0_Simple__simpleTexture + 0_Simple__simpleTextureDrv

使用纹理引用来旋转图片，并在使用了静态编译和运行时编译两种环境。

▶ 源代码：静态编译

 #include <stdio.h>
 #include <windows.h>
 #include <cuda_runtime.h>
 #include "device_launch_parameters.h"
 #include <helper_functions.h>
 #include <helper_cuda.h>

 #define MAX_EPSILON_ERROR 5e-3f
 const float angle = 0.5f;
 texture<float, , cudaReadModeElementType> tex;

 __global__ void transformKernel(float *outputData, int width, int height, float theta)
 {
     unsigned int x = blockIdx.x*blockDim.x + threadIdx.x;
     unsigned int y = blockIdx.y*blockDim.y + threadIdx.y;
     float u = x / (float)width - 0.5f;
     float v = y / (float)height - 0.5f;

     outputData[y*width + x] = tex2D(tex, u * cosf(theta) - v * sinf(theta) + 0.5f, v * cosf(theta) + u * sinf(theta) + 0.5f);
 }

 int main()
 {
     printf("\n\tStart.\n");

     // 读取图片数据
     float *h_data = NULL, *h_dataRef = NULL;
     unsigned int width, height, size;
     sdkLoadPGM("D:\\Code\\CUDA\\cudaProjectTemp\\data\\lena_bw.pgm", &h_data, &width, &height);// 删掉了用函数 sdkFindFilePath() 查找输入文件的过程
     size = width * height * sizeof(float);
     sdkLoadPGM("D:\\Code\\CUDA\\cudaProjectTemp\\data\\ref_rotated.pgm", &h_dataRef, &width, &height);
     printf("\n\tLoad input files, %d x %d pixels\n", width, height);

     // 申请设备内存
     float *d_data = NULL;
     cudaMalloc((void **)&d_data, size);
     cudaArray *cuArray;
     cudaChannelFormatDesc channelDesc = cudaCreateChannelDesc(, , , , cudaChannelFormatKindFloat);
     cudaMallocArray(&cuArray, &channelDesc, width, height);
     cudaMemcpyToArray(cuArray, , , h_data, size, cudaMemcpyHostToDevice);// 与 simpleSurfaceWrite 中不同，直接拷贝进 cuArray 

     // 绑定纹理引用
     tex.addressMode[] = cudaAddressModeWrap;
     tex.addressMode[] = cudaAddressModeWrap;
     tex.filterMode = cudaFilterModeLinear;
     tex.normalized = true;
     cudaBindTextureToArray(tex, cuArray, channelDesc);

     // 预跑
     dim3 dimBlock(, , );
     dim3 dimGrid(width / dimBlock.x, height / dimBlock.y, );
     transformKernel << <dimGrid, dimBlock,  >> >(d_data, width, height, angle);
     cudaDeviceSynchronize();

     StopWatchInterface *timer = NULL;
     sdkCreateTimer(&timer);
     sdkStartTimer(&timer);

     transformKernel << <dimGrid, dimBlock,  >> >(d_data, width, height, angle);
     cudaDeviceSynchronize();

     sdkStopTimer(&timer);
     printf("\n\tCost time: %f ms, %.2f Mpixels/sec\n", sdkGetTimerValue(&timer), (width *height / (sdkGetTimerValue(&timer) / 1000.0f)) / 1e6);
     sdkDeleteTimer(&timer);

     // 结果回收、输出和检验
     cudaMemcpy(h_data, d_data, size, cudaMemcpyDeviceToHost);
     sdkSavePGM("D:\\Code\\CUDA\\cudaProjectTemp\\data\\output.pgm", h_data, width, height);
     printf("\n\tSave output file.\n");
     printf("\n\tFinish, return %s.\n", compareData(h_data, h_dataRef, width * height, MAX_EPSILON_ERROR, 0.0f) ? "Passed" : "Failed");

     cudaFree(d_data);
     cudaFreeArray(cuArray);
     getchar();
     return ;
 }

▶ 输出结果

    Start.

    Load input files,  x  pixels

    Cost time: 0.362788 ms, 722.58 Mpixels/sec

    Save output file.

    Finish, return Passed.

▶ 源代码：运行时编译

 // simpleTexture_kernel.cu
 #ifndef _SIMPLETEXTURE_KERNEL_H_
 #define _SIMPLETEXTURE_KERNEL_H_

 texture<float, , cudaReadModeElementType> tex;

 extern "C" __global__ void transformKernel(float *g_odata, int width, int height, float theta)
 {
     unsigned int x = blockIdx.x*blockDim.x + threadIdx.x;
     unsigned int y = blockIdx.y*blockDim.y + threadIdx.y;
     float u = x / (float)width - 0.5f;
     float v = y / (float)height - 0.5f;

     g_odata[y*width + x] = tex2D(tex, u * cosf(theta) - v * sinf(theta) + 0.5f, v * cosf(theta) + u * sinf(theta) + 0.5f);
 }

 #endif

 // simpleTextureDrv.cpp
 #include <stdio.h>
 #include <iostream>
 #include <helper_functions.h>
 #include <cuda.h>

 #define MAX_EPSILON_ERROR 5e-3f
 #define PATH "D:\\Program\\CUDA9.0\\Samples\\0_Simple\\simpleTextureDrv\\data\\"
 using namespace std;
 float angle = 0.5f;
 CUmodule cuModule;
 CUcontext cuContext;

 CUfunction initCUDA()
 {
     CUfunction cuFunction = ;
     string module_path, ptx_source;
     cuInit();                      // 初始化设备，类似于 runtime 中的函数 cudaSetDevice()
     cuCtxCreate(&cuContext, , );  // 创建上下文，后两个参数分别是标志参数和设备号

     // 读取 .ptx 文件
     module_path = PATH"simpleTexture_kernel64.ptx";
     FILE *fp = fopen(module_path.c_str(), "rb");
     fseek(fp, , SEEK_END);
     int file_size = ftell(fp);
     char *buf = new char[file_size + ];
     fseek(fp, , SEEK_SET);
     fread(buf, sizeof(char), file_size, fp);
     fclose(fp);
     buf[file_size] = '\0';
     ptx_source = buf;
     delete[] buf;

     if (module_path.rfind("ptx") != string::npos)// 使用的是.ptx，需要运行时编译
     {
         // 设定编译参数，CUjit_option 放置参数名，jitOptVals 放置参数值
         const unsigned int jitNumOptions = ;
         CUjit_option *jitOptions = new CUjit_option[jitNumOptions];
         void **jitOptVals = new void *[jitNumOptions];

         // 编译日志长度
         jitOptions[] = CU_JIT_INFO_LOG_BUFFER_SIZE_BYTES;
         int jitLogBufferSize = ;
         jitOptVals[] = (void *)(size_t)jitLogBufferSize;

         // 编译日志内容
         jitOptions[] = CU_JIT_INFO_LOG_BUFFER;
         char *jitLogBuffer = new char[jitLogBufferSize];
         jitOptVals[] = jitLogBuffer;

         // 设定一个内核使用的寄存器数量
         jitOptions[] = CU_JIT_MAX_REGISTERS;
         int jitRegCount = ;
         jitOptVals[] = (void *)(size_t)jitRegCount;

         // 编译模块
         cuModuleLoadDataEx(&cuModule, ptx_source.c_str(), jitNumOptions, jitOptions, (void **)jitOptVals);
         //printf("\n\tPTX JIT log:\n%s\n", jitLogBuffer);// 输出编译日志
     }
     else// 使用的是 .cubin，不用编译（本例中不经过这个分支）
         cuModuleLoad(&cuModule, module_path.c_str());

     // 取出编译好的模块中的函数
     cuModuleGetFunction(&cuFunction, cuModule, "transformKernel");
     return cuFunction;// 删掉了错误检查，如果中间某一步出错，则应该先销毁上下文再退出
 }

 int main()
 {
     printf("\n\tStart.\n"); 

     // 初始化设备，编译 PTX
     CUfunction transform = initCUDA();

     // 读取图片数据
     float *h_data = NULL, *h_dataRef = NULL;
     unsigned int width, height, size;
     sdkLoadPGM(PATH"lena_bw.pgm", &h_data, &width, &height);// 删掉了用函数 sdkFindFilePath() 查找输入文件的过程
     size = width * height * sizeof(float);
     sdkLoadPGM(PATH"ref_rotated.pgm", &h_dataRef, &width, &height);
     printf("\n\tLoad input files, %d x %d pixels\n", width, height);

     // 申请设备内存
     CUdeviceptr d_data = (CUdeviceptr)NULL;
     cuMemAlloc(&d_data, size);
     CUarray cu_array;
     CUDA_ARRAY_DESCRIPTOR desc;
     desc.Format = CU_AD_FORMAT_FLOAT;
     desc.NumChannels = ;
     desc.Width = width;
     desc.Height = height;
     cuArrayCreate(&cu_array, &desc);
     CUDA_MEMCPY2D copyParam;
     memset(&copyParam, , sizeof(copyParam));
     copyParam.dstMemoryType = CU_MEMORYTYPE_ARRAY;
     copyParam.dstArray = cu_array;
     copyParam.srcMemoryType = CU_MEMORYTYPE_HOST;
     copyParam.srcHost = h_data;
     copyParam.srcPitch = width * sizeof(float);
     copyParam.WidthInBytes = copyParam.srcPitch;
     copyParam.Height = height;
     cuMemcpy2D(&copyParam);

     // 绑定纹理引用
     CUtexref cu_texref;
     cuModuleGetTexRef(&cu_texref, cuModule, "tex");
     cuTexRefSetArray(cu_texref, cu_array, CU_TRSA_OVERRIDE_FORMAT);
     cuTexRefSetAddressMode(cu_texref, , CU_TR_ADDRESS_MODE_WRAP);
     cuTexRefSetAddressMode(cu_texref, , CU_TR_ADDRESS_MODE_WRAP);
     cuTexRefSetFilterMode(cu_texref, CU_TR_FILTER_MODE_LINEAR);
     cuTexRefSetFlags(cu_texref, CU_TRSF_NORMALIZED_COORDINATES);
     cuTexRefSetFormat(cu_texref, CU_AD_FORMAT_FLOAT, );
     cuParamSetTexRef(transform, CU_PARAM_TR_DEFAULT, cu_texref);

     int block_size = ;
     StopWatchInterface *timer = NULL;

     // 两种调用 Driver API 的方式
     if ()
     {
         void *args[] = {&d_data, &width, &height, &angle};
         // 预跑
         cuLaunchKernel(transform, (width / block_size), (height / block_size), , block_size, block_size, , , NULL, args, NULL);
         cuCtxSynchronize();
         // 再跑一次测试性能
         sdkCreateTimer(&timer);
         sdkStartTimer(&timer);
         cuLaunchKernel(transform, (width / block_size), (height / block_size), , block_size, block_size, , , NULL, args, NULL);
     }
     else
     {
         int offset = ;
         char argBuffer[];
         // 在一个 CUdeviceptr（unsigned long long）长度的空间里写入调用参数
         *((CUdeviceptr *)&argBuffer[offset]) = d_data;
         offset += sizeof(d_data);
         *((unsigned int *)&argBuffer[offset]) = width;
         offset += sizeof(width);
         *((unsigned int *)&argBuffer[offset]) = height;
         offset += sizeof(height);
         *((float *)&argBuffer[offset]) = angle;
         offset += sizeof(angle);
         void *kernel_launch_config[] = {CU_LAUNCH_PARAM_BUFFER_POINTER, argBuffer, CU_LAUNCH_PARAM_BUFFER_SIZE, &offset, CU_LAUNCH_PARAM_END };
         // 预跑
         cuLaunchKernel(transform, (width / block_size), (height / block_size), ,block_size, block_size, ,,NULL, NULL, (void **)&kernel_launch_config);
         cuCtxSynchronize();
         // 再跑一次测试性能
         sdkCreateTimer(&timer);
         sdkStartTimer(&timer);
         cuLaunchKernel(transform, (width / block_size), (height / block_size), ,block_size, block_size, ,, ,NULL, (void **)&kernel_launch_config);
     }
     cuCtxSynchronize();
     sdkStopTimer(&timer);
     printf("\n\tCost time: %f ms, %.2f Mpixels/sec\n", sdkGetTimerValue(&timer), (width *height / (sdkGetTimerValue(&timer) / 1000.0f)) / 1e6);
     sdkDeleteTimer(&timer);

     // 结果回收、输出和检验
     cuMemcpyDtoH(h_data, d_data, size);
     sdkSavePGM("D:\\Code\\CUDA\\cudaProjectTemp\\data\\output.pgm", h_data, width, height);
     printf("\n\tSave output file.\n");
     printf("\n\tFinish, return %s.\n", compareData(h_data, h_dataRef, width * height, MAX_EPSILON_ERROR, 0.15f) ? "Passed" : "Failed");

     cuMemFree(d_data);
     cuArrayDestroy(cu_array);
     cuCtxDestroy(cuContext);
     getchar();
     return ;
 }

▶ 输出结果：

    Start.

    Load input files,  x  pixels

    Cost time: 0.355230 ms, 737.96 Mpixels/sec

    Save output file.

    Finish, return Passed.

▶ 涨姿势

● 一般，与 0_Simple__simpleSurfaceWrite 类似。