When to Use Parallelism vs. Concurrency: Key Differences, Use Cases, and Examples

As software developers work on performance optimization, two concepts often come into play: concurrency and parallelism. While they may seem similar, they serve distinct purposes and are suited to different types of tasks. In this article, we'll explore when to use parallelism and when to use concurrency, along with real-world examples and best practices.

Understanding Concurrency and Parallelism

What is Concurrency?

Concurrency involves managing multiple tasks that may not run simultaneously but can progress in overlapping time frames. This approach is useful for I/O-bound tasks — those that spend much time waiting for resources, like network responses or file I/O. Concurrency gives the illusion of multitasking by interleaving tasks rather than running them at the exact same time.

For instance, in a web server handling multiple client requests, while one request waits for a database response, another request can be processed. By handling tasks in this interleaved manner, concurrency optimizes time, allowing for higher throughput without waiting for each task to complete in sequence.

What is Parallelism?

Parallelism, on the other hand, is about executing multiple tasks simultaneously. This requires multiple CPU cores, as each task runs on its own core or processor. Parallelism is ideal for CPU-bound tasks — those that demand significant computational power — because it allows true simultaneous execution.

For example, video rendering software often splits frames across multiple CPU cores, rendering multiple frames at once to speed up the overall process. Parallelism effectively divides and conquers these heavy computational loads by using every available processor.

Key Differences Between Concurrency and Parallelism

When to Use Concurrency: Best Use Cases and Examples

Concurrency is best suited for tasks where waiting times are inevitable, such as tasks that depend on network or file I/O operations. Here are some practical examples:

1. Downloading Files from the Internet

When a script needs to download multiple files, waiting for each file to download sequentially would be inefficient. By employing concurrency with Python's ThreadPoolExecutor, you can initiate multiple downloads, allowing them to proceed simultaneously. This approach ensures that while one file is downloading, others can also progress without waiting for one download to finish.

Example Code:

from concurrent.futures import ThreadPoolExecutor
import requests

def download_file(url):
    response = requests.get(url)
    # Save file or process response

urls = ["https://example.com/file1", "https://example.com/file2"]

with ThreadPoolExecutor(max_workers=5) as executor:
    executor.map(download_file, urls)

2. Handling Multiple Database Requests

A web application might need to retrieve data from a database for various user requests. Handling these requests concurrently allows the application to manage multiple database connections effectively. This prevents blocking and keeps the server responsive, especially during high-traffic periods.

Example Code:

from concurrent.futures import ThreadPoolExecutor
import psycopg2

def query_database(query):
    conn = psycopg2.connect("your_database_url")
    cursor = conn.cursor()
    cursor.execute(query)
    return cursor.fetchall()

queries = ["SELECT * FROM users", "SELECT * FROM orders"]

with ThreadPoolExecutor(max_workers=5) as executor:
    results = list(executor.map(query_database, queries))

3. Web Scraping Multiple Pages

Web scraping involves fetching data from multiple web pages, where each page might take time to load. Concurrency allows scraping from different pages in an overlapping manner, drastically reducing the total time taken compared to sequential scraping.

When to Use Parallelism: Best Use Cases and Examples

Parallelism is ideal for CPU-intensive tasks that demand significant computational power. Here are some examples:

1. Image Processing and Transformations

Image processing tasks, such as resizing or filtering a large batch of images, are CPU-bound. Using ProcessPoolExecutor, each image can be processed on a separate CPU core, improving performance by utilizing multiple cores simultaneously.

Example Code:

from concurrent.futures import ProcessPoolExecutor
from PIL import Image

def process_image(image_path):
    img = Image.open(image_path)
    img = img.resize((300, 300))  # Resize image
    img.save("resized_" + image_path)

image_paths = ["image1.jpg", "image2.jpg"]

with ProcessPoolExecutor(max_workers=4) as executor:
    executor.map(process_image, image_paths)

2. Scientific Computations

In scientific computations, such as calculating fractals, simulations, or statistical models, each computation can be performed independently on separate cores. Parallel processing allows these calculations to run truly simultaneously, significantly speeding up the total execution time.

Example Code:

from concurrent.futures import ProcessPoolExecutor

def complex_calculation(n):
    return sum(i * i for i in range(n))

numbers = [10**6, 10**6, 10**6]

with ProcessPoolExecutor(max_workers=3) as executor:
    results = list(executor.map(complex_calculation, numbers))

3. Machine Learning Model Training

Training machine learning models often involves large datasets and multiple epochs. Parallelism can help distribute different training batches across multiple cores or even GPUs, speeding up the model training process.

How to Decide Between Concurrency and Parallelism

The choice between concurrency and parallelism largely depends on the type of task:

• Choose Concurrency for I/O-bound tasks like downloading files, handling network requests, and database operations.
• Choose Parallelism for CPU-bound tasks like image processing, scientific computations, and machine learning model training.

Summary Table

Conclusion

Understanding the difference between concurrency and parallelism is essential for optimizing your software's performance. Use concurrency when tasks spend time waiting for external resources, and turn to parallelism when your tasks need computational power across multiple cores. Both techniques, when applied correctly, can enhance your application's efficiency and user experience.