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我正在研究入侵物种的传播，并尝试使用 XORWOW 随机数生成器在 PyCUDA 内核中生成随机数。我需要能够在研究中用作输入的矩阵非常大（高达 8,000 x 8,000）。

get_random_number索引curandState*XORWOW 生成器时，错误似乎发生在内部。该代码在较小的矩阵上执行没有错误并产生正确的结果。我在 2 个 NVidia Tesla K20X GPU 上运行我的代码。

内核代码和设置：

kernel_code = '''
    #include <curand_kernel.h>
    #include <math.h>

    extern "C" {

    __device__ float get_random_number(curandState* global_state, int thread_id) {

        curandState local_state = global_state[thread_id];
        float num = curand_uniform(&local_state);
        global_state[thread_id] = local_state;
        return num;
    }

    __global__ void survival_of_the_fittest(float* grid_a, float* grid_b, curandState* global_state, int grid_size, float* survival_probabilities) {

        int x = threadIdx.x + blockIdx.x * blockDim.x;             // column index of cell
        int y = threadIdx.y + blockIdx.y * blockDim.y;             // row index of cell

        // make sure this cell is within bounds of grid
        if (x < grid_size && y < grid_size) {

            int thread_id = y * grid_size + x;                      // thread index
            grid_b[thread_id] = grid_a[thread_id];                  // copy current cell
            float num;

            // ignore cell if it is not already populated
            if (grid_a[thread_id] > 0.0) {

                num = get_random_number(global_state, thread_id);

                // agents in this cell die
                if (num < survival_probabilities[thread_id]) {
                    grid_b[thread_id] = 0.0;                        // cell dies
                    //printf("Cell (%d,%d) died (probability of death was %f)\\n", x, y, survival_probabilities[thread_id]);
                }
            }
        }
    }

mod = SourceModule(kernel_code, no_extern_c = True)
survival = mod.get_function('survival_of_the_fittest')

数据设置：

matrix_size = 2000
block_dims = 32
grid_dims = (matrix_size + block_dims - 1) // block_dims

grid_a = gpuarray.to_gpu(np.ones((matrix_size,matrix_size)).astype(np.float32))
grid_b = gpuarray.to_gpu(np.zeros((matrix_size,matrix_size)).astype(np.float32))
generator = curandom.XORWOWRandomNumberGenerator()
grid_size = np.int32(matrix_size)
survival_probabilities = gpuarray.to_gpu(np.random.uniform(0,1,(matrix_size,matrix_size)))

内核调用：

survival(grid_a, grid_b, generator.state, grid_size, survival_probabilities, 
    grid = (grid_dims, grid_dims), block = (block_dims, block_dims, 1))

我希望能够为最大 (8,000 x 8,000) 的矩阵生成范围 (0,1] 内的随机数，但是在大型矩阵上执行我的代码会导致非法内存访问错误。

pycuda._driver.LogicError: cuMemcpyDtoH failed: an illegal memory access was encountered
PyCUDA WARNING: a clean-up operation failed (dead context maybe?)
cuMemFree failed: an illegal memory access was encountered

我索引curandState*不正确get_random_number吗？如果不是，还有什么可能导致此错误？

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1 回答 1

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这里的问题是此代码之间的脱节，该代码确定 PyCUDAcurandom接口为其内部状态分配的状态大小与您帖子中的此代码：

matrix_size = 2000
block_dims = 32
grid_dims = (matrix_size + block_dims - 1) // block_dims

您似乎假设 PyCUDA 会神奇地为您在代码中选择的任何块和网格维度分配足够的状态。这显然不太可能，尤其是在大网格尺寸下。你要么需要

  • curandom修改您的代码以使用与模块内部用于您选择使用的任何生成器相同的块和网格大小，或者
  • 分配和管理您自己的状态暂存空间，以便您有足够的状态分配来服务您选择的块和网格大小

我将它作为练习留给读者，以了解这两种方法中的哪一种在您的应用程序中效果更好。

于 2019-06-25T07:20:41.663 回答