Reputation: 31
I got the following program which is pretty much the SDK Sample "Simple Layered Texture".
// includes, system
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <math.h>
// includes, kernels
#include <cuda_runtime.h>
// includes, project
#include <helper_cuda.h>
#include <helper_functions.h> // helper for shared that are common to CUDA SDK samples
#define EXIT_WAIVED 2
static char *sSDKname = "simpleLayeredTexture";
// includes, kernels
// declare texture reference for layered 2D float texture
// Note: The "dim" field in the texture reference template is now deprecated.
// Instead, please use a texture type macro such as cudaTextureType1D, etc.
typedef int TYPE;
texture<TYPE, cudaTextureType2DLayered> tex;
////////////////////////////////////////////////////////////////////////////////
//! Transform a layer of a layered 2D texture using texture lookups
//! @param g_odata output data in global memory
////////////////////////////////////////////////////////////////////////////////
__global__ void
transformKernel(TYPE *g_odata, int width, int height, int layer)
{
// 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;
float v = (y+0.5f) / (float) height;
// read from texture, do expected transformation and write to global memory
TYPE sample = tex2DLayered(tex, u, v, layer);
g_odata[layer*width*height + y*width + x] = sample;
printf("Sample %d\n", sample);
}
////////////////////////////////////////////////////////////////////////////////
// Program main
////////////////////////////////////////////////////////////////////////////////
int
main(int argc, char **argv)
{
printf("[%s] - Starting...\n", sSDKname);
// 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 to support Texture Arrays. Test will be waived... \n", sSDKname);
cudaDeviceReset();
exit(EXIT_SUCCESS);
}
// generate input data for layered texture
unsigned int width=16, height=16, num_layers = 5;
unsigned int size = width * height * num_layers * sizeof(TYPE);
TYPE *h_data = (TYPE *) malloc(size);
for (unsigned int layer = 0; layer < num_layers; layer++)
for (int i = 0; i < (int)(width * height); i++)
{
h_data[layer*width*height + i] = 15;//(float)i;
}
// this is the expected transformation of the input data (the expected output)
TYPE *h_data_ref = (TYPE *) malloc(size);
for (unsigned int layer = 0; layer < num_layers; layer++)
for (int i = 0; i < (int)(width * height); i++)
{
h_data_ref[layer*width*height + i] = h_data[layer*width*height + i];
}
// allocate device memory for result
TYPE *d_data = NULL;
checkCudaErrors(cudaMalloc((void **) &d_data, size));
// allocate array and copy image data
cudaChannelFormatDesc channelDesc = cudaCreateChannelDesc<TYPE>();
cudaArray *cu_3darray;
checkCudaErrors(cudaMalloc3DArray(&cu_3darray, &channelDesc, make_cudaExtent(width, height, num_layers), cudaArrayLayered));
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(TYPE), width, height);
myparms.dstArray = cu_3darray;
myparms.extent = make_cudaExtent(width, height, num_layers);
myparms.kind = cudaMemcpyHostToDevice;
checkCudaErrors(cudaMemcpy3D(&myparms));
// set texture parameters
tex.addressMode[0] = cudaAddressModeWrap;
tex.addressMode[1] = cudaAddressModeWrap;
// tex.filterMode = cudaFilterModeLinear;
tex.filterMode = cudaFilterModePoint;
tex.normalized = true; // access with normalized texture coordinates
// Bind the array to the texture
checkCudaErrors(cudaBindTextureToArray(tex, cu_3darray, channelDesc));
dim3 dimBlock(8, 8, 1);
dim3 dimGrid(width / dimBlock.x, height / dimBlock.y, 1);
printf("Covering 2D data array of %d x %d: Grid size is %d x %d, each block has 8 x 8 threads\n",
width, height, dimGrid.x, dimGrid.y);
transformKernel<<< dimGrid, dimBlock >>>(d_data, width, height, 0); // 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
for (unsigned int layer = 0; layer < num_layers; layer++)
transformKernel<<< dimGrid, dimBlock, 0 >>>(d_data, width, height, layer);
// 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", (width *height *num_layers / (sdkGetTimerValue(&timer) / 1000.0f) / 1e6));
sdkDeleteTimer(&timer);
// allocate mem for the result on host side
TYPE *h_odata = (TYPE *) malloc(size);
// copy result from device to host
checkCudaErrors(cudaMemcpy(h_odata, d_data, size, cudaMemcpyDeviceToHost));
printf("Comparing kernel output to expected data\n");
#define MIN_EPSILON_ERROR 5e-3f
bResult = compareData(h_odata, h_data_ref, width*height*num_layers, MIN_EPSILON_ERROR, 0.0f);
printf("Host sample: %d == %d\n", h_data_ref[0], h_odata[0]);
// cleanup memory
free(h_data);
free(h_data_ref);
free(h_odata);
checkCudaErrors(cudaFree(d_data));
checkCudaErrors(cudaFreeArray(cu_3darray));
cudaDeviceReset();
if (bResult)
printf("Success!");
else
printf("Failure!");
exit(bResult ? EXIT_SUCCESS : EXIT_FAILURE);
}
The output is correct if I use int (or uint) as TYPE. For float it produces wrong results i.e. always 0 (eventhough the SDK compareData function says everything is fine!?). I'm starting to believe that there is a bug in CUDA. I'm using version 5.0 on a Kepler K20.
Any suggestions and test results are appreciated. The code should be runnable as is.
Thanks in advance, Ben
Edit: OS is Linux (Ubuntu 12.04.2 LTS) x86_64 3.2.0-38-generic
Upvotes: 3
Views: 2087
Reputation: 151934
The problem here is that if you only change this:
typedef int TYPE;
to this:
typedef float TYPE;
then this line in the kernel is no longer correct:
printf("Sample %d\n", sample);
^^
because the printf
format specifier %d
is not correct for float
type. If you change that specifier to %f
, you get expected output:
$ cat t1519.cu
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <math.h>
// includes, kernels
#include <cuda_runtime.h>
// includes, project
#include <helper_cuda.h>
#include <helper_functions.h> // helper for shared that are common to CUDA SDK samples
#define EXIT_WAIVED 2
static char *sSDKname = "simpleLayeredTexture";
// includes, kernels
// declare texture reference for layered 2D float texture
// Note: The "dim" field in the texture reference template is now deprecated.
// Instead, please use a texture type macro such as cudaTextureType1D, etc.
typedef float TYPE;
texture<TYPE, cudaTextureType2DLayered> tex;
////////////////////////////////////////////////////////////////////////////////
//! Transform a layer of a layered 2D texture using texture lookups
//! @param g_odata output data in global memory
////////////////////////////////////////////////////////////////////////////////
__global__ void
transformKernel(TYPE *g_odata, int width, int height, int layer)
{
// 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;
float v = (y+0.5f) / (float) height;
// read from texture, do expected transformation and write to global memory
TYPE sample = tex2DLayered(tex, u, v, layer);
g_odata[layer*width*height + y*width + x] = sample;
printf("Sample %f\n", sample);
}
////////////////////////////////////////////////////////////////////////////////
// Program main
////////////////////////////////////////////////////////////////////////////////
int
main(int argc, char **argv)
{
printf("[%s] - Starting...\n", sSDKname);
// 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 to support Texture Arrays. Test will be waived... \n", sSDKname);
cudaDeviceReset();
exit(EXIT_SUCCESS);
}
// generate input data for layered texture
unsigned int width=16, height=16, num_layers = 5;
unsigned int size = width * height * num_layers * sizeof(TYPE);
TYPE *h_data = (TYPE *) malloc(size);
for (unsigned int layer = 0; layer < num_layers; layer++)
for (int i = 0; i < (int)(width * height); i++)
{
h_data[layer*width*height + i] = 15;//(float)i;
}
// this is the expected transformation of the input data (the expected output)
TYPE *h_data_ref = (TYPE *) malloc(size);
for (unsigned int layer = 0; layer < num_layers; layer++)
for (int i = 0; i < (int)(width * height); i++)
{
h_data_ref[layer*width*height + i] = h_data[layer*width*height + i];
}
// allocate device memory for result
TYPE *d_data = NULL;
checkCudaErrors(cudaMalloc((void **) &d_data, size));
// allocate array and copy image data
cudaChannelFormatDesc channelDesc = cudaCreateChannelDesc<TYPE>();
cudaArray *cu_3darray;
checkCudaErrors(cudaMalloc3DArray(&cu_3darray, &channelDesc, make_cudaExtent(width, height, num_layers), cudaArrayLayered));
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(TYPE), width, height);
myparms.dstArray = cu_3darray;
myparms.extent = make_cudaExtent(width, height, num_layers);
myparms.kind = cudaMemcpyHostToDevice;
checkCudaErrors(cudaMemcpy3D(&myparms));
// set texture parameters
tex.addressMode[0] = cudaAddressModeWrap;
tex.addressMode[1] = cudaAddressModeWrap;
// tex.filterMode = cudaFilterModeLinear;
tex.filterMode = cudaFilterModePoint;
tex.normalized = true; // access with normalized texture coordinates
// Bind the array to the texture
checkCudaErrors(cudaBindTextureToArray(tex, cu_3darray, channelDesc));
dim3 dimBlock(8, 8, 1);
dim3 dimGrid(width / dimBlock.x, height / dimBlock.y, 1);
printf("Covering 2D data array of %d x %d: Grid size is %d x %d, each block has 8 x 8 threads\n",
width, height, dimGrid.x, dimGrid.y);
transformKernel<<< dimGrid, dimBlock >>>(d_data, width, height, 0); // 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
for (unsigned int layer = 0; layer < num_layers; layer++)
transformKernel<<< dimGrid, dimBlock, 0 >>>(d_data, width, height, layer);
// 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", (width *height *num_layers / (sdkGetTimerValue(&timer) / 1000.0f) / 1e6));
sdkDeleteTimer(&timer);
// allocate mem for the result on host side
TYPE *h_odata = (TYPE *) malloc(size);
// copy result from device to host
checkCudaErrors(cudaMemcpy(h_odata, d_data, size, cudaMemcpyDeviceToHost));
printf("Comparing kernel output to expected data\n");
#define MIN_EPSILON_ERROR 5e-3f
bResult = compareData(h_odata, h_data_ref, width*height*num_layers, MIN_EPSILON_ERROR, 0.0f);
printf("Host sample: %d == %d\n", h_data_ref[0], h_odata[0]);
// cleanup memory
free(h_data);
free(h_data_ref);
free(h_odata);
checkCudaErrors(cudaFree(d_data));
checkCudaErrors(cudaFreeArray(cu_3darray));
cudaDeviceReset();
if (bResult)
printf("Success!");
else
printf("Failure!");
exit(bResult ? EXIT_SUCCESS : EXIT_FAILURE);
}
$ nvcc -I/usr/local/cuda/samples/common/inc t1519.cu -o t1519
t1519.cu(15): warning: conversion from a string literal to "char *" is deprecated
t1519.cu(15): warning: conversion from a string literal to "char *" is deprecated
[user2@dc10 misc]$ cuda-memcheck ./t1519
========= CUDA-MEMCHECK
[simpleLayeredTexture] - Starting...
GPU Device 0: "Tesla V100-PCIE-32GB" with compute capability 7.0
CUDA device [Tesla V100-PCIE-32GB] has 80 Multi-Processors SM 7.0
Covering 2D data array of 16 x 16: Grid size is 2 x 2, each block has 8 x 8 threads
Sample 15.000000
Sample 15.000000
Sample 15.000000
Sample 15.000000
Sample 15.000000
Sample 15.000000
Sample 15.000000
Sample 15.000000
Sample 15.000000
Sample 15.000000
Sample 15.000000
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Sample 15.000000
Sample 15.000000
...
Sample 15.000000
Sample 15.000000
Sample 15.000000
Processing time: 13.991 msec
0.09 Mtexlookups/sec
Comparing kernel output to expected data
Host sample: 8964432 == 1
Success!========= ERROR SUMMARY: 0 errors
$
note that the final output line is still incorrect, because I have not modified the incorrect printf
format specifiers there:
printf("Host sample: %d == %d\n", h_data_ref[0], h_odata[0]);
Upvotes: 1