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//Description: openmp

//Create Date: 2021-05-15 12:31:38

//Author: channy

OpenMP

Visual C++ supports the OpenMP 2.0 standard.

Qt:

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QMAKE_CXXFLAGS += -fopenmp
LIBS += -fopenmp

parallel

用在一个代码段之前，表示这段代码将被多个线程并行执行

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#pragma omp parallel
    {
        std::cout << omp_get_thread_num();
    }
	
//output	
//1320

for

用于for循环之前，将循环分配到多个线程中并行执行，必须保证每次循环之间无相关性

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#pragma omp for
	for (int i = 0; i < 20; i++) {
		std::cout << i << " " << omp_get_thread_num() << std::endl;
	}

//invalid

	int a[5], i;

#pragma omp parallel
	{
		// Perform some computation.
#pragma omp for
		for (i = 0; i < 5; i++)
			a[i] = i * i;

		// Print intermediate results.
#pragma omp master
		for (i = 0; i < 5; i++)
			printf_s("a[%d] = %d\n", i, a[i]);

		// Wait.
#pragma omp barrier

// Continue with the computation.
#pragma omp for
		for (i = 0; i < 5; i++)
			a[i] += i;
	}

parallel for

也是用在一个for循环之前，表示当前层for循环的代码将被多个线程并行执行。

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#pragma omp parallel for
    for (int i = 0; i < 10; i++) {
        std::cout << i << " " << omp_get_thread_num() <<std::endl;
    }

parallel for collapse(2)

多层for循环

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	int sum = 0;
#pragma omp parallel for collapse(2)
    for (int i = 0; i < 10; i++) {
        for (int j = 0; j < 11; j++) {
            sum += i * j;
#pragma omp critical
            {
                std::cout << i << " " << j << " " << omp_get_thread_num() << std::endl;
            }
        }
    }

reduction / reduction (operator: var1, val2, …)

其中sum是共享的，采用reduction之后，每个线程根据reduction（+: sum）的声明算出自己的sum，然后再将每个线程的sum加起来。

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	int sum = 0;
#pragma omp parallel for num_threads(4) reduction(+:sum)
	for (int i = 0; i < 100; i++) {
		sum += i;
	}
	std::cout << sum << std::endl;

critical

用在一段代码临界区之前。表示同一时间内该段代码只被单个线程执行

single

只被单个线程执行一次

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	int a = 0, b = 0;
#pragma omp parallel
	{
#pragma omp single
		a++;
#pragma omp critical 
		b++;
	}

	std::cout << a << " " << b << std::endl;

atomic

原子操作，多用于++,–,etc，仅对一句有效

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# pragma omp atomic [read | write | update | capture]

master

用于指定一段代码块由主线程执行

sections / section

用在可能会被并行执行的代码段之前

parallel sections

parallel和sections两个语句的结合

flush

同步

barrier

用于并行区内代码的线程同步，所有线程执行到barrier时要停止，直到所有线程都执行到barrier时才继续往下执行。

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    int sum = 0;
#pragma omp parallel num_threads(10)
    {
#pragma omp atomic
        sum += 10;
#pragma omp barrier
        std::cout << sum << std::endl;
    }

ordered

用于指定并行区域的循环按顺序执行

private / lastprivate / fristprivate

用于指定一个变量是线程私有的。

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	int tmp = 0;
#pragma omp parallel for private(tmp) 
	for (int i = 0; i < 10; i++) {
		tmp += i;
		printf("%d %d %d\n", i, tmp, omp_get_thread_num());
	}

	std::cout << "------------------" << tmp << std::endl;

#pragma omp parallel for firstprivate(tmp)
	for (int i = 0; i < 10; i++) {
		tmp += i;
		printf("%d %d %d\n", i, tmp, omp_get_thread_num());
	}

	std::cout << "------------------" << tmp << std::endl;

#pragma omp parallel for lastprivate(tmp)
	for (int i = 0; i < 10; i++) {
		tmp += i;
		printf("%d %d %d\n", i, tmp, omp_get_thread_num());
	}

	std::cout << "------------------" << tmp << std::endl;

lock

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    int value[10] = {0};
    omp_lock_t lock[10];
#pragma omp parallel for
    for (int i = 0; i < 10; i++) {
        omp_init_lock(&lock[i]);
    }
#pragma omp parallel for
    for (int i = 0; i < 10; i++) {
        int index = rand() % 10;
        omp_set_lock(&lock[index]);
        value[index]++;
        omp_unset_lock(&lock[index]);
    }
#pragma omp parallel for
    for (int i = 0; i < 10; i++) {
        omp_destroy_lock(&lock[i]);
    }

    for (int i = 0; i < 10; i++) {
        std::cout << value[i] << " ";
    }

barrier / nowait

for循环结束后默认带barrier

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#pragma omp parallel
	{
#pragma omp for nowait
		for (int i = 0; i < 10; i++) {
			printf("+");
		}

#pragma omp for 
		for (int i = 0; i < 10; i++) {
			printf("-");
		}

		for (int i = 0; i < 10; i++) {
			printf("*");
		}
	}

task

for和sections指令的”缺陷“：无法根据运行时的环境动态的进行任务划分，必须是预先能知道的任务划分的情况。

task主要适用于不规则的循环迭代（如下面的循环）和递归的函数调用

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void mytask(int& a) {
    a++;
}

int main(int argc, char *argv[])
{
    int N = 10;
    int a[N];

    for (int i = 0; i < N; i++) {
        a[i] = 1;
    }

#pragma omp parallel num_threads(2)
    {
#pragma omp single
        {
            for (int i = 0; i < N; i = i + a[i])
            {
#pragma omp task
                mytask(a[i]);
            }
        }
    }

    for (int i = 0; i < N; i++) {
        std::cout << a[i] << std::endl;
    }

    return 0;
}

schedule

simd

向量化

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