Question

我是CUDA和GPGPU的新手。我正在尝试检查大量数字（大于32位）的属性，我想尝试使用配备nVidia GTX 1080的Windows 7 64位机器来执行此操作：

Detected 1 CUDA Capable device(s)

Device 0: "GeForce GTX 1080"
  CUDA Driver Version / Runtime Version          8.0 / 8.0
  CUDA Capability Major/Minor version number:    6.1
  Total amount of global memory:                 8192 MBytes (8589934592 bytes)
  (20) Multiprocessors, (128) CUDA Cores/MP:     2560 CUDA Cores
  GPU Max Clock rate:                            1734 MHz (1.73 GHz)
  Memory Clock rate:                             5005 Mhz
  Memory Bus Width:                              256-bit
  L2 Cache Size:                                 2097152 bytes
  Maximum Texture Dimension Size (x,y,z)         1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
  Maximum Layered 1D Texture Size, (num) layers  1D=(32768), 2048 layers
  Maximum Layered 2D Texture Size, (num) layers  2D=(32768, 32768), 2048 layers
  Total amount of constant memory:               65536 bytes
  Total amount of shared memory per block:       49152 bytes
  Total number of registers available per block: 65536
  Warp size:                                     32
  Maximum number of threads per multiprocessor:  2048
  Maximum number of threads per block:           1024
  Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
  Max dimension size of a grid size    (x,y,z): (2147483647, 65535, 65535)
  Maximum memory pitch:                          2147483647 bytes
  Texture alignment:                             512 bytes
  Concurrent copy and kernel execution:          Yes with 2 copy engine(s)
  Run time limit on kernels:                     Yes
  Integrated GPU sharing Host Memory:            No
  Support host page-locked memory mapping:       Yes
  Alignment requirement for Surfaces:            Yes
  Device has ECC support:                        Disabled
  CUDA Device Driver Mode (TCC or WDDM):         WDDM (Windows Display Driver Model)
  Device supports Unified Addressing (UVA):      Yes
  Device PCI Domain ID / Bus ID / location ID:   0 / 1 / 0
  Compute Mode:
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

当我运行以下代码时，“sum”的值是无意义的（28,20等），即使我可以看到threadId从0到4095：

#include <cuda.h>
#include <cuda_runtime.h>
#include "device_launch_parameters.h"
#include "stdio.h"

__global__ void Simple(unsigned long long int *sum)
{
    unsigned long long int blockId = blockIdx.x + blockIdx.y * gridDim.x + gridDim.x * gridDim.y * blockIdx.z;

    unsigned long long int threadId = blockId * (blockDim.x * blockDim.y * blockDim.z)
        + (threadIdx.z * (blockDim.x * blockDim.y))
        + (threadIdx.y * blockDim.x)
        + threadIdx.x;

    printf("threadId = %llu.\n", threadId);
    // Check threadId for property.  Possibly introduce a grid stride for loop to give each thread a range to check.
    sum[0]++;
}

int main(int argc, char **argv)
{
    unsigned long long int  sum[] = { 0 };

    unsigned long long int *dev_sum;

    cudaMalloc((void**)&dev_sum, sizeof(unsigned long long int));
    cudaMemcpy(dev_sum, sum, sizeof(unsigned long long int), cudaMemcpyHostToDevice);

    dim3 grid(2, 1, 1);
    dim3 block(1024, 1, 1);

    printf("--------- Start kernel ---------\n\n");
    Simple <<< grid, block >>> (dev_sum);
    cudaDeviceSynchronize();

    cudaMemcpy(sum, dev_sum, sizeof(unsigned long long int), cudaMemcpyDeviceToHost);

    printf("sum = %llu.\n", sum[0]);

    cudaFree(dev_sum);

    getchar();

    return 0;
}

如何通过添加网格步长循环来修改此内核调用以获取最大线程数（使用我的设置）在0到10 ^ 12之间的数字范围内运行？

dim3 grid(2, 1, 1);
dim3 block(1024, 1, 1);

Simple <<< grid, block >>> (dev_sum);

Answer 1

所有线程都在内存中的相同位置进行递增，这会导致竞争条件。这就是结果不正确的原因。你应该使用原子加法使它正确（在CUDA中有一个函数）。

使用CUDA线程索引作为数字

1 个答案: