如何：编写 parallel_for 循环
本示例演示如何使用 concurrency::parallel_for 来计算两个矩阵的产品。
示例
下面的示例演示 matrix_multiply 函数，该函数计算两个方形矩阵的乘积。
// Computes the product of two square matrices.
void matrix_multiply(double** m1, double** m2, double** result, size_t size)
{
for (size_t i = 0; i < size; i++)
{
for (size_t j = 0; j < size; j++)
{
double temp = 0;
for (int k = 0; k < size; k++)
{
temp += m1[i][k] * m2[k][j];
}
result[i][j] = temp;
}
}
}
下面的示例演示 parallel_matrix_multiply 函数，该函数使用 parallel_for 算法并行执行外层循环。
// Computes the product of two square matrices in parallel.
void parallel_matrix_multiply(double** m1, double** m2, double** result, size_t size)
{
parallel_for (size_t(0), size, [&](size_t i)
{
for (size_t j = 0; j < size; j++)
{
double temp = 0;
for (int k = 0; k < size; k++)
{
temp += m1[i][k] * m2[k][j];
}
result[i][j] = temp;
}
});
}
此示例仅并行化外层循环，这是因为该循环执行的工作足够多，可以从并行处理的开销中受益。 如果并行化内层循环，则将不会获得性能上的提升，这是因为内层循环执行的少量工作不能抵消并行处理的开销。 因此，仅并行化外部循环是在大多数系统上最大程度地发挥并发优势的最佳方式。
以下更为完整的示例将比较 matrix_multiply 函数与 parallel_matrix_multiply 函数的性能。
// parallel-matrix-multiply.cpp
// compile with: /EHsc
#include <windows.h>
#include <ppl.h>
#include <iostream>
#include <random>
using namespace concurrency;
using namespace std;
// Calls the provided work function and returns the number of milliseconds
// that it takes to call that function.
template <class Function>
__int64 time_call(Function&& f)
{
__int64 begin = GetTickCount();
f();
return GetTickCount() - begin;
}
// Creates a square matrix with the given number of rows and columns.
double** create_matrix(size_t size);
// Frees the memory that was allocated for the given square matrix.
void destroy_matrix(double** m, size_t size);
// Initializes the given square matrix with values that are generated
// by the given generator function.
template <class Generator>
double** initialize_matrix(double** m, size_t size, Generator& gen);
// Computes the product of two square matrices.
void matrix_multiply(double** m1, double** m2, double** result, size_t size)
{
for (size_t i = 0; i < size; i++)
{
for (size_t j = 0; j < size; j++)
{
double temp = 0;
for (int k = 0; k < size; k++)
{
temp += m1[i][k] * m2[k][j];
}
result[i][j] = temp;
}
}
}
// Computes the product of two square matrices in parallel.
void parallel_matrix_multiply(double** m1, double** m2, double** result, size_t size)
{
parallel_for (size_t(0), size, [&](size_t i)
{
for (size_t j = 0; j < size; j++)
{
double temp = 0;
for (int k = 0; k < size; k++)
{
temp += m1[i][k] * m2[k][j];
}
result[i][j] = temp;
}
});
}
int wmain()
{
// The number of rows and columns in each matrix.
// TODO: Change this value to experiment with serial
// versus parallel performance.
const size_t size = 750;
// Create a random number generator.
mt19937 gen(42);
// Create and initialize the input matrices and the matrix that
// holds the result.
double** m1 = initialize_matrix(create_matrix(size), size, gen);
double** m2 = initialize_matrix(create_matrix(size), size, gen);
double** result = create_matrix(size);
// Print to the console the time it takes to multiply the
// matrices serially.
wcout << L"serial: " << time_call([&] {
matrix_multiply(m1, m2, result, size);
}) << endl;
// Print to the console the time it takes to multiply the
// matrices in parallel.
wcout << L"parallel: " << time_call([&] {
parallel_matrix_multiply(m1, m2, result, size);
}) << endl;
// Free the memory that was allocated for the matrices.
destroy_matrix(m1, size);
destroy_matrix(m2, size);
destroy_matrix(result, size);
}
// Creates a square matrix with the given number of rows and columns.
double** create_matrix(size_t size)
{
double** m = new double*[size];
for (size_t i = 0; i < size; ++i)
{
m[i] = new double[size];
}
return m;
}
// Frees the memory that was allocated for the given square matrix.
void destroy_matrix(double** m, size_t size)
{
for (size_t i = 0; i < size; ++i)
{
delete[] m[i];
}
delete m;
}
// Initializes the given square matrix with values that are generated
// by the given generator function.
template <class Generator>
double** initialize_matrix(double** m, size_t size, Generator& gen)
{
for (size_t i = 0; i < size; ++i)
{
for (size_t j = 0; j < size; ++j)
{
m[i][j] = static_cast<double>(gen());
}
}
return m;
}
下例是四处理器计算机的输出结果。
serial: 3853
parallel: 1311
编译代码
若要编译代码，将其复制然后将其粘贴在 Visual Studio 项目中，或将它粘贴到名为的文件并行的矩阵-multiply.cpp ，然后在 Visual Studio 命令提示符窗口中运行以下命令。
cl.exe /EHsc parallel-matrix-multiply.cpp