cuda-samples/Samples/0_Introduction/simpleCubemapTexture/simpleCubemapTexture.cu
2022-01-13 11:35:24 +05:30

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/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
*
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*
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/*
* This sample demonstrates how to use texture fetches from layered 2D textures
* in CUDA C
*
* This sample first generates a 3D input data array for the layered texture
* and the expected output. Then it starts CUDA C kernels, one for each layer,
* which fetch their layer's texture data (using normalized texture coordinates)
* transform it to the expected output, and write it to a 3D output data array.
*/
// includes, system
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <math.h>
// includes CUDA
#include <cuda_runtime.h>
// helper functions and utilities to work with CUDA
#include <helper_functions.h>
#include <helper_cuda.h>
static const char *sSDKname = "simpleCubemapTexture";
// includes, kernels
////////////////////////////////////////////////////////////////////////////////
//! Transform a cubemap face of a linear buffe using cubemap texture lookups
//! @param g_odata output data in global memory
////////////////////////////////////////////////////////////////////////////////
__global__ void transformKernel(float *g_odata, int width,
cudaTextureObject_t tex) {
// calculate this thread's data point
unsigned int x = blockIdx.x * blockDim.x + threadIdx.x;
unsigned int y = blockIdx.y * blockDim.y + threadIdx.y;
// 0.5f offset and division are necessary to access the original data points
// in the texture (such that bilinear interpolation will not be activated).
// For details, see also CUDA Programming Guide, Appendix D
float u = ((x + 0.5f) / (float)width) * 2.f - 1.f;
float v = ((y + 0.5f) / (float)width) * 2.f - 1.f;
float cx, cy, cz;
for (unsigned int face = 0; face < 6; face++) {
// Layer 0 is positive X face
if (face == 0) {
cx = 1;
cy = -v;
cz = -u;
}
// Layer 1 is negative X face
else if (face == 1) {
cx = -1;
cy = -v;
cz = u;
}
// Layer 2 is positive Y face
else if (face == 2) {
cx = u;
cy = 1;
cz = v;
}
// Layer 3 is negative Y face
else if (face == 3) {
cx = u;
cy = -1;
cz = -v;
}
// Layer 4 is positive Z face
else if (face == 4) {
cx = u;
cy = -v;
cz = 1;
}
// Layer 4 is negative Z face
else if (face == 5) {
cx = -u;
cy = -v;
cz = -1;
}
// read from texture, do expected transformation and write to global memory
g_odata[face * width * width + y * width + x] =
-texCubemap<float>(tex, cx, cy, cz);
}
}
////////////////////////////////////////////////////////////////////////////////
// Program main
////////////////////////////////////////////////////////////////////////////////
int main(int argc, char **argv) {
// use command-line specified CUDA device, otherwise use device with highest
// Gflops/s
int devID = findCudaDevice(argc, (const char **)argv);
bool bResult = true;
// get number of SMs on this GPU
cudaDeviceProp deviceProps;
checkCudaErrors(cudaGetDeviceProperties(&deviceProps, devID));
printf("CUDA device [%s] has %d Multi-Processors ", deviceProps.name,
deviceProps.multiProcessorCount);
printf("SM %d.%d\n", deviceProps.major, deviceProps.minor);
if (deviceProps.major < 2) {
printf(
"%s requires SM 2.0 or higher for support of Texture Arrays. Test "
"will exit... \n",
sSDKname);
exit(EXIT_WAIVED);
}
// generate input data for layered texture
unsigned int width = 64, num_faces = 6, num_layers = 1;
unsigned int cubemap_size = width * width * num_faces;
unsigned int size = cubemap_size * num_layers * sizeof(float);
float *h_data = (float *)malloc(size);
for (int i = 0; i < (int)(cubemap_size * num_layers); i++) {
h_data[i] = (float)i;
}
// this is the expected transformation of the input data (the expected output)
float *h_data_ref = (float *)malloc(size);
for (unsigned int layer = 0; layer < num_layers; layer++) {
for (int i = 0; i < (int)(cubemap_size); i++) {
h_data_ref[layer * cubemap_size + i] =
-h_data[layer * cubemap_size + i] + layer;
}
}
// allocate device memory for result
float *d_data = NULL;
checkCudaErrors(cudaMalloc((void **)&d_data, size));
// allocate array and copy image data
cudaChannelFormatDesc channelDesc =
cudaCreateChannelDesc(32, 0, 0, 0, cudaChannelFormatKindFloat);
cudaArray *cu_3darray;
// checkCudaErrors(cudaMalloc3DArray( &cu_3darray, &channelDesc,
// make_cudaExtent(width, height, num_layers), cudaArrayLayered ));
checkCudaErrors(cudaMalloc3DArray(&cu_3darray, &channelDesc,
make_cudaExtent(width, width, num_faces),
cudaArrayCubemap));
cudaMemcpy3DParms myparms = {0};
myparms.srcPos = make_cudaPos(0, 0, 0);
myparms.dstPos = make_cudaPos(0, 0, 0);
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;
checkCudaErrors(cudaMemcpy3D(&myparms));
cudaTextureObject_t tex;
cudaResourceDesc texRes;
memset(&texRes, 0, sizeof(cudaResourceDesc));
texRes.resType = cudaResourceTypeArray;
texRes.res.array.array = cu_3darray;
cudaTextureDesc texDescr;
memset(&texDescr, 0, sizeof(cudaTextureDesc));
texDescr.normalizedCoords = true;
texDescr.filterMode = cudaFilterModeLinear;
texDescr.addressMode[0] = cudaAddressModeWrap;
texDescr.addressMode[1] = cudaAddressModeWrap;
texDescr.addressMode[2] = cudaAddressModeWrap;
texDescr.readMode = cudaReadModeElementType;
checkCudaErrors(cudaCreateTextureObject(&tex, &texRes, &texDescr, NULL));
dim3 dimBlock(8, 8, 1);
dim3 dimGrid(width / dimBlock.x, width / dimBlock.y, 1);
printf(
"Covering Cubemap data array of %d~3 x %d: Grid size is %d x %d, each "
"block has 8 x 8 threads\n",
width, num_layers, dimGrid.x, dimGrid.y);
transformKernel<<<dimGrid, dimBlock>>>(d_data, width,
tex); // warmup (for better timing)
// check if kernel execution generated an error
getLastCudaError("warmup Kernel execution failed");
checkCudaErrors(cudaDeviceSynchronize());
StopWatchInterface *timer = NULL;
sdkCreateTimer(&timer);
sdkStartTimer(&timer);
// execute the kernel
transformKernel<<<dimGrid, dimBlock, 0>>>(d_data, width, tex);
// check if kernel execution generated an error
getLastCudaError("Kernel execution failed");
checkCudaErrors(cudaDeviceSynchronize());
sdkStopTimer(&timer);
printf("Processing time: %.3f msec\n", sdkGetTimerValue(&timer));
printf("%.2f Mtexlookups/sec\n",
(cubemap_size / (sdkGetTimerValue(&timer) / 1000.0f) / 1e6));
sdkDeleteTimer(&timer);
// allocate mem for the result on host side
float *h_odata = (float *)malloc(size);
// copy result from device to host
checkCudaErrors(cudaMemcpy(h_odata, d_data, size, cudaMemcpyDeviceToHost));
// write regression file if necessary
if (checkCmdLineFlag(argc, (const char **)argv, "regression")) {
// write file for regression test
sdkWriteFile<float>("./data/regression.dat", h_odata, width * width, 0.0f,
false);
} else {
printf("Comparing kernel output to expected data\n");
#define MIN_EPSILON_ERROR 5e-3f
bResult =
compareData(h_odata, h_data_ref, cubemap_size, MIN_EPSILON_ERROR, 0.0f);
}
// cleanup memory
free(h_data);
free(h_data_ref);
free(h_odata);
checkCudaErrors(cudaDestroyTextureObject(tex));
checkCudaErrors(cudaFree(d_data));
checkCudaErrors(cudaFreeArray(cu_3darray));
exit(bResult ? EXIT_SUCCESS : EXIT_FAILURE);
}