一、前言
Syncedmemory类包含的头文件只有一个common.hpp。而common也是整个caffe的基础设施之一，主要作用是管理全局的资源。在caffe中的全局资源有哪些呢？
根据代码，可以总结为：
1、随机数;
2、GPU设备信息;
3、并行的训练时的solver_rank等。
caffe就是将这些全局信息用全局静态变量表示，看起简直有点too young too simple。
那么问题是什么？简单，就是多线程下对全局资源的争夺。对付这种race我知道可以从两处入手：
1、阻塞，让一个线程访问的时候，其他线程休眠;
2、加锁，就是让原本并行的访问变成穿行的访问。
但是这两种都是降低多线程效率的方法，caffe则引入了boost中的局部显存存储。具体地跟着代码走两步。
二、源码分析
首先看包含关系，因为很多…….
#include <boost/shared_ptr.hpp>//boost
#include <gflags/gflags.h>
#include <glog/logging.h>
#include <climits>
#include <cmath>
#include <fstream> // NOLINT(readability/streams)
#include <iostream> // NOLINT(readability/streams)
#include <map>
#include <set>
#include <sstream>
#include <string>
#include <utility> // pair
#include <vector>
#include "caffe/util/device_alternate.hpp"
然后是一个将宏转换为字符串的宏：
// Convert macro to string
#define STRINGIFY(m) #m
#define AS_STRING(m) STRINGIFY(m)
实例化类，据说是和模板头文件和实现分离有关，模板我不熟，尚待进一步研究：
// Instantiate a class with float and double specifications.
#define INSTANTIATE_CLASS(classname) \
char gInstantiationGuard##classname; \
template class classname<float>; \
template class classname<double>
#define INSTANTIATE_LAYER_GPU_FORWARD(classname) \
template void classname<float>::Forward_gpu( \
const std::vector<Blob<float>*>& bottom, \
const std::vector<Blob<float>*>& top); \
template void classname<double>::Forward_gpu( \
const std::vector<Blob<double>*>& bottom, \
const std::vector<Blob<double>*>& top);
#define INSTANTIATE_LAYER_GPU_BACKWARD(classname) \
template void classname<float>::Backward_gpu( \
const std::vector<Blob<float>*>& top, \
const std::vector<bool>& propagate_down, \
const std::vector<Blob<float>*>& bottom); \
template void classname<double>::Backward_gpu( \
const std::vector<Blob<double>*>& top, \
const std::vector<bool>& propagate_down, \
const std::vector<Blob<double>*>& bottom)
#define INSTANTIATE_LAYER_GPU_FUNCS(classname) \
INSTANTIATE_LAYER_GPU_FORWARD(classname); \
INSTANTIATE_LAYER_GPU_BACKWARD(classname)
这里可以看到common确实是基础设施的感觉，把整个系统的命名空间都给申明了：
// Common functions and classes from std that caffe often uses.
using std::fstream;
using std::ios;
using std::isnan;
using std::isinf;
using std::iterator;
using std::make_pair;
using std::map;
using std::ostringstream;
using std::pair;
using std::set;
using std::string;
using std::stringstream;
using std::vector;
下面就是common定义的Caffe类，该类负责实际管理全局资源：
// 全局初始化函数，在main函数中调用的，主要是初始化gfags和glogs
void GlobalInit(int* pargc, char*** pargv);
// 单例模式，定义Caffe类
class Caffe {
public:
~Caffe();
// 利用局部线程存储，保证每个线程只有一个实例
static Caffe& Get();
enum Brew { CPU, GPU };
//随机数类，关键的在 class Generator
class RNG {
public:
RNG();
explicit RNG(unsigned int seed);
explicit RNG(const RNG&);
RNG& operator=(const RNG&);
void* generator();
private:
class Generator;
shared_ptr<Generator> generator_;
};
// 获得随机数、cublas_handle等全局资源
inline static RNG& rng_stream() {
if (!Get().random_generator_) {
Get().random_generator_.reset(new RNG());
}
return *(Get().random_generator_);
}
#ifndef CPU_ONLY
inline static cublasHandle_t cublas_handle() { return Get().cublas_handle_; }
inline static curandGenerator_t curand_generator() {
return Get().curand_generator_;
}
#endif
// Returns the mode: running on CPU or GPU.
inline static Brew mode() { return Get().mode_; }
// The setters for the variables
// Sets the mode. It is recommended that you don't change the mode halfway
// into the program since that may cause allocation of pinned memory being
// freed in a non-pinned way, which may cause problems - I haven't verified
// 作者建议不要中途更换mode，因为这可能引起锁页内存被用分页内存的方式释放，这样有问题。
inline static void set_mode(Brew mode) { Get().mode_ = mode; }
// Sets the random seed of both boost and curand
static void set_random_seed(const unsigned int seed);
// Sets the device. Since we have cublas and curand stuff, set device also
// requires us to reset those values.
static void SetDevice(const int device_id);
// Prints the current GPU status.
static void DeviceQuery();
// Check if specified device is available
static bool CheckDevice(const int device_id);
// Search from start_id to the highest possible device ordinal,
// return the ordinal of the first available device.
static int FindDevice(const int start_id = );
// Parallel training
inline static int solver_count() { return Get().solver_count_; }
inline static void set_solver_count(int val) { Get().solver_count_ = val; }
inline static int solver_rank() { return Get().solver_rank_; }
inline static void set_solver_rank(int val) { Get().solver_rank_ = val; }
inline static bool multiprocess() { return Get().multiprocess_; }
inline static void set_multiprocess(bool val) { Get().multiprocess_ = val; }
inline static bool root_solver() { return Get().solver_rank_ == ; }
protected:
#ifndef CPU_ONLY
cublasHandle_t cublas_handle_;
curandGenerator_t curand_generator_;
#endif
shared_ptr<RNG> random_generator_;
Brew mode_;
// Parallel training
int solver_count_;
int solver_rank_;
bool multiprocess_;
private:
// 禁止构造函数，防止出现相同实例
Caffe();
DISABLE_COPY_AND_ASSIGN(Caffe);//禁止复制和赋值
};
}
实现
首先，看局部线程存储(TSL)的应用。
(1)局部线程存储机制：
对于共享资源，TSL保证每个线程拥有一个资源的副本，然后允许各线程访问各自对应的副本，最后再将副本合并。
static boost::thread_specific_ptr<Caffe> thread_instance_;//智能指针的一种，为了实现TSL
Caffe& Caffe::Get() {
if (!thread_instance_.get()) {//确保该线程内没有Caffe实例，再创建新实例。
thread_instance_.reset(new Caffe());
}
return *(thread_instance_.get());//返回的是实例而不再是指针了。智能指针的get()将使变量脱离智能指针的控制
}
(2)随机数
1、随机数种子可以由熵获得，或者是根据时间。
// 随机数种子
int64_t cluster_seedgen(void) {
int64_t s, seed, pid;
FILE* f = fopen("/dev/urandom", "rb");
if (f && fread(&seed, , sizeof(seed), f) == sizeof(seed)) {
fclose(f);
return seed;
}
LOG(INFO) << "System entropy source not available, "
"using fallback algorithm to generate seed instead.";
if (f)
fclose(f);
pid = getpid();
s = time(NULL);
seed = std::abs(((s * ) * ((pid - ) * )) % );//时间加进程号，再加一堆混乱的数？？？？
return seed;
}
2、随机数产生
Caffe内由RNG类，RNG类有Generator类
实际是boost::mt19937。
class Caffe::RNG::Generator {
public:
Generator() : rng_(new caffe::rng_t(cluster_seedgen())) {}//typedef boost::mt19937 rng_t;
explicit Generator(unsigned int seed) : rng_(new caffe::rng_t(seed)) {}
caffe::rng_t* rng() { return rng_.get(); }
private:
shared_ptr<caffe::rng_t> rng_;
};
(3)GPU设备管理
这里主要是cuda API
void Caffe::SetDevice(const int device_id) {
int current_device;
CUDA_CHECK(cudaGetDevice(¤t_device));//current_device表示现在GPU的个数
if (current_device == device_id) {
return;//从0标志ID的，所以current_device<device_id
}
//
CUDA_CHECK(cudaSetDevice(device_id));
if (Get().cublas_handle_) CUBLAS_CHECK(cublasDestroy(Get().cublas_handle_));
if (Get().curand_generator_) {
CURAND_CHECK(curandDestroyGenerator(Get().curand_generator_));
}
CUBLAS_CHECK(cublasCreate(&Get().cublas_handle_));
CURAND_CHECK(curandCreateGenerator(&Get().curand_generator_,
CURAND_RNG_PSEUDO_DEFAULT));
CURAND_CHECK(curandSetPseudoRandomGeneratorSeed(Get().curand_generator_,
cluster_seedgen()));
}
void Caffe::DeviceQuery() {
cudaDeviceProp prop;//GPU property
int device;
if (cudaSuccess != cudaGetDevice(&device)) {
printf("No cuda device present.\n");
return;
}
CUDA_CHECK(cudaGetDeviceProperties(&prop, device));
LOG(INFO) << "Device id: " << device;//设备ID
LOG(INFO) << "Major revision number: " << prop.major;
LOG(INFO) << "Minor revision number: " << prop.minor;
LOG(INFO) << "Name: " << prop.name;
LOG(INFO) << "Total global memory: " << prop.totalGlobalMem;//全局内存大小
LOG(INFO) << "Total shared memory per block: " << prop.sharedMemPerBlock;//每个block的共享内存的大小
LOG(INFO) << "Total registers per block: " << prop.regsPerBlock;//每个block的寄存器数量
LOG(INFO) << "Warp size: " << prop.warpSize;//束的大小
LOG(INFO) << "Maximum memory pitch: " << prop.memPitch;//pitch用于二位数组
LOG(INFO) << "Maximum threads per block: " << prop.maxThreadsPerBlock;//，每个block的线程最大数
LOG(INFO) << "Maximum dimension of block: "
<< prop.maxThreadsDim[] << ", " << prop.maxThreadsDim[] << ", "
<< prop.maxThreadsDim[];//线程的维度
LOG(INFO) << "Maximum dimension of grid: "
<< prop.maxGridSize[] << ", " << prop.maxGridSize[] << ", "
<< prop.maxGridSize[];//block的维度
LOG(INFO) << "Clock rate: " << prop.clockRate;
LOG(INFO) << "Total constant memory: " << prop.totalConstMem;//最大常量内存
LOG(INFO) << "Texture alignment: " << prop.textureAlignment;//纹理？？？？
LOG(INFO) << "Concurrent copy and execution: "
<< (prop.deviceOverlap ? "Yes" : "No");
LOG(INFO) << "Number of multiprocessors: " << prop.multiProcessorCount;//sm的数量
LOG(INFO) << "Kernel execution timeout: "
<< (prop.kernelExecTimeoutEnabled ? "Yes" : "No");
return;
}
bool Caffe::CheckDevice(const int device_id) {
//cudaSetdevice只是显示id，并没有创建相应的上下文，不保险。
//
// In a shared environment where the devices are set to EXCLUSIVE_PROCESS
// or EXCLUSIVE_THREAD mode, cudaSetDevice() returns cudaSuccess
// even if the device is exclusively occupied by another process or thread.
// Cuda operations that initialize the context are needed to check
// the permission. cudaFree(0) is one of those with no side effect,
// except the context initialization.
bool r = ((cudaSuccess == cudaSetDevice(device_id)) &&
(cudaSuccess == cudaFree()));
// reset any error that may have occurred.
cudaGetLastError();
return r;
}
int Caffe::FindDevice(const int start_id) {
// This function finds the first available device by checking devices with
// ordinal from start_id to the highest available value. In the
// EXCLUSIVE_PROCESS or EXCLUSIVE_THREAD mode, if it succeeds, it also
// claims the device due to the initialization of the context.
int count = ;
CUDA_CHECK(cudaGetDeviceCount(&count));
for (int i = start_id; i < count; i++) {
if (CheckDevice(i)) return i;
}
return -;
}
三、总结
全局资源的管理，随机数、设备信息、并行训练信息
![tx2安装caffe总结[图]](data:image/gif;base64,R0lGODlhAQABAIAAAP///wAAACwAAAAAAQABAAACAkQBADs=)