在图像处理等应用中，我们经常需要对矩阵，大数量STL对象进行遍历操作，因此并行化对算法的加速也非常重要。
除了使用opencv提供的**parallel_for_**函数可对普通STL容器进行并行遍历，如vector。
参见 https://blog.csdn.net/weixin_41469272/article/details/126617752
本文介绍其他两种并行办法。 TBB和OMP。

OMP parallel

OpenMP安装

sudo apt install libomp-dev

OpenMP示例

1) OMP Hello World

OMP是相对使用较为简洁的并行工具，仅需在需要并行的语句前加入#pragma omp parallel，便可实现并行。

      #pragma omp parallel
      {
         每个线程都会执行大括号里的代码
      }

说明：以下出现c++代码c的写法
参考：https://blog.csdn.net/ab0902cd/article/details/108770396
https://blog.csdn.net/zhongkejingwang/article/details/40350027
omp_test.cpp

#include <omp.h>

int main(){
    printf("The output:\n");
    #pragma omp parallel     /* define multi-thread section */
    {
        printf("Hello World\n");
    }
    /* Resume Serial section*/
    printf("Done\n");
}

g++ omp_test.cpp -fopenmp -o omptest
./test

Result:

The output:
Hello World
Hello World
Hello World
Hello World
Hello World
Hello World
Hello World
Hello World
Done

2) OMP for 并行

当需要将for循环并行，则可在for语句之前加上：#pragma omp parallel for

int main(int argc, char *argv[]) {
  int length = 6;
  float *buf = new float[length];
  #pragma omp parallel for num_threads(3)
     for(int i = 0; i < length; i++) {
       int tid = omp_get_thread_num();

       printf("i:%d is handled on thread %d\n", i, tid);
       buf[i] = i;
     }
}

其中num_threads用于指定线程个数。
Result

i:0 is handled on thread 0
i:1 is handled on thread 0
i:4 is handled on thread 2
i:5 is handled on thread 2
i:2 is handled on thread 1
i:3 is handled on thread 1

3. OMP 官方示例

#include <stdlib.h>   //malloc and free
#include <stdio.h>    //printf
#include <omp.h>      //OpenMP

// Very small values for this simple illustrative example
#define ARRAY_SIZE 8     //Size of arrays whose elements will be added together.
#define NUM_THREADS 4    //Number of threads to use for vector addition.

/*
 *  Classic vector addition using openMP default data decomposition.
 *
 *  Compile using gcc like this:
 *      gcc -o va-omp-simple VA-OMP-simple.c -fopenmp
 *
 *  Execute:
 *      ./va-omp-simple
 */
int main (int argc, char *argv[])
{
    // elements of arrays a and b will be added
    // and placed in array c
    int * a;
    int * b;
    int * c;

    int n = ARRAY_SIZE;                 // number of array elements
    int n_per_thread;                   // elements per thread
    int total_threads = NUM_THREADS;    // number of threads to use  
    int i;       // loop index

        // allocate spce for the arrays
    a = (int *) malloc(sizeof(int)*n);
    b = (int *) malloc(sizeof(int)*n);
    c = (int *) malloc(sizeof(int)*n);

        // initialize arrays a and b with consecutive integer values
    // as a simple example
        for(i=0; i<n; i++) {
            a[i] = i;
        }
        for(i=0; i<n; i++) {
            b[i] = i;
        }

    // Additional work to set the number of threads.
    // We hard-code to 4 for illustration purposes only.
    omp_set_num_threads(total_threads);

    // determine how many elements each process will work on
    n_per_thread = n/total_threads;

        // Compute the vector addition
    // Here is where the 4 threads are specifically 'forked' to
    // execute in parallel. This is directed by the pragma and
    // thread forking is compiled into the resulting exacutable.
    // Here we use a 'static schedule' so each thread works on  
    // a 2-element chunk of the original 8-element arrays.
    #pragma omp parallel for shared(a, b, c) private(i) schedule(static, n_per_thread)
        for(i=0; i<n; i++) {
        c[i] = a[i]+b[i];
        // Which thread am I? Show who works on what for this samll example
        printf("Thread %d works on element%d\n", omp_get_thread_num(), i);
        }

    // Check for correctness (only plausible for small vector size)
    // A test we would eventually leave out
    printf("i\ta[i]\t+\tb[i]\t=\tc[i]\n");
        for(i=0; i<n; i++) {
        printf("%d\t%d\t\t%d\t\t%d\n", i, a[i], b[i], c[i]);
        }

        // clean up memory
        free(a);  free(b); free(c);

    return 0;
}

Result:

其中，shared括号中说明所有线程公用的变量名，private括号中的变量为各个线程均独立的变量。
schedule()用于指定循环的线程分布策略，默认为static。
具体不同：
schedule(kind [, chunk_size])

kind:
• static: Iterations are divided into chunks of size chunk_size. Chunks are assigned to threads in the team in round-robin fashion in order of thread number.
• dynamic: Each thread executes a chunk of iterations then requests another chunk until no chunks remain to be distributed.
• guided: Each thread executes a chunk of iterations then requests another chunk until no chunks remain to be assigned. The chunk sizes start large and shrink to the indicated chunk_size as chunks are scheduled.
• auto: The decision regarding scheduling is delegated to the compiler and/or runtime system.
• runtime: The schedule and chunk size are taken from the run-sched-var ICV

static: OpenMP会给每个线程分配chunk_size次迭代计算。这个分配是静态的，线程分配规则根据for的遍历的顺序。
dynamic：动态调度迭代的分配是依赖于运行状态进行动态确定的，当需要分配新线程时，已有线程结束，则直接使用完成的线程，而不开辟新的线程。
guided：循环迭代划分成块的大小与未分配迭代次数除以线程数成比例，然后随着循环迭代的分配，块大小会减小为chunk值。chunk的默认值为1。开始比较大，以后逐渐减小。
runtime：将调度决策推迟到指定时开始，这选项不能指定块大小?（暂未测试）

参考：
https://blog.csdn.net/gengshenghong/article/details/7000979
https://blog.csdn.net/yiguagua/article/details/107053043

4) map使用OMP遍历

关于invalid controlling predicate的问题
OMP不支持终止条件为“!=”或者“==”的for循环，因为无法判断循环的数量。

int main()
{
  map<int,int> mii;
  map<int, string> mis;
  for (int i = 0; i < 50; i++) {mis[i] = to_string(i);}

  clock_t start,end;
  start = clock();

#if 1
  mutex m;
  auto it = mis.begin();
  #pragma omp parallel for num_threads(3) shared(it)
  //Error '!=" can not be used in omp: invalid controlling predicate
  for (int i = 0; i < mis.size(); i++)
  {
    int tid = omp_get_thread_num();
    m.lock();
    mii[it->first] = atoi(it->second.c_str());
    cout << "Thread " << tid << " handle " << it->first << endl;
    m.unlock();
    it++;
  }

#else
  for (auto it : mis)
  {
    int tid = omp_get_thread_num();
    mii[it.first] = atoi(it.second.c_str());
    cout << "Thread " << tid << " handle " << it.first << endl;
  }
#endif

  end = clock();
  cout<<"time = "<<double(end-start)/CLOCKS_PER_SEC<<"s"<<endl;


  for (auto it = mii.begin(); it != mii.end(); it++)
  {
    cout << "it->first: " << it->first << " it->second: " << it->second << endl;
  }
}

Result：

加OMP：time = 0.000877s
不加并行：time = 0.001862s

TBB的安装和使用

关于Intel的oneTBB工具与g++版本相互制约，因此在安装时较为麻烦
以下测试选择的工具版本：
TBB：v2020.0
Gcc：9.4

Gcc9的安装

sudo add-apt-repository ppa:ubuntu-toolchain-r/test
sudo apt-get update
sudo apt-get install gcc-9 g++-9

TBB 安装

wget https://github.com/oneapi-src/oneTBB/archive/refs/tags/v2020.0.tar.gz
tar zxvf v2020.0.tar.gz
cd oneTBB

cp build/linux.gcc.inc build/linux.gcc-9.inc
修改 build/linux.gcc-9.inc 15,16 行:
CPLUS ?= g++-9
CONLY ?= gcc-9 

#build
make compiler=gcc-9 stdver=c++17 -j20 DESTDIR=install tbb_build_prefix=build

#***************************** TBB examples build *****************************************
#build test code:
g++-9 -std=c++17  -I ~/Download/softpackages/oneTBB/install/include/ -L/home/niebaozhen/Download/    softpackages/oneTBB/install/lib/ std_for_each.cpp -ltbb -Wl,-rpath=/home/niebaozhen/Download/soft    packages/oneTBB/install/lib/

参考链接：https://blog.csdn.net/weixin_32207065/article/details/112270765

Tips:
当TBB版本大于v2021.1.1时，cmake被引入，但是该版本TBB不支持gcc9/10
但是gcc版本高等于9时，才支持对TBB的使用，且编译标准建议为c++17。
说明链接
v2021.1.1版本编译命令

#tbb version >= v2021.1.1: cmake employed, however,
#libc++9&10 are incompatible with TBB version > 2021.xx

mkdir build install
cd build
cmake -DCMAKE_INSTALL_PREFIX=../install/ -DCMAKE_CXX_STANDARD=17 -DCMAKE_CXX_COMPILER=/usr/bin/g++-9  -DTBB_TEST=OFF ..cmake -DCMAKE_INSTALL_PREFIX=../install/  -DTBB_TEST=OFF ..
make -j30
make install

TBB使用

std_for_each.cpp

#include <iostream>
#include <unistd.h>
#include <map>
#include <algorithm>
#include <chrono>

#define __MUTEX__ 0
#if __MUTEX__
#include <mutex>
#endif

#if __GNUC__ >= 9
#include <execution>
#endif

using namespace std; 


int main ()
{
  //cout << "gnu version: " << __GNUC__ << endl;
  int a[] = {0,1,3,4,5,6,7,8,9};
  
  map<int, int> mii;
  #if __MUTEX__
  mutex m;
  #endif  
 
  auto tt1 = chrono::steady_clock::now();

  #if __GNUC__ >= 9
  for_each(execution::par, begin(a), std::end(a), [&](int i) {
  #else
  for_each(begin(a), std::end(a), [&](int i) {
  #endif
    #if __MUTEX__
    lock_guard<mutex> guard(m);
    #endif
    mii[i] = i*2+1;
    //sleep(1);
    //cout << "Sleep one second" << endl;
  }); 

  auto tt2 = chrono::steady_clock::now();
  auto dt = chrono::duration_cast<chrono::duration<double>>(tt2 - tt1);

  cout << "time = " << dt.count() << "s" <<endl;

  for(auto it = mii.begin(); it != mii.end(); it++) {
    cout << "mii[" << it->first << "] = " << it->second << endl;
  }   
}

build:

g++ std_for_each.cpp
或：
g++-9 -std=c++17  -I ~/Download/softpackages/oneTBB/install/include/ -L/home/niebaozhen/Download/softpackages/oneTBB/install/lib/ std_for_each.cpp -ltbb -Wl,-rpath=/home/niebaozhen/Download/softpackages/oneTBB/install/lib/

Result：

可以看出，当遍历所操作的工作比较少时，并行反而会带来更多的耗时。
当遍历的操作较多，这里sleep来模拟较多的工作，并行体现出优势。
总之：