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python - 并行处理没有提高效率？

问题描述

我正在学习 AI 并阅读有关并行处理的信息。这是我复制解决骑士之旅问题的代码来测试的代码：

# Python3 program to solve Knight Tour problem using Backtracking

# Chessboard Size
# starting time
start = time.time()
n = 8


def isSafe(x, y, board):
    '''
        A utility function to check if i,j are valid indexes
        for N*N chessboard
    '''
    if(x >= 0 and y >= 0 and x < n and y < n and board[x][y] == -1):
        return True
    return False


def printSolution(n, board):
    '''
        A utility function to print Chessboard matrix
    '''
    for i in range(n):
        for j in range(n):
            print(board[i][j], end=' ')
        print()


def solveKT(n):
    '''
        This function solves the Knight Tour problem using
        Backtracking. This function mainly uses solveKTUtil()
        to solve the problem. It returns false if no complete
        tour is possible, otherwise return true and prints the
        tour.
        Please note that there may be more than one solutions,
        this function prints one of the feasible solutions.
    '''

    # Initialization of Board matrix
    board = [[-1 for i in range(n)]for i in range(n)]

    # move_x and move_y define next move of Knight.
    # move_x is for next value of x coordinate
    # move_y is for next value of y coordinate
    move_x = [2, 1, -1, -2, -2, -1, 1, 2]
    move_y = [1, 2, 2, 1, -1, -2, -2, -1]

    # Since the Knight is initially at the first block
    board[0][0] = 0

    # Step counter for knight's position
    pos = 1

    # Checking if solution exists or not
    if(not solveKTUtil(n, board, 0, 0, move_x, move_y, pos)):
        print("Solution does not exist")
    else:
        printSolution(n, board)


def solveKTUtil(n, board, curr_x, curr_y, move_x, move_y, pos):
    '''
        A recursive utility function to solve Knight Tour
        problem
    '''

    if(pos == n**2):
        return True

    # Try all next moves from the current coordinate x, y
    for i in range(8):
        new_x = curr_x + move_x[i]
        new_y = curr_y + move_y[i]
        if(isSafe(new_x, new_y, board)):
            board[new_x][new_y] = pos
            if(solveKTUtil(n, board, new_x, new_y, move_x, move_y, pos+1)):
                return True

            # Backtracking
            board[new_x][new_y] = -1
    return False


# Driver Code
#if __name__ == "__main__":
    
    # Function Call
    #solveKT(n)
    # printing main program process id

    # creating processes

p1 = multiprocessing.Process(target=solveKT(n))
p2 = multiprocessing.Process(target=solveKT(n))

# starting processes
p1.start()
p2.start()

# wait until processes are finished
p1.join()
p2.join()
# end time
end = time.time()
# total time taken
print(f"Runtime of the program is {end - start}")
# This code is contributed by AAKASH PAL

它打印出的时间是 61.015116691589355 秒。似乎它解决了第一个问题（p1），然后解决了第二个问题。当我尝试在没有多处理的情况下解决时，所花费的时间仅为 25.836514472961426 秒。那么，多处理真的可以独立处理多任务吗？

标签： pythonmultiprocessingknights-tour

解决方案

问题在这里

p1 = multiprocessing.Process(target=solveKT(n))
p2 = multiprocessing.Process(target=solveKT(n))

函数的参数总是被急切地评估。solveKT(n)甚至在多处理过程开始之前进行评估。

您需要告诉 multiprocessing 您想要调用solveKT(n)而不实际调用solveKT(n). 您可以通过分别传递名称和参数来做到这一点：

p1 = multiprocessing.Process(target=solveKT, args=(n,))
p2 = multiprocessing.Process(target=solveKT, args=(n,))

文档：https ://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process

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