Caffe源码解读2--syncedmem.hpp

#ifndef CAFFE_SYNCEDMEM_HPP_
#define CAFFE_SYNCEDMEM_HPP_

#include <cstdlib>

#include "caffe/common.hpp"
#include "caffe/util/math_functions.hpp"

namespace caffe {

// Theoretically, CaffeMallocHost and CaffeFreeHost should simply call the
// cudaMallocHost and cudaFree functions in order to create pinned memory.
// However, those codes rely on the existence of a cuda GPU (I don't know
// why that is a must since allocating memory should not be accessing the
// GPU resource, but it just creates an error as of Cuda 5.0) and will cause
// problem when running on a machine without GPU. Thus, we simply define
// these two functions for safety and possible future change if the problem
// of calling cuda functions disappears in a future version.
//
// In practice, although we are creating unpinned memory here, as long as we
// are constantly accessing them the memory pages almost always stays in
// the physical memory (assuming we have large enough memory installed), and
// does not seem to create a memory bottleneck here.


//SynceMem：做内存同步的操作。

//两个全局的内联函数。
//机器支持GPU并且安装了cuda，通过cudaMallocHost分配的hostmemory将会被pinned（内存不会被paged out），
//注：内存有页作为基本的管理单元，分配的内存可以常住在内存空间中对提高效率有帮助，空间不会被别的进程抢占。
      //内存越大，能背分配的pinned内存自然也越大。对于单一的GPU提升不显著，但对多个GPU可以显著提高稳定性。


inline void CaffeMallocHost(void** ptr, size_t size) {
  *ptr = malloc(size);
  CHECK(*ptr) << "host allocation of size " << size << " failed";
}

inline void CaffeFreeHost(void* ptr) {
  free(ptr);
}


/**
 * @brief Manages memory allocation and synchronization between the host (CPU)
 *        and device (GPU).
 *
 * TODO(dox): more thorough description.
 */
class SyncedMemory {//SyncedMemory类，首先是构造函数和析构函数。
 public:
  SyncedMemory()//参数构造函数：负责初始化
      : cpu_ptr_(NULL), gpu_ptr_(NULL), size_(0), head_(UNINITIALIZED),
        own_cpu_data_(false) {}
  explicit SyncedMemory(size_t size)//带explicit关键字：单个参数构造函数，explicit禁止单参数构造函数的隐式转换
      : cpu_ptr_(NULL), gpu_ptr_(NULL), size_(size), head_(UNINITIALIZED),
        own_cpu_data_(false) {}
  ~SyncedMemory();//其在析构时，也调用CaffeFreeHost
 
  const void* cpu_data();//获得cpu上data的地址
  void set_cpu_data(void* data);//将cpu的data指针指向一个新的区域由data指针传入，并且将原来申请的内存释放。
  const void* gpu_data();//获得gpu数据地址//获得set gpu的数据地址
 
  void* mutable_cpu_data();//返回cpu上data的指针
  void* mutable_gpu_data();//返回gpu上data指针
  enum SyncedHead { UNINITIALIZED, HEAD_AT_CPU, HEAD_AT_GPU, SYNCED };//SyncedHead是个枚举类型，用来设定head_状态，
  SyncedHead head() { return head_; }//head()函数即返回相应的数据状态，
  size_t size() { return size_; }//size（）函数返回数据大小

 private:
  void to_cpu();
  void to_gpu();
  void* cpu_ptr_;
  void* gpu_ptr_;
  size_t size_;
  SyncedHead head_;
  bool own_cpu_data_;

//head未被初始化的状态：首先需要分配内存，根据cpu和gpu的情况而定，再将数据从cpu或gpu拷贝到另一处。
//之后函数会重新标记Head状态，数据是否在cpu或者在gpu中。
//cpu_ptr和gpu_ptr分别是cpu和gpu中的数据指针

  DISABLE_COPY_AND_ASSIGN(SyncedMemory);//禁止该类的拷贝与赋值
};  // class SyncedMemory

}  // namespace caffe

#endif  // CAFFE_SYNCEDMEM_HPP_