Concurrency vs. Parallelism – Definition and Differences

Discover the differences between concurrency and parallelism, and learn which is best for your software development needs.
11 min read
Concurrency vs Parallelism blog image

This blog will discuss concurrency and parallelism in detail to help you choose the best concept for your application.

What is Concurrency?

In simple terms, concurrency is a concept used in software development to handle multiple tasks simultaneously. However, in theory, it does not run all the tasks at the exact time. Instead, it allows the system or application to manage multiple tasks simultaneously by rapidly switching between them, creating an illusion of parallel processing. This process is also known as task interleaving.

For example, consider a web server that needs to handle multiple user requests.

  • User 1 sends a request to the server to retrieve data.
  • User 2 sends a request to the server to upload a file.
  • User 3 sends a request to the server to retrieve images.

Without concurrency, each user must wait until the previous request is fulfilled.

  • Step 1: CPU starts processing the data retrieval request in thread 1.
  • Step 2: While thread 1 waits for the result, the CPU starts the file uploading process in thread 2.
  • Step 3: While thread 2 waits for the file to be uploaded, the CPU starts image retrieval in thread 3.
  • Step 4: Then, the CPU switches between these 3 threads based on resource availability to complete all 3 tasks simultaneously.
Example of 3 tasks running concurrently

Compared to the synchronous execution approach, the concurrency approach is much faster and extremely useful for single-core environments to enhance the overall system’s response time, resource utilization, and system throughput capabilities. However, concurrency is not limited to single-core; it can also be implemented in multi-core environments.

Use Cases of Concurrency

  • Responsive user interfaces.
  • Web servers.
  • Real-time systems.
  • Networking & I/O operations.
  • Background processing.

Different Concurrency Models

With the increasing complexity and demands of modern applications, developers have introduced new concurrency models to address the shortcomings of the traditional approach. Here are some key concurrency models and their uses:

1. Cooperative Multitasking

In this model, tasks voluntarily give up control to the scheduler at appropriate points, allowing it to process other tasks. This yielding often happens when the task is idle or waiting for I/O operations. This is one of the easiest models to implement since the context switching is managed within the application code.


  • Lightweight embedded systems
  • Early versions of Microsoft Windows (Windows 3.x)
  • Classic Mac OS

Real-World Applications:

2. Preemptive Multitasking

The operating system or runtime scheduler forces the tasks to stop and allocates CPU time to other tasks based on a scheduling algorithm. This model ensures that all tasks get an equal share of CPU time. However, it requires more complex context switching.


Real-World Applications:

  • Modern operating systems (Windows, macOS, Linux)
  • Web servers.

3. Event-Driven Concurrency

In this model, the tasks are divided into small non-blocking operations and enqueued in a queue. Then, they get jobs from the queue, perform the required action, and move to the next one, keeping the system interactive.


  • Node.js (JavaScript runtime).
  • JavaScript’s async/await pattern.
  • Python’s asyncio library.

Real-World Applications:

  • Web servers like Node.js.
  • Real-time chat applications.

4. Actor Model

Uses actors to send and receive messages asynchronously. Each actor processes one message at a time, avoiding shared state and reducing the need for locks.


Real-World Applications:

  • Distributed systems.
  • Telecommunications systems.
  • Real-time data processing systems.

5. Reactive Programming

This model allows you to create data streams (observables) and define how they should be processed (operators) and reacted to (observers). Data changes or events occur, which automatically propagate through the streams to all subscribed observers. This approach makes managing asynchronous data and events easier, providing a clean and declarative way to handle complex data flows.


Real-World Applications:

  • Real-time data processing pipelines.
  • Interactive user interfaces.
  • Applications requiring dynamic and responsive data handling.

What is Parallelism?

Parallelism is another popular concept used in software development to handle multiple tasks simultaneously. Unlike concurrency, which creates the illusion of parallel processing by rapidly switching between tasks, parallelism actually executes multiple tasks simultaneously using multiple CPU cores or processors. It involves breaking larger tasks into smaller, independent subtasks that can be executed in parallel. This process is known as task decomposition.

For example, consider a data processing application that generates reports after performing analyses and running simulations. Without parallelism, this will run as one large task, taking significant time to complete. But, if you opt for parallelism, it will complete the task much quicker through task decomposition.

Here is how parallelism works:

  • Step 1: Divide the main task into independent subtasks. These subtasks should be able to run without waiting for inputs from other tasks. However, if there are any dependencies, you need to schedule them accordingly to ensure they are executed in the correct order. In this example, I will assume no dependencies between subtasks.
  • Subtask 1: Performing data analysis.
  • Subtask 2: Generating reports.
  • Subtask 3: Running simulations.
  • Step 2: Assign 3 subtasks to 3 cores.
  • Step 3: Finally, combine results from each sub-task to get the final output of the original task.
Example of 3 tasks running in parallel

Use Cases of Parallelism

  • Scientific computations and simulations.
  • Data processing.
  • Image processing.
  • Machine learning.
  • Risk analysis.

Different Parallelism Models

Similar to concurrency, parallelism also has several different models to utilize multi-core processors efficiently and distributed computing resources. Here are some key parallelism models and their uses:

1. Data Parallelism

This model distributes data across multiple processors and performs the same operation on each data subset simultaneously. It is particularly effective for tasks that can be easily divided into independent sub-tasks.


  • SIMD (Single Instruction, Multiple Data) operations.
  • Parallel array processing.
  • MapReduce framework.

Real-World Applications:

  • Image and signal processing
  • Large-scale data analysis
  • Scientific simulations

2. Task Parallelism

Task parallelism involves dividing the overall task into smaller, independent tasks that can be executed concurrently on different processors. Each task performs a different operation.


  • Thread-based parallelism in Java.
  • Parallel Tasks in .NET.
  • POSIX threads.

Real-World Applications:

  • Web servers handling multiple client requests.
  • Parallel algorithm implementations.
  • Real-time processing systems.

3. Pipeline Parallelism

In pipeline parallelism, tasks are divided into stages, and each stage is processed in parallel. Data flows through the pipeline, with each stage operating concurrently.


  • Unix pipeline commands.
  • Image processing pipelines.
  • Data processing pipelines in ETL (Extract, Transform, Load) tools.

Real-World Applications:

  • Video and audio processing.
  • Real-time data streaming applications.
  • Manufacturing and assembly line automation.

4. Fork/Join Model

This model involves breaking a task into smaller sub-tasks (forking), executing them in parallel, and then combining the results (joining). It is useful for divide-and-conquer algorithms.


  • Fork/Join framework in Java.
  • Parallel recursive algorithms (e.g., parallel mergesort).
  • Intel Threading Building Blocks (TBB).

Real-World Applications:

  • Complex computational tasks like sorting large datasets.
  • Recursive algorithms.
  • Large-scale scientific computations.

5. GPU Parallelism

GPU parallelism leverages the massively parallel processing capabilities of Graphics Processing Units (GPUs) to execute thousands of threads simultaneously, making it ideal for highly parallel tasks.


  • CUDA (Compute Unified Device Architecture) by NVIDIA.
  • OpenCL (Open Computing Language).
  • TensorFlow for deep learning.

Real-World Applications:

  • Machine learning and deep learning.
  • Real-time graphics rendering.
  • High-performance scientific computing.

Concurrency vs. Parallelism

Since you now have a good understanding of how concurrency and parallelism work, let’s compare them in several aspects to see how we can get the best out of both.

1. Resource Utilization

  • Concurrency: Runs multiple tasks within a single core, sharing resources between tasks. For example, the CPU switches between tasks during idle or waiting periods.
  • Parallelism: Use multiple cores or processors to execute tasks simultaneously.

2. Focus

  • Concurrency: Focuses on managing multiple tasks at the same time.
  • Parallelism: Focuses on executing multiple tasks at the same time.

3. Task Execution

  • Concurrency: Tasks executed in an interleaved manner. The rapid context switching of the CPU creates an illusion of parallel execution.
  • Parallelism: Tasks are executed in a true parallel nature on different processors or cores.

4. Context Switching

  • Concurrency: Frequent context switching occurs when the CPU switches between tasks to give the appearance of simultaneous execution. Sometimes, this can negatively affect performance if tasks frequently become idle.
  • Parallelism: Minimal or no context switching since tasks run on separate cores or processors.

5. Use cases

  • Concurrency: I/O-bound tasks like disk I/O, network communication, or user input.
  • Parallelism: CPU-bound tasks that require intensive processing like mathematical computations, data analysis, and image processing.
A table explaining and comparing the differences between concurrency and parallelism

Can We Use Concurrency and Parallelism Together?

Based on the above comparison, we can notice that concurrency and parallelism complement each other in many situations. But before getting into real-world examples, let’s see how this combination works under the hood in a multi-core environment. For that, let’s consider a web server that performs data reading, writing, and analysis.

Step 1: Identifying Tasks

First, you need to identify the I/O-bound tasks and the CPU-bound tasks in your application. In this case:

  • I/O bound – Data reading and writing.
  • CPU bound – Data analysis.

Step 2: Concurrency Execution

Data reading and writing tasks can be executed in separate threads within a single core since they are I/O-bound tasks. The server uses an event loop to manage these tasks and quickly switches between threads, interleaving the task’s execution. You can use an asynchronous programming library like Python asyncio to implement this concurrency behaviour.

Step 3: Parallel Execution

Multiple cores can be assigned to CPU-bound tasks to handle them in parallel. In this case, data analysis can be divided into multiple subtasks and will execute each subtask in an independent core. You can use a parallel execution framework like Python concurrent.futures to implement this behaviour.

Step 4: Synchronization and Coordination

Sometimes, threads running in different cores can depend on each other. In such situations, synchronization mechanisms like locks and semaphores are needed to ensure data integrity and avoid race conditions.

Visualization of concurrency and parallelism in multi-core processing

The code snippet below shows how to use concurrency and parallelism in the same application using Python:

import asyncio
from concurrent.futures import ProcessPoolExecutor
import os

# Simulate I/O-bound task (data reading)
async def read_data():
    await asyncio.sleep(1)  # Simulate I/O delay
    data = [1, 2, 3, 4, 5]  # Dummy data
    print("Data read completed")
    return data

# Simulate I/O-bound task (data writing)
async def write_data(data):
    await asyncio.sleep(1)  # Simulate I/O delay
    print(f"Data write completed: {data}")

# Simulate CPU-bound task (data analysis)
def analyze_data(data):
    print(f"Data analysis started on CPU: {os.getpid()}")
    result = [x ** 2 for x in data]  # Simulate computation
    print(f"Data analysis completed on CPU: {os.getpid()}")
    return result

async def handle_request():
    # Concurrency: Read data asynchronously
    data = await read_data()
    # Parallelism: Analyze data in parallel
    loop = asyncio.get_event_loop()
    with ProcessPoolExecutor() as executor:
        analyzed_data = await loop.run_in_executor(executor, analyze_data, data)
    # Concurrency: Write data asynchronously
    await write_data(analyzed_data)

async def main():
    # Simulate handling multiple requests
    await asyncio.gather(handle_request(), handle_request())

# Run the server

Real-World Examples of Combining Concurrency and Parallelism

Now, let’s discuss some common use cases where we can combine concurrency and parallelism to achieve optimal performance.

1. Financial Data Processing

The main tasks of a financial data processing system include data collection, processing, and analysis while serving day-to-day operations.

  • Concurrency is used to fetch financial data from various resources like the stock market using asynchronous I/O operations.
  • Analyzing the collected data to generate reports. This is a CPU-intense task, and parallelism is used to execute it parallel without affecting the day-to-day operations.

2. Video Processing

The main tasks of a video processing system include uploading, encoding/decoding, and analyzing video files.

  • Concurrency can be used to handle multiple video upload requests using asynchronous I/O operations. This allows users to upload videos without waiting for other uploads to complete.
  • Parallelism is used for CPU-intensive tasks like encoding, decoding, and analyzing video files.

3. Data Scraping

The main tasks of a data scraping service include fetching data from various websites and parsing/analyzing the collected data for insights.

  • Data fetching can be handled using concurrency. It ensures that data collection is efficient and does not block while waiting for responses.
  • Parallelism is used to process the collected data across multiple CPU cores. It improves the organization’s decision-making process by providing real-time reports.


Concurrency and parallelism are two key concepts used in software development to improve application performance. Concurrency allows multiple tasks to run simultaneously, while parallelism speeds up data processing by using multiple CPU cores. While they have distinct functionalities, integrating them can significantly enhance the performance of applications with both I/O-bound and CPU-bound tasks.

Bright Data’s tools, like the Web Scraper APIs, Web Scraper Functions, and Scraping Browser, are designed to fully exploit these techniques. They use asynchronous operations to collect data from multiple sources simultaneously and parallel processing to analyze and organize the data quickly. So, choosing a data provider like Bright Data, which has already integrated concurrency and parallelism into its core, can save time and effort, as you won’t need to implement these concepts from scratch while web scraping.

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