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https://github.com/NVIDIA/cuda-samples.git
synced 2026-10-25 22:28:17 +08:00
Apply consistent code formatting across the repo. Add clang-format and pre-commit hooks.
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Rob Armstrong
@@ -26,27 +26,27 @@
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*/
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/*
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* This sample demonstrates how to use texture fetches from layered 2D textures
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* in CUDA C
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*
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* This sample first generates a 3D input data array for the layered texture
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* and the expected output. Then it starts CUDA C kernels, one for each layer,
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* which fetch their layer's texture data (using normalized texture coordinates)
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* transform it to the expected output, and write it to a 3D output data array.
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*/
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* This sample demonstrates how to use texture fetches from layered 2D textures
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* in CUDA C
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*
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* This sample first generates a 3D input data array for the layered texture
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* and the expected output. Then it starts CUDA C kernels, one for each layer,
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* which fetch their layer's texture data (using normalized texture coordinates)
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* transform it to the expected output, and write it to a 3D output data array.
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*/
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// includes, system
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#include <stdlib.h>
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#include <stdio.h>
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#include <string.h>
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#include <math.h>
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#include <stdio.h>
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#include <stdlib.h>
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#include <string.h>
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// includes, kernels
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#include <cuda_runtime.h>
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// includes, project
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#include <helper_cuda.h>
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#include <helper_functions.h> // helper for shared that are common to CUDA Samples
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#include <helper_functions.h> // helper for shared that are common to CUDA Samples
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static const char *sSDKname = "simpleLayeredTexture";
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@@ -54,163 +54,156 @@ static const char *sSDKname = "simpleLayeredTexture";
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//! Transform a layer of a layered 2D texture using texture lookups
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//! @param g_odata output data in global memory
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////////////////////////////////////////////////////////////////////////////////
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__global__ void transformKernel(float *g_odata, int width, int height,
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int layer, cudaTextureObject_t tex) {
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// calculate this thread's data point
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unsigned int x = blockIdx.x * blockDim.x + threadIdx.x;
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unsigned int y = blockIdx.y * blockDim.y + threadIdx.y;
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__global__ void transformKernel(float *g_odata, int width, int height, int layer, cudaTextureObject_t tex)
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{
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// calculate this thread's data point
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unsigned int x = blockIdx.x * blockDim.x + threadIdx.x;
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unsigned int y = blockIdx.y * blockDim.y + threadIdx.y;
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// 0.5f offset and division are necessary to access the original data points
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// in the texture (such that bilinear interpolation will not be activated).
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// For details, see also CUDA Programming Guide, Appendix D
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float u = (x + 0.5f) / (float)width;
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float v = (y + 0.5f) / (float)height;
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// 0.5f offset and division are necessary to access the original data points
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// in the texture (such that bilinear interpolation will not be activated).
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// For details, see also CUDA Programming Guide, Appendix D
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float u = (x + 0.5f) / (float)width;
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float v = (y + 0.5f) / (float)height;
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// read from texture, do expected transformation and write to global memory
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g_odata[layer * width * height + y * width + x] =
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-tex2DLayered<float>(tex, u, v, layer) + layer;
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// read from texture, do expected transformation and write to global memory
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g_odata[layer * width * height + y * width + x] = -tex2DLayered<float>(tex, u, v, layer) + layer;
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}
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////////////////////////////////////////////////////////////////////////////////
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// Program main
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////////////////////////////////////////////////////////////////////////////////
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int main(int argc, char **argv) {
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printf("[%s] - Starting...\n", sSDKname);
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int main(int argc, char **argv)
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{
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printf("[%s] - Starting...\n", sSDKname);
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// use command-line specified CUDA device, otherwise use device with highest
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// Gflops/s
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int devID = findCudaDevice(argc, (const char **)argv);
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// use command-line specified CUDA device, otherwise use device with highest
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// Gflops/s
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int devID = findCudaDevice(argc, (const char **)argv);
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bool bResult = true;
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bool bResult = true;
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// get number of SMs on this GPU
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cudaDeviceProp deviceProps;
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// get number of SMs on this GPU
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cudaDeviceProp deviceProps;
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checkCudaErrors(cudaGetDeviceProperties(&deviceProps, devID));
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printf("CUDA device [%s] has %d Multi-Processors ", deviceProps.name,
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deviceProps.multiProcessorCount);
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printf("SM %d.%d\n", deviceProps.major, deviceProps.minor);
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checkCudaErrors(cudaGetDeviceProperties(&deviceProps, devID));
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printf("CUDA device [%s] has %d Multi-Processors ", deviceProps.name, deviceProps.multiProcessorCount);
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printf("SM %d.%d\n", deviceProps.major, deviceProps.minor);
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// generate input data for layered texture
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unsigned int width = 512, height = 512, num_layers = 5;
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unsigned int size = width * height * num_layers * sizeof(float);
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float *h_data = (float *)malloc(size);
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// generate input data for layered texture
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unsigned int width = 512, height = 512, num_layers = 5;
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unsigned int size = width * height * num_layers * sizeof(float);
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float *h_data = (float *)malloc(size);
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for (unsigned int layer = 0; layer < num_layers; layer++)
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for (int i = 0; i < (int)(width * height); i++) {
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h_data[layer * width * height + i] = (float)i;
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for (unsigned int layer = 0; layer < num_layers; layer++)
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for (int i = 0; i < (int)(width * height); i++) {
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h_data[layer * width * height + i] = (float)i;
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}
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// this is the expected transformation of the input data (the expected output)
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float *h_data_ref = (float *)malloc(size);
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for (unsigned int layer = 0; layer < num_layers; layer++)
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for (int i = 0; i < (int)(width * height); i++) {
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h_data_ref[layer * width * height + i] = -h_data[layer * width * height + i] + layer;
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}
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// allocate device memory for result
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float *d_data = NULL;
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checkCudaErrors(cudaMalloc((void **)&d_data, size));
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// allocate array and copy image data
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cudaChannelFormatDesc channelDesc = cudaCreateChannelDesc(32, 0, 0, 0, cudaChannelFormatKindFloat);
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cudaArray *cu_3darray;
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checkCudaErrors(
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cudaMalloc3DArray(&cu_3darray, &channelDesc, make_cudaExtent(width, height, num_layers), cudaArrayLayered));
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cudaMemcpy3DParms myparms = {0};
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myparms.srcPos = make_cudaPos(0, 0, 0);
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myparms.dstPos = make_cudaPos(0, 0, 0);
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myparms.srcPtr = make_cudaPitchedPtr(h_data, width * sizeof(float), width, height);
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myparms.dstArray = cu_3darray;
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myparms.extent = make_cudaExtent(width, height, num_layers);
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myparms.kind = cudaMemcpyHostToDevice;
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checkCudaErrors(cudaMemcpy3D(&myparms));
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cudaTextureObject_t tex;
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cudaResourceDesc texRes;
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memset(&texRes, 0, sizeof(cudaResourceDesc));
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texRes.resType = cudaResourceTypeArray;
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texRes.res.array.array = cu_3darray;
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cudaTextureDesc texDescr;
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memset(&texDescr, 0, sizeof(cudaTextureDesc));
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texDescr.normalizedCoords = true;
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texDescr.filterMode = cudaFilterModeLinear;
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texDescr.addressMode[0] = cudaAddressModeWrap;
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texDescr.addressMode[1] = cudaAddressModeWrap;
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texDescr.readMode = cudaReadModeElementType;
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checkCudaErrors(cudaCreateTextureObject(&tex, &texRes, &texDescr, NULL));
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dim3 dimBlock(8, 8, 1);
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dim3 dimGrid(width / dimBlock.x, height / dimBlock.y, 1);
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printf("Covering 2D data array of %d x %d: Grid size is %d x %d, each block has "
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"8 x 8 threads\n",
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width,
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height,
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dimGrid.x,
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dimGrid.y);
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transformKernel<<<dimGrid, dimBlock>>>(d_data, width, height, 0,
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tex); // warmup (for better timing)
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// check if kernel execution generated an error
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getLastCudaError("warmup Kernel execution failed");
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checkCudaErrors(cudaDeviceSynchronize());
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StopWatchInterface *timer = NULL;
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sdkCreateTimer(&timer);
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sdkStartTimer(&timer);
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// execute the kernel
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for (unsigned int layer = 0; layer < num_layers; layer++)
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transformKernel<<<dimGrid, dimBlock, 0>>>(d_data, width, height, layer, tex);
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// check if kernel execution generated an error
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getLastCudaError("Kernel execution failed");
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checkCudaErrors(cudaDeviceSynchronize());
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sdkStopTimer(&timer);
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printf("Processing time: %.3f msec\n", sdkGetTimerValue(&timer));
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printf("%.2f Mtexlookups/sec\n", (width * height * num_layers / (sdkGetTimerValue(&timer) / 1000.0f) / 1e6));
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sdkDeleteTimer(&timer);
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// allocate mem for the result on host side
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float *h_odata = (float *)malloc(size);
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// copy result from device to host
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checkCudaErrors(cudaMemcpy(h_odata, d_data, size, cudaMemcpyDeviceToHost));
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// write regression file if necessary
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if (checkCmdLineFlag(argc, (const char **)argv, "regression")) {
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// write file for regression test
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sdkWriteFile<float>("./data/regression.dat", h_odata, width * height, 0.0f, false);
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}
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// this is the expected transformation of the input data (the expected output)
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float *h_data_ref = (float *)malloc(size);
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for (unsigned int layer = 0; layer < num_layers; layer++)
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for (int i = 0; i < (int)(width * height); i++) {
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h_data_ref[layer * width * height + i] =
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-h_data[layer * width * height + i] + layer;
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}
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// allocate device memory for result
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float *d_data = NULL;
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checkCudaErrors(cudaMalloc((void **)&d_data, size));
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// allocate array and copy image data
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cudaChannelFormatDesc channelDesc =
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cudaCreateChannelDesc(32, 0, 0, 0, cudaChannelFormatKindFloat);
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cudaArray *cu_3darray;
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checkCudaErrors(cudaMalloc3DArray(&cu_3darray, &channelDesc,
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make_cudaExtent(width, height, num_layers),
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cudaArrayLayered));
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cudaMemcpy3DParms myparms = {0};
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myparms.srcPos = make_cudaPos(0, 0, 0);
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myparms.dstPos = make_cudaPos(0, 0, 0);
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myparms.srcPtr =
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make_cudaPitchedPtr(h_data, width * sizeof(float), width, height);
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myparms.dstArray = cu_3darray;
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myparms.extent = make_cudaExtent(width, height, num_layers);
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myparms.kind = cudaMemcpyHostToDevice;
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checkCudaErrors(cudaMemcpy3D(&myparms));
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cudaTextureObject_t tex;
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cudaResourceDesc texRes;
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memset(&texRes, 0, sizeof(cudaResourceDesc));
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texRes.resType = cudaResourceTypeArray;
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texRes.res.array.array = cu_3darray;
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cudaTextureDesc texDescr;
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memset(&texDescr, 0, sizeof(cudaTextureDesc));
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texDescr.normalizedCoords = true;
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texDescr.filterMode = cudaFilterModeLinear;
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texDescr.addressMode[0] = cudaAddressModeWrap;
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texDescr.addressMode[1] = cudaAddressModeWrap;
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texDescr.readMode = cudaReadModeElementType;
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checkCudaErrors(cudaCreateTextureObject(&tex, &texRes, &texDescr, NULL));
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dim3 dimBlock(8, 8, 1);
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dim3 dimGrid(width / dimBlock.x, height / dimBlock.y, 1);
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printf(
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"Covering 2D data array of %d x %d: Grid size is %d x %d, each block has "
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"8 x 8 threads\n",
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width, height, dimGrid.x, dimGrid.y);
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transformKernel<<<dimGrid, dimBlock>>>(d_data, width, height, 0,
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tex); // warmup (for better timing)
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// check if kernel execution generated an error
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getLastCudaError("warmup Kernel execution failed");
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checkCudaErrors(cudaDeviceSynchronize());
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StopWatchInterface *timer = NULL;
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sdkCreateTimer(&timer);
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sdkStartTimer(&timer);
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// execute the kernel
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for (unsigned int layer = 0; layer < num_layers; layer++)
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transformKernel<<<dimGrid, dimBlock, 0>>>(d_data, width, height, layer,
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tex);
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// check if kernel execution generated an error
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getLastCudaError("Kernel execution failed");
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checkCudaErrors(cudaDeviceSynchronize());
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sdkStopTimer(&timer);
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printf("Processing time: %.3f msec\n", sdkGetTimerValue(&timer));
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printf("%.2f Mtexlookups/sec\n",
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(width * height * num_layers / (sdkGetTimerValue(&timer) / 1000.0f) /
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1e6));
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sdkDeleteTimer(&timer);
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// allocate mem for the result on host side
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float *h_odata = (float *)malloc(size);
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// copy result from device to host
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checkCudaErrors(cudaMemcpy(h_odata, d_data, size, cudaMemcpyDeviceToHost));
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// write regression file if necessary
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if (checkCmdLineFlag(argc, (const char **)argv, "regression")) {
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// write file for regression test
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sdkWriteFile<float>("./data/regression.dat", h_odata, width * height, 0.0f,
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false);
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} else {
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printf("Comparing kernel output to expected data\n");
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else {
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printf("Comparing kernel output to expected data\n");
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#define MIN_EPSILON_ERROR 5e-3f
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bResult = compareData(h_odata, h_data_ref, width * height * num_layers,
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MIN_EPSILON_ERROR, 0.0f);
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}
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bResult = compareData(h_odata, h_data_ref, width * height * num_layers, MIN_EPSILON_ERROR, 0.0f);
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}
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// cleanup memory
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free(h_data);
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free(h_data_ref);
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free(h_odata);
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// cleanup memory
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free(h_data);
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free(h_data_ref);
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free(h_odata);
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checkCudaErrors(cudaDestroyTextureObject(tex));
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checkCudaErrors(cudaFree(d_data));
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checkCudaErrors(cudaFreeArray(cu_3darray));
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checkCudaErrors(cudaDestroyTextureObject(tex));
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checkCudaErrors(cudaFree(d_data));
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checkCudaErrors(cudaFreeArray(cu_3darray));
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exit(bResult ? EXIT_SUCCESS : EXIT_FAILURE);
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exit(bResult ? EXIT_SUCCESS : EXIT_FAILURE);
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}
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