////
// declaration, forward
extern "C" void computeGold(char *reference, char *idata,
const unsigned int len);
extern "C" void computeGold2(int2 *reference, int2 *idata,
const unsigned int len);
///////////////////////////////////////////////////////////////////////////////
//! Simple test kernel for device functionality
//! @param g_odata memory to process (in and out)
///////////////////////////////////////////////////////////////////////////////
__global__ void kernel(int *g_data) {
// write data to global memory
const unsigned int tid = threadIdx.x;
int data = g_data[tid];
// use integer arithmetic to process all four bytes with one thread
// this serializes the execution, but is the simplest solutions to avoid
// bank conflicts for this very low number of threads
// in general it is more efficient to process each byte by a separate thread,
// to avoid bank conflicts the access pattern should be
// g_data[4 * wtid + wid], where wtid is the thread id within the half warp
// and wid is the warp id
// see also the programming guide for a more in depth discussion.
g_data[tid] =
((((data << 0) >> 24) - 10) << 24) | ((((data << 8) >> 24) - 10) << 16) |
((((data << 16) >> 24) - 10) << 8) | ((((data << 24) >> 24) - 10) << 0);
}
///////////////////////////////////////////////////////////////////////////////
//! Demonstration that int2 data can be used in the cpp code
//! @param g_odata memory to process (in and out)
///////////////////////////////////////////////////////////////////////////////
__global__ void kernel2(int2 *g_data) {
// write data to global memory
const unsigned int tid = threadIdx.x;
int2 data = g_data[tid];
// use integer arithmetic to process all four bytes with one thread
// this serializes the execution, but is the simplest solutions to avoid
// bank conflicts for this very low number of threads
// in general it is more efficient to process each byte by a separate thread,
// to avoid bank conflicts the access pattern should be
// g_data[4 * wtid + wid], where wtid is the thread id within the half warp
// and wid is the warp id
// see also the programming guide for a more in depth discussion.
g_data[tid].x = data.x - data.y;
}
////////////////////////////////////////////////////////////////////////////////
//! Entry point for Cuda functionality on host side
//! @param argc command line argument count
//! @param argv command line arguments
//! @param data data to process on the device
//! @param len len of \a data
////////////////////////////////////////////////////////////////////////////////
extern "C" bool runTest(const int argc, const char **argv, char *data,
int2 *data_int2, unsigned int len) {
// use command-line specified CUDA device, otherwise use device with highest
// Gflops/s
findCudaDevice(argc, (const char **)argv);
const unsigned int num_threads = len / 4;
assert(0 == (len % 4));
const unsigned int mem_size = sizeof(char) * len;
const unsigned int mem_size_int2 = sizeof(int2) * len;
// allocate device memory
char *d_data;
checkCudaErrors(cudaMalloc((void **)&d_data, mem_size));
// copy host memory to device
checkCudaErrors(cudaMemcpy(d_data, data, mem_size, cudaMemcpyHostToDevice));
// allocate device memory for int2 version
int2 *d_data_int2;
checkCudaErrors(cudaMalloc((void **)&d_data_int2, mem_size_int2));
// copy host memory to device
checkCudaErrors(cudaMemcpy(d_data_int2, data_int2, mem_size_int2,
cudaMemcpyHostToDevice));
// setup execution parameters
dim3 grid(1, 1, 1);
dim3 threads(num_threads, 1, 1);
dim3 threads2(len, 1, 1); // more threads needed fir separate int2 version
// execute the kernel
kernel<<<grid, threads>>>((int *)d_data);
kernel2<<<grid, threads2>>>(d_data_int2);
// check if kernel execution generated and error
getLastCudaError("Kernel execution failed");
// compute reference solutions
char *reference = (char *)malloc(mem_size);
computeGold(reference, data, len);
int2 *reference2 = (int2 *)malloc(mem_size_int2);
computeGold2(reference2, data_int2, len);
// copy results from device to host
checkCudaErrors(cudaMemcpy(data, d_data, mem_size, cudaMemcpyDeviceToHost));
checkCudaErrors(cudaMemcpy(data_int2, d_data_int2, mem_size_int2,
cudaMemcpyDeviceToHost));
// check result
bool success = true;
for (unsigned int i = 0; i < len; i++) {
if (reference[i] != data[i] || reference2[i].x != data_int2[i].x ||
reference2[i].y != data_int2[i].y) {
success = false;
}
}
// cleanup memory
checkCudaErrors(cudaFree(d_data));
checkCudaErrors(cudaFree(d_data_int2));
free(reference);
free(reference2);
return success;
}
|
