Question

我写的是在我的Nvidia GTX 560Ti卡上使用CUDA的 atomicAdd（）时发生的明显损坏。在开发某些代码期间，我遇到了使用atomicAdd的问题，它似乎破坏了内存。我设计了一个测试，以确定是否确实如此，以及是否可以在我的应用条件之外复制该行为。我编写了一个测试程序，它使用atomicAdd增加缓冲区中稀疏位置的数量。在我的560Ti上，测试表明atomicAdd 会破坏内存中的随机位。特别地，翻转了在有意不被访问或修改的位置中的少量（看似）随机放置的位。内核代码很简单，它只有一个atomicAdd。测试代码如下：

#include <stdio.h>
#include <stdlib.h>
#include <sys/time.h>

#define ANSI_RED "\e[0;41m\e[41;37m"
#define ANSI_BLACK "\e[0;30m"

__global__ void kernel( unsigned int *a, unsigned int *map, int M, int N )
{
    // Add to buffer.
    atomicAdd( a + map[ blockIdx.x * N + threadIdx.x ], 1 );
}

template < class T > void swap( T &a, T &b ) { T t; t = a; a = b; b = t; }

int main( void )
{
    // Chooses 560Ti on my machine
    cudaSetDevice( 1 );
    srand( time( 0 ) );
    unsigned int M = 1024, N = 256;
    unsigned int L = M * N, K = N;
    unsigned int *dev_buf, *dev_map;
    unsigned int *buf = new unsigned int[ L ];
    unsigned int *map = new unsigned int[ L ];
    unsigned int *indices = new unsigned int[ K ];
    bool *check = new bool[ L ];

    // Use buffer to indicate which spots in buffer should have valid values.
    for( int l = 0; l < L; l++ ) check[ l ] = false;

    // Generate K random indices into an L-sized buffer, init "check"
    for( int k = 0; k < K; k++ )
    {
        int i = rand( ) % L;
        while( check[ i ] )
            i = rand( ) % L;
        indices[ k ] = i;
        check[ i ] = true;
    }

    // Generate a random M (blocks) x N (threads) array "map" of indices that contains
    //   offsets into "buf" such that there are at most K locations in "buf" that
    //   should be written to.
    for( int m = 0; m < M; m++ )
        for( int n = 0; n < N; n++ ) // Init.
            map[ m * N + n ] = indices[ n ];
    for( int i = 0; i < L; i++ ) // Shuffle.
        swap( map[ i ], map[ i + rand( ) % ( L - i ) ] );

    // Allocate and initialize device memory.
    cudaMalloc( &dev_buf, L * sizeof( unsigned int ) );
    cudaMalloc( &dev_map, N * M * sizeof( unsigned int ) );
    cudaMemset( dev_buf, 0, L * sizeof( unsigned int ) );
    cudaMemcpy( dev_map, map, L * sizeof( unsigned int ), cudaMemcpyHostToDevice );

    kernel<<< M, N >>>( dev_buf, dev_map, M, N );

    // Copy back to host.
    cudaMemcpy( buf, dev_buf, L * sizeof( unsigned int ), cudaMemcpyDeviceToHost );

    // Print non-zero values.  Highlight abnormalities.
    int j = 0;
    for( int i = 0; i < L; i++ )
    {
        if( buf[ i ] != 0 )
        {
            if( ( buf[ i ] == M ) || ( buf[ i ] == 2 * M ) )
                printf( "%d @ %d [%s]\t",
                        buf[ i ], i, check[ i ] ? "true" : "false" );
            else
                printf( ANSI_RED "%d @ %d [%s]\t" ANSI_BLACK,
                        buf[ i ], i, check[ i ] ? "true" : "false" );
            j++;
        }
    }
    printf( "\nj = %d\n", j );
}

编译：

nvcc test_atomicadd_bug.cu -o test_atomicadd_bug -arch sm_21

所有内核调用应该（总计）将所有K个位置递增M次，从而在每个位置产生K * M = 1024的结果。因此，在运行代码时，它应该打印出非零值（1024）及其位置。但是，在下面的示例输出中，除了255个1024实例外，它还打印出一个1023和一个1。在其他运行中，结果是不同的。即使srand（0）替换了时间看到的RNG，运行结果也不同。我在GTX 560Ti和特斯拉C2070上都试过这个。特斯拉没有任何腐败现象。我无法访问另一个560Ti。

1024 @ 1228 [true]    1024 @ 1271 [true]    1024 @ 1842 [true]    1024 @ 2480 [true]    1024 @ 3012 [true]
1024 @ 3802 [true]    1024 @ 4649 [true]    1024 @ 5636 [true]    1024 @ 6988 [true]    1024 @ 9400 [true]
1024 @ 10912 [true]    1024 @ 10930 [true]    1024 @ 11550 [true]    1024 @ 11888 [true]    1024 @ 12047 [true]
1024 @ 12837 [true]    1024 @ 12868 [true]    1024 @ 12991 [true]    1024 @ 16294 [true]    1024 @ 16690 [true]
1024 @ 17396 [true]    1024 @ 17529 [true]    1024 @ 19857 [true]    1024 @ 20926 [true]    1024 @ 22189 [true]
1024 @ 22391 [true]    1024 @ 22613 [true]    1024 @ 22851 [true]    1024 @ 23562 [true]    1024 @ 23955 [true]
1024 @ 24598 [true]    1024 @ 26058 [true]    1024 @ 26441 [true]    1024 @ 26962 [true]    1024 @ 27141 [true]
1024 @ 28101 [true]    1024 @ 28332 [true]    1024 @ 29485 [true]    1024 @ 29487 [true]    1024 @ 29942 [true]
1024 @ 31213 [true]    1024 @ 31965 [true]    1024 @ 35774 [true]    1024 @ 39342 [true]    1024 @ 39883 [true]
1024 @ 39960 [true]    1024 @ 40252 [true]    1024 @ 41435 [true]    1024 @ 42975 [true]    1024 @ 43336 [true]
1024 @ 44527 [true]    1024 @ 44657 [true]    1 @ 45494 [false]    1024 @ 46940 [true]    1024 @ 46983 [true]
1024 @ 48660 [true]    1024 @ 49034 [true]    1024 @ 49420 [true]    1024 @ 49620 [true]    1024 @ 50813 [true]
1024 @ 53859 [true]    1024 @ 55527 [true]    1024 @ 56677 [true]    1024 @ 57048 [true]    1024 @ 57759 [true]
1024 @ 58505 [true]    1024 @ 59539 [true]    1024 @ 59856 [true]    1024 @ 60341 [true]    1024 @ 61556 [true]
1024 @ 61733 [true]    1023 @ 61878 [true]    1024 @ 62025 [true]    1024 @ 65333 [true]    1024 @ 66131 [true]
1024 @ 67196 [true]    1024 @ 69428 [true]    1024 @ 70555 [true]    1024 @ 73135 [true]    1024 @ 73696 [true]
1024 @ 76797 [true]    1024 @ 76947 [true]    1024 @ 79166 [true]    1024 @ 79301 [true]    1024 @ 80182 [true]
1024 @ 80348 [true]    1024 @ 80574 [true]    1024 @ 81386 [true]    1024 @ 84416 [true]    1024 @ 86472 [true]
1024 @ 88234 [true]    1024 @ 88622 [true]    1024 @ 89355 [true]    1024 @ 89571 [true]    1024 @ 90716 [true]
1024 @ 91386 [true]    1024 @ 94846 [true]    1024 @ 95779 [true]    1024 @ 99146 [true]    1024 @ 99569 [true]
1024 @ 100202 [true]    1024 @ 102972 [true]    1024 @ 103909 [true]    1024 @ 104373 [true]    1024 @ 107707 [true]
1024 @ 108543 [true]    1024 @ 108617 [true]    1024 @ 109212 [true]    1024 @ 109388 [true]    1024 @ 111836 [true]
1024 @ 113078 [true]    1024 @ 113343 [true]    1024 @ 114451 [true]    1024 @ 114849 [true]    1024 @ 115024 [true]
1024 @ 115338 [true]    1024 @ 116675 [true]    1024 @ 118624 [true]    1024 @ 119884 [true]    1024 @ 120807 [true]
1024 @ 121993 [true]    1024 @ 122050 [true]    1024 @ 124643 [true]    1024 @ 125161 [true]    1024 @ 125843 [true]
1024 @ 126890 [true]    1024 @ 127718 [true]    1024 @ 127810 [true]    1024 @ 129646 [true]    1024 @ 129907 [true]
1024 @ 132288 [true]    1024 @ 132706 [true]    1024 @ 135574 [true]    1024 @ 136913 [true]    1024 @ 137346 [true]
1024 @ 138326 [true]    1024 @ 138685 [true]    1024 @ 138939 [true]    1024 @ 140996 [true]    1024 @ 141304 [true]
1024 @ 143902 [true]    1024 @ 145723 [true]    1024 @ 146149 [true]    1024 @ 149696 [true]    1024 @ 149726 [true]
1024 @ 150294 [true]    1024 @ 152057 [true]    1024 @ 152198 [true]    1024 @ 152239 [true]    1024 @ 153002 [true]
1024 @ 153776 [true]    1024 @ 156081 [true]    1024 @ 156377 [true]    1024 @ 156654 [true]    1024 @ 158008 [true]
1024 @ 158677 [true]    1024 @ 159369 [true]    1024 @ 159996 [true]    1024 @ 160060 [true]    1024 @ 161456 [true]
1024 @ 161732 [true]    1024 @ 163269 [true]    1024 @ 163675 [true]    1024 @ 163684 [true]    1024 @ 164397 [true]
1024 @ 165077 [true]    1024 @ 166036 [true]    1024 @ 168301 [true]    1024 @ 168409 [true]    1024 @ 171499 [true]
1024 @ 171772 [true]    1024 @ 173353 [true]    1024 @ 175290 [true]    1024 @ 175573 [true]    1024 @ 177155 [true]
1024 @ 178142 [true]    1024 @ 178718 [true]    1024 @ 178822 [true]    1024 @ 179161 [true]    1024 @ 179654 [true]
1024 @ 180683 [true]    1024 @ 182432 [true]    1024 @ 183086 [true]    1024 @ 183695 [true]    1024 @ 184730 [true]
1024 @ 186884 [true]    1024 @ 187746 [true]    1024 @ 188603 [true]    1024 @ 188948 [true]    1024 @ 189124 [true]
1024 @ 190268 [true]    1024 @ 191208 [true]    1024 @ 192630 [true]    1024 @ 193617 [true]    1024 @ 195426 [true]
1024 @ 198352 [true]    1024 @ 201345 [true]    1024 @ 201416 [true]    1024 @ 203214 [true]    1024 @ 205418 [true]
1024 @ 207467 [true]    1024 @ 208763 [true]    1024 @ 208924 [true]    1024 @ 209269 [true]    1024 @ 210679 [true]
1024 @ 211622 [true]    1024 @ 212029 [true]    1024 @ 212135 [true]    1024 @ 213228 [true]    1024 @ 216151 [true]
1024 @ 216425 [true]    1024 @ 216432 [true]    1024 @ 218039 [true]    1024 @ 219445 [true]    1024 @ 219675 [true]
1024 @ 220504 [true]    1024 @ 220702 [true]    1024 @ 220716 [true]    1024 @ 222687 [true]    1024 @ 223582 [true]
1024 @ 223758 [true]    1024 @ 223917 [true]    1024 @ 224254 [true]    1024 @ 224825 [true]    1024 @ 224845 [true]
1024 @ 225372 [true]    1024 @ 226297 [true]    1024 @ 228158 [true]    1024 @ 228367 [true]    1024 @ 229494 [true]
1024 @ 229636 [true]    1024 @ 230722 [true]    1024 @ 232001 [true]    1024 @ 232693 [true]    1024 @ 234729 [true]
1024 @ 235132 [true]    1024 @ 242699 [true]    1024 @ 245103 [true]    1024 @ 245948 [true]    1024 @ 246903 [true]
1024 @ 247836 [true]    1024 @ 247871 [true]    1024 @ 248694 [true]    1024 @ 248801 [true]    1024 @ 250204 [true]
1024 @ 250899 [true]    1024 @ 250968 [true]    1024 @ 251738 [true]    1024 @ 251930 [true]    1024 @ 256221 [true]
1024 @ 258244 [true]    1024 @ 258908 [true]    1024 @ 259884 [true]    1024 @ 260318 [true]    1024 @ 260424 [true]
1024 @ 260884 [true]    1024 @ 260953 [true]
j = 257

我的问题是：我使用atomicAdd的方式有问题吗？这是否发生在其他Nvidia GPU上？其他560 Ti？可以想象我的卡有问题吗？在560Ti上，atomicAdd真的可能不安全吗？

提前感谢您的帮助，克里斯

编辑：我的卡一定不好。当我用常规添加操作替换atomicAdd时，此测试也失败。（是的，值不再是常数，因为操作不是原子，竞争条件等 - 但是，在它们应该为零的地方存在非零值，不应该在那些存储器位置执行操作。）它还会继续重启，我正在进入只运行登录屏幕的重启系统（所以可能是X，但没有OpenGL？）。系统正在运行Ubuntu 10.04和CUDA 4.0。 GPU是GeForce GTX 560 Ti。 有谁知道这是一种常见的故障模式吗？

Answer 1

CentOS 6.3 CUDA 5.0 GeForce 560Ti - 输出中没有“假”。

只有特斯拉才能保证在GPGPU计算中正常工作。 GeForce卡 - 不是。这就是特斯拉这么贵的原因。

尝试升级CUDA工具包。它可以解决问题...但我想这是你硬件的错误。

编辑：我注意到你安装了几个视频卡。你检查了温度吗？我的朋友遇到了一个奇怪的来源问题：一个GPU加热了第二个GPU的PCI插槽（或类似的东西），第二个卡产生了不好的结果。

原子Add（）在GTX 560 Ti上是否安全？

1 个答案: