在cuda中线程/扭曲本地锁定

时间:2017-08-16 16:31:58

标签: cuda critical-section gpu-warp

我想在cuda中实施关键部分。我在这个主题上阅读了很多问题和答案,答案通常涉及atomicCAS和atomicExch。

但是,这在warp级别不起作用,因为warp中的所有线程在atomicCAS之后获取相同的锁,从而导致死锁。

我认为通过使用warp __ballot或__any指令,有一种方法可以真正锁定cuda。

然而,经过多次尝试,我没有达到令人满意的(阅读工作)解决方案。

这里有没有人对此有一个好的答案?

ps:我知道经线发散是不好的,所以不要告诉我改变我的算法。

1 个答案:

答案 0 :(得分:3)

Atomics可以在warp级别工作。锁内经线协商的危险与锁的获取方式有关。诸如thisthis以及this之类的问题指出了这种危险,但您不应将此视为使用原子的限制。由于条件代码在GPU(*)上的执行方式以及条件构造中的2条路径之间的依赖性而产生危险。

为了避免扭曲中的上述危险,有必要打破条件结构中的2个路径之间的依赖关系。锁的获取必须能够独立于关键部分和锁的释放进行。

然而,既然你只想在经线内进行协调,那么原子就是矫枉过正。我们已经有了许多强大的warp通信和协调机制,例如你提到的__ballot()

接下来是一个在机制中使用__ballot()以确保只有1个线程(每个warp)将“获取锁定”的工作示例。由于这一切都只发生在经线级别,我们再一次可以利用它来完全免除锁定的释放。我们可以知道在关键部分完成时释放锁定(因为我们只需要在整个warp中跟踪完成)。

此示例使用__ballot()机制在warp级别执行流压缩,以确保一次只有1个线程进入“临界区”:

$ cat t398.cu
#include <stdio.h>
#include <stdlib.h>

const int num_threads = 1024;  // should be 1024 or less for this demo

// returns true if the thread acquired the lock
// will return true for at most 1 thread per warp

__device__ bool warp_lock(int req){
  return ((__ffs(__ballot(req))) == ((threadIdx.x & 31)+1));
}

__global__ void test_lock(int *pattern, int *result){

  __shared__ int done;
  __shared__ int warp_index[32];
  int myreq = pattern[threadIdx.x];
  done = 1;
  if ((threadIdx.x & 31) == 0) warp_index[threadIdx.x>>5] = 0;
  do {
    done = 1;
    __syncthreads();
    // attempt to  "acquire lock"
    bool mylock = warp_lock(myreq);
    // if lock acquired, do "critical section"
    if (mylock){
      done = 0;
      int my_index = (warp_index[threadIdx.x>>5]++) + ((threadIdx.x>>5)*32);
      result[my_index] = threadIdx.x;
      myreq = 0;
      }
    __syncthreads();
    } while (!done);
}

int main(){

  int *h_pattern, *d_pattern, *h_result, *d_result;
  h_pattern = (int *)malloc(num_threads*sizeof(int));
  h_result  = (int *)malloc(num_threads*sizeof(int));
  cudaMalloc(&d_pattern, num_threads*sizeof(int));
  cudaMalloc(&d_result, num_threads*sizeof(int));
  for (int i = 0; i < num_threads; i++) {h_pattern[i] = rand()&1;}
  cudaMemcpy(d_pattern, h_pattern, num_threads*sizeof(int), cudaMemcpyHostToDevice);
  cudaMemset(d_result, 0, num_threads*sizeof(int));
  test_lock<<<1, num_threads>>>(d_pattern, d_result);
  cudaMemcpy(h_result, d_result, num_threads*sizeof(int), cudaMemcpyDeviceToHost);
  printf("index, in, out\n");
  for (int i = 0; i < num_threads; i++)
    printf("%4d, %d, %4d\n", i, h_pattern[i], h_result[i]);
  return 0;
}
$ nvcc -arch=sm_61 -o t398 t398.cu
$ cuda-memcheck ./t398
========= CUDA-MEMCHECK
index, in, out
   0, 1,    0
   1, 0,    2
   2, 1,    3
   3, 1,    4
   4, 1,    5
   5, 1,    8
   6, 0,    9
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   8, 1,   13
   9, 1,   14
  10, 0,   20
  11, 1,   22
  12, 0,   23
  13, 1,   27
  14, 1,   28
  15, 0,   29
  16, 0,   30
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  36, 1,   40
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  42, 1,   50
  43, 1,   52
  44, 0,   55
  45, 1,   59
  46, 0,   60
  47, 0,   61
  48, 1,   63
  49, 0,    0
  50, 1,    0
  51, 0,    0
  52, 1,    0
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  63, 1,    0
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  65, 1,   67
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  70, 0,   75
  71, 1,   78
  72, 0,   80
  73, 1,   86
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  77, 0,    0
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 798, 1,    0
 799, 0,    0
 800, 0,  803
 801, 0,  804
 802, 0,  805
 803, 1,  806
 804, 1,  807
 805, 1,  808
 806, 1,  810
 807, 1,  812
 808, 1,  813
 809, 0,  814
 810, 1,  815
 811, 0,  816
 812, 1,  817
 813, 1,  820
 814, 1,  826
 815, 1,  829
 816, 1,  831
 817, 1,    0
 818, 0,    0
 819, 0,    0
 820, 1,    0
 821, 0,    0
 822, 0,    0
 823, 0,    0
 824, 0,    0
 825, 0,    0
 826, 1,    0
 827, 0,    0
 828, 0,    0
 829, 1,    0
 830, 0,    0
 831, 1,    0
 832, 1,  832
 833, 1,  833
 834, 0,  838
 835, 0,  839
 836, 0,  842
 837, 0,  843
 838, 1,  844
 839, 1,  847
 840, 0,  849
 841, 0,  850
 842, 1,  851
 843, 1,  852
 844, 1,  853
 845, 0,  856
 846, 0,  857
 847, 1,  859
 848, 0,  863
 849, 1,    0
 850, 1,    0
 851, 1,    0
 852, 1,    0
 853, 1,    0
 854, 0,    0
 855, 0,    0
 856, 1,    0
 857, 1,    0
 858, 0,    0
 859, 1,    0
 860, 0,    0
 861, 0,    0
 862, 0,    0
 863, 1,    0
 864, 1,  864
 865, 1,  865
 866, 0,  867
 867, 1,  868
 868, 1,  869
 869, 1,  871
 870, 0,  873
 871, 1,  874
 872, 0,  875
 873, 1,  877
 874, 1,  881
 875, 1,  882
 876, 0,  885
 877, 1,  887
 878, 0,  888
 879, 0,  890
 880, 0,  891
 881, 1,  893
 882, 1,  894
 883, 0,    0
 884, 0,    0
 885, 1,    0
 886, 0,    0
 887, 1,    0
 888, 1,    0
 889, 0,    0
 890, 1,    0
 891, 1,    0
 892, 0,    0
 893, 1,    0
 894, 1,    0
 895, 0,    0
 896, 0,  897
 897, 1,  898
 898, 1,  899
 899, 1,  902
 900, 0,  904
 901, 0,  907
 902, 1,  908
 903, 0,  910
 904, 1,  913
 905, 0,  919
 906, 0,  921
 907, 1,  922
 908, 1,  927
 909, 0,    0
 910, 1,    0
 911, 0,    0
 912, 0,    0
 913, 1,    0
 914, 0,    0
 915, 0,    0
 916, 0,    0
 917, 0,    0
 918, 0,    0
 919, 1,    0
 920, 0,    0
 921, 1,    0
 922, 1,    0
 923, 0,    0
 924, 0,    0
 925, 0,    0
 926, 0,    0
 927, 1,    0
 928, 1,  928
 929, 0,  931
 930, 0,  933
 931, 1,  935
 932, 0,  936
 933, 1,  938
 934, 0,  939
 935, 1,  942
 936, 1,  944
 937, 0,  945
 938, 1,  946
 939, 1,  948
 940, 0,  949
 941, 0,  950
 942, 1,  951
 943, 0,  954
 944, 1,  958
 945, 1,  959
 946, 1,    0
 947, 0,    0
 948, 1,    0
 949, 1,    0
 950, 1,    0
 951, 1,    0
 952, 0,    0
 953, 0,    0
 954, 1,    0
 955, 0,    0
 956, 0,    0
 957, 0,    0
 958, 1,    0
 959, 1,    0
 960, 0,  962
 961, 0,  964
 962, 1,  965
 963, 0,  966
 964, 1,  967
 965, 1,  968
 966, 1,  971
 967, 1,  972
 968, 1,  973
 969, 0,  977
 970, 0,  979
 971, 1,  985
 972, 1,  987
 973, 1,  988
 974, 0,  991
 975, 0,    0
 976, 0,    0
 977, 1,    0
 978, 0,    0
 979, 1,    0
 980, 0,    0
 981, 0,    0
 982, 0,    0
 983, 0,    0
 984, 0,    0
 985, 1,    0
 986, 0,    0
 987, 1,    0
 988, 1,    0
 989, 0,    0
 990, 0,    0
 991, 1,    0
 992, 0,  993
 993, 1,  994
 994, 1,  995
 995, 1,  997
 996, 0,  999
 997, 1, 1000
 998, 0, 1002
 999, 1, 1004
1000, 1, 1005
1001, 0, 1006
1002, 1, 1007
1003, 0, 1009
1004, 1, 1012
1005, 1, 1018
1006, 1, 1019
1007, 1, 1021
1008, 0, 1022
1009, 1,    0
1010, 0,    0
1011, 0,    0
1012, 1,    0
1013, 0,    0
1014, 0,    0
1015, 0,    0
1016, 0,    0
1017, 0,    0
1018, 1,    0
1019, 1,    0
1020, 0,    0
1021, 1,    0
1022, 1,    0
1023, 0,    0
========= ERROR SUMMARY: 0 errors
$

请注意,所有线程都可以通过“获取锁定”部分继续进行而没有任何线程间依赖性(尽管一次只有一个线程将“赢”),并注意所有线程都可以通过“临界区”继续执行任何线程间依赖。

(*)请注意,这种情况为mitigated in Volta