Hi,
I just started programming with CUDA and I’m totally new with the environment.
as i execute the following code (adding to vectors), there is a problem: “Segmentation fault (core dumped)”
how can I fix the problem?
thanks
Hi!
remove “&” sign in cudaMemcpy calls.
I removed them, but that wasn’t solved…
Allocate h_A and h_B with
h_A = malloc(size);
h_B = malloc(size);
Properly #including <stdlib.h> would have caught this. Along the same lines, you should provide prototypes for functions in your code. If nothing else helps, a debugger will point out which source code line causes the segmentation fault.
I have similar problem while running cuda sdk 6.5 vector add program.
- Vector addition: C = A + B.
- This sample is a very basic sample that implements element by element
- vector addition. It is the same as the sample illustrating Chapter 2
- of the programming guide with some additions like error checking.
*/
#include <stdio.h>
// For the CUDA runtime routines (prefixed with “cuda_”)
#include <cuda_runtime.h>
/**
-
CUDA Kernel Device code
-
Computes the vector addition of A and B into C. The 3 vectors have the same
-
number of elements numElements.
*/
global void
vectorAdd(const float *A, const float *B, float *C, int numElements)
{
int i = blockDim.x * blockIdx.x + threadIdx.x;if (i < numElements)
{
C[i] = A[i] + B[i];
}
}
/**
-
Host main routine
*/
int
main(void)
{
// Error code to check return values for CUDA calls
cudaError_t err = cudaSuccess;// Print the vector length to be used, and compute its size
int numElements = 50000;
size_t size = numElements * sizeof(float);
printf(“[Vector addition of %d elements]\n”, numElements);// Allocate the host input vector A
float *h_A = (float *)malloc(size);// Allocate the host input vector B
float *h_B = (float *)malloc(size);// Allocate the host output vector C
float *h_C = (float *)malloc(size);// Verify that allocations succeeded
if (h_A == NULL || h_B == NULL || h_C == NULL)
{
fprintf(stderr, “Failed to allocate host vectors!\n”);
exit(EXIT_FAILURE);
}// Initialize the host input vectors
for (int i = 0; i < numElements; ++i)
{
h_A[i] = rand()/(float)RAND_MAX;
h_B[i] = rand()/(float)RAND_MAX;
}// Allocate the device input vector A
float *d_A = NULL;
err = cudaMalloc((void **)&d_A, size);if (err != cudaSuccess)
{
fprintf(stderr, “Failed to allocate device vector A (error code %s)!\n”, cudaGetErrorString(err));
exit(EXIT_FAILURE);
}// Allocate the device input vector B
float *d_B = NULL;
err = cudaMalloc((void **)&d_B, size);if (err != cudaSuccess)
{
fprintf(stderr, “Failed to allocate device vector B (error code %s)!\n”, cudaGetErrorString(err));
exit(EXIT_FAILURE);
}// Allocate the device output vector C
float *d_C = NULL;
err = cudaMalloc((void **)&d_C, size);if (err != cudaSuccess)
{
fprintf(stderr, “Failed to allocate device vector C (error code %s)!\n”, cudaGetErrorString(err));
exit(EXIT_FAILURE);
}// Copy the host input vectors A and B in host memory to the device input vectors in
// device memory
printf(“Copy input data from the host memory to the CUDA device\n”);
err = cudaMemcpy(d_A, h_A, size, cudaMemcpyHostToDevice);if (err != cudaSuccess)
{
fprintf(stderr, “Failed to copy vector A from host to device (error code %s)!\n”, cudaGetErrorString(err));
exit(EXIT_FAILURE);
}err = cudaMemcpy(d_B, h_B, size, cudaMemcpyHostToDevice);
if (err != cudaSuccess)
{
fprintf(stderr, “Failed to copy vector B from host to device (error code %s)!\n”, cudaGetErrorString(err));
exit(EXIT_FAILURE);
}// Launch the Vector Add CUDA Kernel
int threadsPerBlock = 256;
int blocksPerGrid =(numElements + threadsPerBlock - 1) / threadsPerBlock;
printf(“CUDA kernel launch with %d blocks of %d threads\n”, blocksPerGrid, threadsPerBlock);
vectorAdd<<<blocksPerGrid, threadsPerBlock>>>(d_A, d_B, d_C, numElements);
err = cudaGetLastError();if (err != cudaSuccess)
{
fprintf(stderr, “Failed to launch vectorAdd kernel (error code %s)!\n”, cudaGetErrorString(err));
exit(EXIT_FAILURE);
}// Copy the device result vector in device memory to the host result vector
// in host memory.
printf(“Copy output data from the CUDA device to the host memory\n”);
err = cudaMemcpy(h_C, d_C, size, cudaMemcpyDeviceToHost);if (err != cudaSuccess)
{
fprintf(stderr, “Failed to copy vector C from device to host (error code %s)!\n”, cudaGetErrorString(err));
exit(EXIT_FAILURE);
}// Verify that the result vector is correct
for (int i = 0; i < numElements; ++i)
{
if (fabs(h_A[i] + h_B[i] - h_C[i]) > 1e-5)
{
fprintf(stderr, “Result verification failed at element %d!\n”, i);
exit(EXIT_FAILURE);
}
}printf(“Test PASSED\n”);
// Free device global memory
err = cudaFree(d_A);if (err != cudaSuccess)
{
fprintf(stderr, “Failed to free device vector A (error code %s)!\n”, cudaGetErrorString(err));
exit(EXIT_FAILURE);
}err = cudaFree(d_B);
if (err != cudaSuccess)
{
fprintf(stderr, “Failed to free device vector B (error code %s)!\n”, cudaGetErrorString(err));
exit(EXIT_FAILURE);
}err = cudaFree(d_C);
if (err != cudaSuccess)
{
fprintf(stderr, “Failed to free device vector C (error code %s)!\n”, cudaGetErrorString(err));
exit(EXIT_FAILURE);
}// Free host memory
free(h_A);
free(h_B);
free(h_C);// Reset the device and exit
// cudaDeviceReset causes the driver to clean up all state. While
// not mandatory in normal operation, it is good practice. It is also
// needed to ensure correct operation when the application is being
// profiled. Calling cudaDeviceReset causes all profile data to be
// flushed before the application exits
err = cudaDeviceReset();if (err != cudaSuccess)
{
fprintf(stderr, “Failed to deinitialize the device! error=%s\n”, cudaGetErrorString(err));
exit(EXIT_FAILURE);
}printf(“Done\n”);
return 0;
}
ERROR: Segmentation fault (core dumped)