CUDA分段错误的简单教程示例

Question

I am trying to run the example add.cu (see below) from this official nvidia tutorial using nvcc add.cu -o add_cuda; ./add_cuda nvcc add.cu -o add_cuda; ./add_cuda and get Segmentation fault (core dumped) .

I installed the nvidia cuda toolkit on Ubuntu 18 using sudo apt install nvidia-cuda-toolkit . I have a NVIDIA GF100GL Quadro 5000 and am using NVIDIA driver metapackage from nvidia-driver-390 (proprietary, tested)

I have little C++ experience, but the pure C++ code from the beginning of the tutorial compiled and ran correctly.

Following a comment, I added a check for the return of cudaMallocManaged and got operation not supported .

#include <iostream>
#include <math.h>
// Kernel function to add the elements of two arrays
__global__
void add(int n, float *x, float *y)
{
  for (int i = 0; i < n; i++)
    y[i] = x[i] + y[i];
}

int main(void)
{
  int N = 1<<20;
  float *x, *y;

  // Allocate Unified Memory – accessible from CPU or GPU
  cudaMallocManaged(&x, N*sizeof(float));
  cudaMallocManaged(&y, N*sizeof(float));

  // initialize x and y arrays on the host
  for (int i = 0; i < N; i++) {
    x[i] = 1.0f;
    y[i] = 2.0f;
  }

  // Run kernel on 1M elements on the GPU
  add<<<1, 1>>>(N, x, y);

  // Wait for GPU to finish before accessing on host
  cudaDeviceSynchronize();

  // Check for errors (all values should be 3.0f)
  float maxError = 0.0f;
  for (int i = 0; i < N; i++)
    maxError = fmax(maxError, fabs(y[i]-3.0f));
  std::cout << "Max error: " << maxError << std::endl;

  // Free memory
  cudaFree(x);
  cudaFree(y);

  return 0;
}

Answer 1

Your card belongs to fermi family with compute capability version 2.0. It does not support the Unified Memory as stated here:

K.1.1. System Requirements

Unified Memory has two basic requirements:

a GPU with SM architecture 3.0 or higher (Kepler class or newer)

a 64-bit host application and non-embedded operating system (Linux, Windows, macOS)