Gemini代码摘抄（三）process_vertices

process_vertices函数

有两个参数：process和要处理的点集合

process_vertices(std::function<R(VertexId)> process, Bitmap * active) {}

其调用如下：

double delta = graph->process_vertices<double>(
    [&](VertexId vtx){
      curr[vtx] = (double)1;
      if (graph->out_degree[vtx]>0) {
        curr[vtx] /= graph->out_degree[vtx];
      }
      return (double)1;
    },
    active_in
  );

函数体内部如下：

根据已有的threads和设置的threads_per_socket把线程划分到分区，s_i 是分区号，s_j socket内的线程编号

 // process vertices
  template<typename R>
  R process_vertices(std::function<R(VertexId)> process, Bitmap * active) {
    double stream_time = 0;
    stream_time -= MPI_Wtime();

    R reducer = 0;
    size_t basic_chunk = 64;
    for (int t_i=0;t_i<threads;t_i++) {
      int s_i = get_socket_id(t_i);
      int s_j = get_socket_offset(t_i);

计算分区大小partition_size，thread_state[t_i]->curr和thread_state[t_i]->end分别表示分区内每个线程t_i负责部分的首尾

      VertexId partition_size = local_partition_offset[s_i+1] -local_partition_offset[s_i];
      thread_state[t_i]->curr = local_partition_offset[s_i] + partition_size / threads_per_socket  / basic_chunk * basic_chunk * s_j;
      thread_state[t_i]->end = local_partition_offset[s_i] + partition_size / threads_per_socket / basic_chunk * basic_chunk * (s_j+1);

如果一个分区规定的线程数已经满了，此时线程的尾就设置为下一分区的开始。把当前线程的状态设置为working，表示正在执行自己的任务。

      if (s_j == threads_per_socket - 1) {
        thread_state[t_i]->end = local_partition_offset[s_i+1];
      }
      thread_state[t_i]->status = WORKING;
    }

下列代码是并行执行的。
__sync_fetch_and_add相关介绍看这里

下面一段代码讲的是线程执行自己的任务。

把线程现在的位置赋给v_i然后curr往后移64位，一个基本块的大小，当v_i已经到end的时候就跳出循环，表示当前线程自己的工作已经完成了，该线程状态置为stealing，看看其他线程还有什么工作可以帮忙做的。

    #pragma omp parallel reduction(+:reducer)
    {
      R local_reducer = 0;
      int thread_id = omp_get_thread_num();
      while (true) {
        VertexId v_i = __sync_fetch_and_add(&thread_state[thread_id]->curr, basic_chunk);
        if (v_i >= thread_state[thread_id]->end) break;

下面这一段不是很懂
从v_i开始，做完一个往后移动一个，直到word为0

        unsigned long word = active->data[WORD_OFFSET(v_i)];
        while (word != 0) {
          if (word & 1) {
            local_reducer += process(v_i);
          }
          v_i++;
          word = word >> 1;
        }
      }
      thread_state[thread_id]->status = STEALING;    

下面一段代码讲的是线程stealing任务做。

根据当前线程id计算一个线程id出来赋值给t_i，当线程t_i的状态处于stealing时，他就执行reduce

      for (int t_offset=1;t_offset<threads;t_offset++) {
        int t_i = (thread_id + t_offset) % threads;
        while (thread_state[t_i]->status!=STEALING) {
          VertexId v_i = __sync_fetch_and_add(&thread_state[t_i]->curr, basic_chunk);
          if (v_i >= thread_state[t_i]->end) continue;
          unsigned long word = active->data[WORD_OFFSET(v_i)];
          while (word != 0) {
            if (word & 1) {
              local_reducer += process(v_i);
            }
            v_i++;
            word = word >> 1;
          }
        }
      }
      reducer += local_reducer;
    }

以上都是并行执行的代码。
MPI_ALLreduce函数
相关MPI编程

    R global_reducer;
    MPI_Datatype dt = get_mpi_data_type<R>();
    MPI_Allreduce(&reducer, &global_reducer, 1, dt, MPI_SUM, MPI_COMM_WORLD);
    stream_time += MPI_Wtime();
    #ifdef PRINT_DEBUG_MESSAGES
    if (partition_id==0) {
      printf("process_vertices took %lf (s)\n", stream_time);
    }
    #endif
    return global_reducer;
  }

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MPI资料下载:http://micro.ustc.edu.cn/Linux/MPI/