0_Simple__simpleCubemapTexture

立方体纹理贴图

▶ 源代码。用纹理方法把元素按原顺序从 CUDA3D 数组中取出来，求个相反数放入全局内存，输出。

 #include <stdio.h>

 #include "cuda_runtime.h"

 #include "device_launch_parameters.h"

 #include <helper_functions.h>

 #include <helper_cuda.h>

 #define MIN_EPSILON_ERROR 5e-3f

 texture<float, cudaTextureTypeCubemap> tex;

 __global__ void transformKernel(float *g_odata, int width)

 {

     unsigned int x = blockIdx.x*blockDim.x + threadIdx.x;

     unsigned int y = blockIdx.y*blockDim.y + threadIdx.y;

     float u = ((x + 0.5f) / (float)width) * .f - .f;// [0, width-1] 间隔 1 的坐标变换为 [-1+1/width,1-1/width] 间隔 1/width 的坐标

     float v = ((y + 0.5f) / (float)width) * .f - .f;

     float cx, cy, cz;

     for (unsigned int face = ; face < ; face++)

     {

         if (face == )// x 正层

         {

             cx = ;

             cy = -v;

             cz = -u;

         }

         else if (face == )// x 负层

         {

             cx = -;

             cy = -v;

             cz = u;

         }

         else if (face == )// y 正层

         {

             cx = u;

             cy = ;

             cz = v;

         }

         else if (face == )// y 负层

         {

             cx = u;

             cy = -;

             cz = -v;

         }

         else if (face == )// z 正层

         {

             cx = u;

             cy = -v;

             cz = ;

         }

         else if (face == )// z 负层

         {

             cx = -u;

             cy = -v;

             cz = -;

         }

         g_odata[face*width*width + y*width + x] = - texCubemap(tex, cx, cy, cz);// 纹理数据读取到全局内存中输出

     }

 }

 int main(int argc, char** argv)

 {

     unsigned int width = , num_faces = , num_layers = ;

     unsigned int cubemap_size = width * width * num_faces;

     unsigned int size = cubemap_size * num_layers * sizeof(float);

     float *h_data = (float *)malloc(size);

     float *h_data_ref = (float *)malloc(size);  // 理论输出

     float *d_data = NULL;

     cudaMalloc((void **)&d_data, size);

     for (int i = ; i < (int)(cubemap_size * num_layers); i++)

         h_data[i] = (float)i;

     for (unsigned int layer = ; layer < num_layers; layer++)

     {

         for (int i = ; i < (int)(cubemap_size); i++)

             h_data_ref[layer*cubemap_size + i] = -h_data[layer*cubemap_size + i] + layer;

     }

     printf("\n\t\Input data.n\t");

     for (int i = ; i < width * num_faces * num_layers; i++)

     {

         printf("%2.1f ", h_data[i]);

         if ((i + ) % width == )

             printf("\n\t");

         if ((i + ) % (width *width) == )

             printf("\n\t");

     }

     printf("\n\tIdeal output data\n\t");

     for (int i = ; i < width * num_faces * num_layers; i++)

     {

         printf("%2.1f ", h_data_ref[i]);

         if ((i + ) % width == )

             printf("\n\t");

         if ((i + ) % (width *width) == )

             printf("\n\t");

     }

     // 设置 CUDA 3D 数组参数和数据拷贝

     cudaChannelFormatDesc channelDesc = cudaCreateChannelDesc(, , , , cudaChannelFormatKindFloat);

     cudaArray *cu_3darray;

     cudaMalloc3DArray(&cu_3darray, &channelDesc, make_cudaExtent(width, width, num_faces), cudaArrayCubemap);

     cudaMemcpy3DParms myparms = {  };

     myparms.srcPos = make_cudaPos(, , );

     myparms.dstPos = make_cudaPos(, , );

     myparms.srcPtr = make_cudaPitchedPtr(h_data, width * sizeof(float), width, width);

     myparms.dstArray = cu_3darray;

     myparms.extent = make_cudaExtent(width, width, num_faces);

     myparms.kind = cudaMemcpyHostToDevice;

     cudaMemcpy3D(&myparms);

     // 设置纹理参数并绑定

     tex.addressMode[] = cudaAddressModeWrap;

     tex.addressMode[] = cudaAddressModeWrap;

     tex.filterMode = cudaFilterModeLinear;

     tex.normalized = true;

     cudaBindTextureToArray(tex, cu_3darray, channelDesc);

     dim3 dimBlock(, , );

     dim3 dimGrid(width / dimBlock.x, width / dimBlock.y, );

     printf("\n\tCubemap data of %d * %d * %d: Grid size is %d x %d, each block has 8 x 8 threads.\n", width, width, num_layers, dimGrid.x, dimGrid.y);

     transformKernel << < dimGrid, dimBlock >> >(d_data, width);// 预跑

     cudaDeviceSynchronize();

     StopWatchInterface *timer = NULL;// 新的计时工具

     sdkCreateTimer(&timer);

     sdkStartTimer(&timer);

     transformKernel << < dimGrid, dimBlock,  >> >(d_data, width);

     cudaDeviceSynchronize();

     sdkStopTimer(&timer);

     printf("\n\Time: %.3f msec, %.2f Mtexlookups/sec\n", sdkGetTimerValue(&timer), (cubemap_size / (sdkGetTimerValue(&timer) / 1000.0f) / 1e6));

     sdkDeleteTimer(&timer);

     // 返回计算结果并检验

     memset(h_data, , size);

     cudaMemcpy(h_data, d_data, size, cudaMemcpyDeviceToHost);

     if (checkCmdLineFlag(argc, (const char **)argv, "regression"))

         sdkWriteFile<float>("./data/regression.dat", h_data, width * width, 0.0f, false);

     else

         printf("Comparing kernel output to expected data return %d\n", compareData(h_data, h_data_ref, cubemap_size, MIN_EPSILON_ERROR, 0.0f));

     printf("\n\tActual output data\n\t");

     for (int i = ; i < width * num_faces * num_layers; i++)

     {

         printf("%2.1f ", h_data[i]);

         if ((i + ) % width == )

             printf("\n\t");

         if ((i + ) % (width * width) == )

             printf("\n\t");

     }

     free(h_data);

     free(h_data_ref);

     cudaFree(d_data);

     cudaFreeArray(cu_3darray);

     getchar();

     return ;

 }

▶ 输出结果

    Input data.n    0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 14.0 15.0 16.0 17.0 18.0 19.0 20.0 21.0 22.0 23.0 24.0 25.0 26.0 27.0 28.0 29.0 30.0 31.0 32.0 33.0 34.0 35.0 36.0 37.0 38.0 39.0 40.0 41.0 42.0 43.0 44.0 45.0 46.0 47.0 48.0 49.0 50.0 51.0 52.0 53.0 54.0 55.0 56.0 57.0 58.0 59.0 60.0 61.0 62.0 63.0

    64.0 65.0 66.0 67.0 68.0 69.0 70.0 71.0 72.0 73.0 74.0 75.0 76.0 77.0 78.0 79.0 80.0 81.0 82.0 83.0 84.0 85.0 86.0 87.0 88.0 89.0 90.0 91.0 92.0 93.0 94.0 95.0 96.0 97.0 98.0 99.0 100.0 101.0 102.0 103.0 104.0 105.0 106.0 107.0 108.0 109.0 110.0 111.0 112.0 113.0 114.0 115.0 116.0 117.0 118.0 119.0 120.0 121.0 122.0 123.0 124.0 125.0 126.0 127.0

    128.0 129.0 130.0 131.0 132.0 133.0 134.0 135.0 136.0 137.0 138.0 139.0 140.0 141.0 142.0 143.0 144.0 145.0 146.0 147.0 148.0 149.0 150.0 151.0 152.0 153.0 154.0 155.0 156.0 157.0 158.0 159.0 160.0 161.0 162.0 163.0 164.0 165.0 166.0 167.0 168.0 169.0 170.0 171.0 172.0 173.0 174.0 175.0 176.0 177.0 178.0 179.0 180.0 181.0 182.0 183.0 184.0 185.0 186.0 187.0 188.0 189.0 190.0 191.0

    192.0 193.0 194.0 195.0 196.0 197.0 198.0 199.0 200.0 201.0 202.0 203.0 204.0 205.0 206.0 207.0 208.0 209.0 210.0 211.0 212.0 213.0 214.0 215.0 216.0 217.0 218.0 219.0 220.0 221.0 222.0 223.0 224.0 225.0 226.0 227.0 228.0 229.0 230.0 231.0 232.0 233.0 234.0 235.0 236.0 237.0 238.0 239.0 240.0 241.0 242.0 243.0 244.0 245.0 246.0 247.0 248.0 249.0 250.0 251.0 252.0 253.0 254.0 255.0

    256.0 257.0 258.0 259.0 260.0 261.0 262.0 263.0 264.0 265.0 266.0 267.0 268.0 269.0 270.0 271.0 272.0 273.0 274.0 275.0 276.0 277.0 278.0 279.0 280.0 281.0 282.0 283.0 284.0 285.0 286.0 287.0 288.0 289.0 290.0 291.0 292.0 293.0 294.0 295.0 296.0 297.0 298.0 299.0 300.0 301.0 302.0 303.0 304.0 305.0 306.0 307.0 308.0 309.0 310.0 311.0 312.0 313.0 314.0 315.0 316.0 317.0 318.0 319.0

    320.0 321.0 322.0 323.0 324.0 325.0 326.0 327.0 328.0 329.0 330.0 331.0 332.0 333.0 334.0 335.0 336.0 337.0 338.0 339.0 340.0 341.0 342.0 343.0 344.0 345.0 346.0 347.0 348.0 349.0 350.0 351.0 352.0 353.0 354.0 355.0 356.0 357.0 358.0 359.0 360.0 361.0 362.0 363.0 364.0 365.0 366.0 367.0 368.0 369.0 370.0 371.0 372.0 373.0 374.0 375.0 376.0 377.0 378.0 379.0 380.0 381.0 382.0 383.0

    Ideal output data

    0.0 -1.0 -2.0 -3.0 -4.0 -5.0 -6.0 -7.0 -8.0 -9.0 -10.0 -11.0 -12.0 -13.0 -14.0 -15.0 -16.0 -17.0 -18.0 -19.0 -20.0 -21.0 -22.0 -23.0 -24.0 -25.0 -26.0 -27.0 -28.0 -29.0 -30.0 -31.0 -32.0 -33.0 -34.0 -35.0 -36.0 -37.0 -38.0 -39.0 -40.0 -41.0 -42.0 -43.0 -44.0 -45.0 -46.0 -47.0 -48.0 -49.0 -50.0 -51.0 -52.0 -53.0 -54.0 -55.0 -56.0 -57.0 -58.0 -59.0 -60.0 -61.0 -62.0 -63.0

    -64.0 -65.0 -66.0 -67.0 -68.0 -69.0 -70.0 -71.0 -72.0 -73.0 -74.0 -75.0 -76.0 -77.0 -78.0 -79.0 -80.0 -81.0 -82.0 -83.0 -84.0 -85.0 -86.0 -87.0 -88.0 -89.0 -90.0 -91.0 -92.0 -93.0 -94.0 -95.0 -96.0 -97.0 -98.0 -99.0 -100.0 -101.0 -102.0 -103.0 -104.0 -105.0 -106.0 -107.0 -108.0 -109.0 -110.0 -111.0 -112.0 -113.0 -114.0 -115.0 -116.0 -117.0 -118.0 -119.0 -120.0 -121.0 -122.0 -123.0 -124.0 -125.0 -126.0 -127.0

    -128.0 -129.0 -130.0 -131.0 -132.0 -133.0 -134.0 -135.0 -136.0 -137.0 -138.0 -139.0 -140.0 -141.0 -142.0 -143.0 -144.0 -145.0 -146.0 -147.0 -148.0 -149.0 -150.0 -151.0 -152.0 -153.0 -154.0 -155.0 -156.0 -157.0 -158.0 -159.0 -160.0 -161.0 -162.0 -163.0 -164.0 -165.0 -166.0 -167.0 -168.0 -169.0 -170.0 -171.0 -172.0 -173.0 -174.0 -175.0 -176.0 -177.0 -178.0 -179.0 -180.0 -181.0 -182.0 -183.0 -184.0 -185.0 -186.0 -187.0 -188.0 -189.0 -190.0 -191.0

    -192.0 -193.0 -194.0 -195.0 -196.0 -197.0 -198.0 -199.0 -200.0 -201.0 -202.0 -203.0 -204.0 -205.0 -206.0 -207.0 -208.0 -209.0 -210.0 -211.0 -212.0 -213.0 -214.0 -215.0 -216.0 -217.0 -218.0 -219.0 -220.0 -221.0 -222.0 -223.0 -224.0 -225.0 -226.0 -227.0 -228.0 -229.0 -230.0 -231.0 -232.0 -233.0 -234.0 -235.0 -236.0 -237.0 -238.0 -239.0 -240.0 -241.0 -242.0 -243.0 -244.0 -245.0 -246.0 -247.0 -248.0 -249.0 -250.0 -251.0 -252.0 -253.0 -254.0 -255.0

    -256.0 -257.0 -258.0 -259.0 -260.0 -261.0 -262.0 -263.0 -264.0 -265.0 -266.0 -267.0 -268.0 -269.0 -270.0 -271.0 -272.0 -273.0 -274.0 -275.0 -276.0 -277.0 -278.0 -279.0 -280.0 -281.0 -282.0 -283.0 -284.0 -285.0 -286.0 -287.0 -288.0 -289.0 -290.0 -291.0 -292.0 -293.0 -294.0 -295.0 -296.0 -297.0 -298.0 -299.0 -300.0 -301.0 -302.0 -303.0 -304.0 -305.0 -306.0 -307.0 -308.0 -309.0 -310.0 -311.0 -312.0 -313.0 -314.0 -315.0 -316.0 -317.0 -318.0 -319.0

    -320.0 -321.0 -322.0 -323.0 -324.0 -325.0 -326.0 -327.0 -328.0 -329.0 -330.0 -331.0 -332.0 -333.0 -334.0 -335.0 -336.0 -337.0 -338.0 -339.0 -340.0 -341.0 -342.0 -343.0 -344.0 -345.0 -346.0 -347.0 -348.0 -349.0 -350.0 -351.0 -352.0 -353.0 -354.0 -355.0 -356.0 -357.0 -358.0 -359.0 -360.0 -361.0 -362.0 -363.0 -364.0 -365.0 -366.0 -367.0 -368.0 -369.0 -370.0 -371.0 -372.0 -373.0 -374.0 -375.0 -376.0 -377.0 -378.0 -379.0 -380.0 -381.0 -382.0 -383.0

    Cubemap data of  *  * : Grid size is  x , each block has  x  threads.

Time: 0.098 msec, 249.50 Mtexlookups/sec

Comparing kernel output to expected data return 

    Actual output data

    -0.0 -1.0 -2.0 -3.0 -4.0 -5.0 -6.0 -7.0 -8.0 -9.0 -10.0 -11.0 -12.0 -13.0 -14.0 -15.0 -16.0 -17.0 -18.0 -19.0 -20.0 -21.0 -22.0 -23.0 -24.0 -25.0 -26.0 -27.0 -28.0 -29.0 -30.0 -31.0 -32.0 -33.0 -34.0 -35.0 -36.0 -37.0 -38.0 -39.0 -40.0 -41.0 -42.0 -43.0 -44.0 -45.0 -46.0 -47.0 -48.0 -49.0 -50.0 -51.0 -52.0 -53.0 -54.0 -55.0 -56.0 -57.0 -58.0 -59.0 -60.0 -61.0 -62.0 -63.0

    -64.0 -65.0 -66.0 -67.0 -68.0 -69.0 -70.0 -71.0 -72.0 -73.0 -74.0 -75.0 -76.0 -77.0 -78.0 -79.0 -80.0 -81.0 -82.0 -83.0 -84.0 -85.0 -86.0 -87.0 -88.0 -89.0 -90.0 -91.0 -92.0 -93.0 -94.0 -95.0 -96.0 -97.0 -98.0 -99.0 -100.0 -101.0 -102.0 -103.0 -104.0 -105.0 -106.0 -107.0 -108.0 -109.0 -110.0 -111.0 -112.0 -113.0 -114.0 -115.0 -116.0 -117.0 -118.0 -119.0 -120.0 -121.0 -122.0 -123.0 -124.0 -125.0 -126.0 -127.0

    -128.0 -129.0 -130.0 -131.0 -132.0 -133.0 -134.0 -135.0 -136.0 -137.0 -138.0 -139.0 -140.0 -141.0 -142.0 -143.0 -144.0 -145.0 -146.0 -147.0 -148.0 -149.0 -150.0 -151.0 -152.0 -153.0 -154.0 -155.0 -156.0 -157.0 -158.0 -159.0 -160.0 -161.0 -162.0 -163.0 -164.0 -165.0 -166.0 -167.0 -168.0 -169.0 -170.0 -171.0 -172.0 -173.0 -174.0 -175.0 -176.0 -177.0 -178.0 -179.0 -180.0 -181.0 -182.0 -183.0 -184.0 -185.0 -186.0 -187.0 -188.0 -189.0 -190.0 -191.0

    -192.0 -193.0 -194.0 -195.0 -196.0 -197.0 -198.0 -199.0 -200.0 -201.0 -202.0 -203.0 -204.0 -205.0 -206.0 -207.0 -208.0 -209.0 -210.0 -211.0 -212.0 -213.0 -214.0 -215.0 -216.0 -217.0 -218.0 -219.0 -220.0 -221.0 -222.0 -223.0 -224.0 -225.0 -226.0 -227.0 -228.0 -229.0 -230.0 -231.0 -232.0 -233.0 -234.0 -235.0 -236.0 -237.0 -238.0 -239.0 -240.0 -241.0 -242.0 -243.0 -244.0 -245.0 -246.0 -247.0 -248.0 -249.0 -250.0 -251.0 -252.0 -253.0 -254.0 -255.0

    -256.0 -257.0 -258.0 -259.0 -260.0 -261.0 -262.0 -263.0 -264.0 -265.0 -266.0 -267.0 -268.0 -269.0 -270.0 -271.0 -272.0 -273.0 -274.0 -275.0 -276.0 -277.0 -278.0 -279.0 -280.0 -281.0 -282.0 -283.0 -284.0 -285.0 -286.0 -287.0 -288.0 -289.0 -290.0 -291.0 -292.0 -293.0 -294.0 -295.0 -296.0 -297.0 -298.0 -299.0 -300.0 -301.0 -302.0 -303.0 -304.0 -305.0 -306.0 -307.0 -308.0 -309.0 -310.0 -311.0 -312.0 -313.0 -314.0 -315.0 -316.0 -317.0 -318.0 -319.0

    -320.0 -321.0 -322.0 -323.0 -324.0 -325.0 -326.0 -327.0 -328.0 -329.0 -330.0 -331.0 -332.0 -333.0 -334.0 -335.0 -336.0 -337.0 -338.0 -339.0 -340.0 -341.0 -342.0 -343.0 -344.0 -345.0 -346.0 -347.0 -348.0 -349.0 -350.0 -351.0 -352.0 -353.0 -354.0 -355.0 -356.0 -357.0 -358.0 -359.0 -360.0 -361.0 -362.0 -363.0 -364.0 -365.0 -366.0 -367.0 -368.0 -369.0 -370.0 -371.0 -372.0 -373.0 -374.0 -375.0 -376.0 -377.0 -378.0 -379.0 -380.0 -381.0 -382.0 -383.0

▶ 涨姿势

● helper_time.h 中新定义的计时函数

 // 关键步骤

 StopWatchInterface *timer = NULL;

 sdkCreateTimer(&timer);

 sdkStartTimer(&timer);

 sdkStopTimer(&timer);

 sdkGetTimerValue(&timer);

 sdkDeleteTimer(&timer);

 // helper_time.h

 class StopWatchInterface

 {

     public:

         StopWatchInterface() {};

         virtual ~StopWatchInterface() {};

     public:

         virtual void start() = ;

         virtual void stop() = ;

         virtual void reset() = ;

         virtual float getTime() = ;// 获取计时（计时器不停）

         virtual float getAverageTime() = ;

 };

 inline bool sdkCreateTimer(StopWatchInterface **timer_interface)

 {

 #if defined(WIN32) || defined(_WIN32) || defined(WIN64) || defined(_WIN64)

     *timer_interface = (StopWatchInterface *)new StopWatchWin();

 #else

     *timer_interface = (StopWatchInterface *)new StopWatchLinux();

 #endif

     return (*timer_interface != NULL) ? true : false;

 }

 inline bool sdkDeleteTimer(StopWatchInterface **timer_interface)

 {

     if (*timer_interface)

     {

         delete *timer_interface;

         *timer_interface = NULL;

     }

     return true;

 }

 inline bool sdkStartTimer(StopWatchInterface **timer_interface)

 {

     if (*timer_interface)

         (*timer_interface)->start();

     return true;

 }

 inline bool sdkStopTimer(StopWatchInterface **timer_interface)

 {

     if (*timer_interface)

         (*timer_interface)->stop();

     return true;

 }

 inline float sdkGetTimerValue(StopWatchInterface **timer_interface)

 {

     if (*timer_interface)

         return (*timer_interface)->getTime();

     else

         return 0.0f;

 }

● 立方体纹理贴图。六个面分别为 x = 1 正面、x = -1 轴负面、y = 1 正面、y = -1 负面、z = 1 正面、x = -1 负面，对应前、后、右、左、上、下。按照线性下标 [0, width * width * 6 - 1] 顺序访问时，各元素存储位置如下图所示（width == 2 为例）。

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