How to Implement Parallelism in Python: Unleashing Multi-Core Power?

Question: Embracing Parallelism in Python

In C , OpenMP provides robust support for parallel programming. However, Python lacks this convenient tool. How do we achieve parallelism in Python programs, particularly in scenarios where independent functions require parallelization?

Consider the following code structure:

<code class="python">solve1(A)
solve2(B)</code>

Where solve1 and solve2 are independent functions. The aim is to execute this code in parallel, minimizing execution time.

Let's explore the provided code snippet:

<code class="python">def solve(Q, G, n):
    i = 0
    tol = 10 ** -4

    while i < 1000:
        inneropt, partition, x = setinner(Q, G, n)
        outeropt = setouter(Q, G, n)

        if (outeropt - inneropt) / (1 + abs(outeropt) + abs(inneropt)) < tol:
            break
            
        node1 = partition[0]
        node2 = partition[1]
    
        G = updateGraph(G, node1, node2)

        if i == 999:
            print "Maximum iteration reaches"
    print inneropt</code>

We aim to parallelize the setinner and setouter functions.

Answer: Unleashing Parallel Power with Multiprocessing Module

The multiprocessing module offers a powerful solution for parallel programming in Python. It allows us to spawn multiple processes that can execute tasks concurrently, utilizing the processing power of multiple CPU cores.

For the provided code, a processing pool can be employed. Here's how it's done:

<code class="python">from multiprocessing import Pool

pool = Pool()
result1 = pool.apply_async(solve1, [A])    # evaluate "solve1(A)" asynchronously
result2 = pool.apply_async(solve2, [B])    # evaluate "solve2(B)" asynchronously
answer1 = result1.get(timeout=10)
answer2 = result2.get(timeout=10)</code>

This code snippet creates a pool of processes that will execute the solve1 and solve2 functions asynchronously. Each CPU core can execute one process simultaneously, effectively reducing the execution time.

An alternative way to parallelize tasks is by using the map function:

<code class="python">args = [A, B]
results = pool.map(solve1, args)</code>

This approach applies the solve1 function to each element in the args list in parallel.

However, it's important to note that threads should not be used for parallel programming in Python. This is because the GIL (Global Interpreter Lock) prevents multiple threads from executing Python bytecode concurrently, essentially negating any potential benefits of parallelism.

The above is the detailed content of How to Implement Parallelism in Python: Unleashing Multi-Core Power?. For more information, please follow other related articles on the PHP Chinese website!