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）。

　　　　

0_Simple__simpleCubemapTexture的更多相关文章

随机推荐

  1. java中 static,final,transient,volatile关键字的作用

    static 和final static  静态修饰关键字,可以修饰 变量,程序块,类的方法: 当你定义一个static的变量的时候jvm会将将其分配在内存堆上,所有程序对它的引用都会指向这一个地址而 ...

  2. 大家一起做训练 第一场 E Number With The Given Amount Of Divisors

    题目来源:CodeForce #27 E 题目意思和题目标题一样,给一个n,求约数的个数恰好为n个的最小的数.保证答案在1018内. Orz,这题训练的时候没写出来. 这道题目分析一下,1018的不大 ...

  3. day 2 Linux Shell笔记

    ------------------------------------------------------------------- -------------------------------- ...

  4. Oracle集合类型

    Oracle集合类型介绍   集合类型   1. 使用条件:    a. 单行单列的数据,使用标量变量 .     b. 单行多列数据,使用记录    c. 单列多行数据,使用集合        *集 ...

  5. Kettle入门教程

    最近做的项目用到了ETL工具Kettle,这个工具相当好用,可以将各种类型数据作为数据流,经过处理后再生成各种类型的数据.正如其名“水壶”,将各个地方的水倒进水壶里,再用水壶倒入不同的容器.不过一来初 ...

  6. tomcat源码阅读之session管理器（Manager）

    一.UML图分析: (一) Session: Session保存了一个客户端访问服务器时,服务器专门为这个客户端建立一个session用来保存相关的会话信息,session有一个有效时间,这个时间默认 ...

  7. 【转】每天一个linux命令（8）：cp 命令

    原文网址:http://www.cnblogs.com/peida/archive/2012/10/29/2744185.html cp命令用来复制文件或者目录,是Linux系统中最常用的命令之一.一 ...

  8. java 多线程之 interrupt()和线程终止方式

    interrupt() 说明 interrupt()的作用是中断本线程. 本线程中断自己是被允许的:其它线程调用本线程的interrupt()方法时,会通过checkAccess()检查权限.这有可能 ...

  9. ML(4.1): R C4.5

    决策树模型中最为流行的是C4.5算法,  该类算法70年代末,J Ross Quinlan提出ID3算法,此算法的目的在于减少树的深度.但是忽略了叶子数目的研究.1993年,Quinlan本人以ID3 ...

  10. ORM练习项目-图书管理系统（BMS）实现细节

    分析 一本书 可以由多个作者编著 一本书只能由一个出版社出版 一个作者可以写多本书 每个作者有自己的简介 对应关系: Author-Book # 多对多 Publish-Book # 一对多 Auth ...