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June 07, 2022
8 minutes read

How Do Concurrency and Parallelism Differ?

If you are a software engineer, you’ve probably heard those two words many times. If not, don’t worry, we will explain the logic behind them as applying concurrency and parallelism may level up your programming journey.

Concurrency and parallelism are closely related to multitasking. And what do we usually do during trips? We keep checking the map, maintaining a conversation with passengers, thinking if we packed everything we needed, and even dealing with unexpected situations… The beauty of such multitasking is that you probably don’t notice the shifting from one task to another.

In this blog post, we’re going to cover why concurrency and parallelism are related to multitasking and the main differences between them. Fasten your seatbelt – we’re gonna lead you through this journey.

Concurrency vs parallelism
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Concept of concurrency

Concurrency could be described as the application's ability to perform multiple tasks at the same time.  In other words, concurrency can be described as computer multitasking because it's about juggling between the tasks in computer programming. 

So, while driving the car, you need to check your speed, balance between the accelerator and brake pedals, and look at the mirrors. You’re dealing with those tasks at the same time but not necessarily all of them are in progress all the time. That’s what concurrency computing is about.

What you're actually doing here is constantly switching between the different tasks – this process is called concurrency. Concurrent sessions in programming mean that multiple activities are processed using the same central processing unit (CPU). 

The goal of those concurrent computing activities is to prevent the delay in their progress. When one activity is forced to wait, another can be successfully processed without affecting the other. 

Concurrency occurs when the device has one central processing unit and cannot process multiple tasks at the same moment. The logic behind the concurrent computing works like this:

Concurrent computing

But what happens when the computer is equipped with more than one central processing unit, or its CPU can handle multiple tasks simultaneously? It’s called parallel execution:

Parallel execution

In the above example we see two single core CPUs performing only one task each. But suppose we have two multi-core processors which can perform concurrent tasks. In such cases, multiple concurrent threads are processed by one CPU while another takes care of other concurrent tasks. This logic is called parallel concurrent execution.

Parallel concurrent execution

Concept of parallelism

Parallelism occurs when the application splits one task into smaller pieces and processes them in parallel. It can happen while using multi-core processors, which can handle several tasks simultaneously, at the same time.

Let’s get back to our journey here. You’re driving a car, and your friend sits on your side. You both need to drive safely and reach your destination on time, check the map, and ensure the good vibes. 

Those tasks can be done parallelly, allowing simultaneous ongoing progress. In this case, you and your friend are acting like two multi-core CPUs that divide tasks into smaller subtasks that can be performed at the same time. If driving the car is your main task, it can be divided into changing gear, keeping your eyes on the road, accelerating, and checking mirrors. Your friend’s subtasks are changing the volume of music, skipping songs you don’t like, checking maps, and leading the conversation. 

Here’s how parallelism looks in this situation:

Parallelism

How is concurrency related to parallelism?

Concurrency and parallelism are interrelated as an application can apply both approaches in computer programming. 

Concurrency itself provides the illusion of parallelism. Even though the tasks are in progress at the same time, they’re not performed simultaneously. It means that another action can start without finishing the previous, leaving it on hold but still in progress. 

Both forms of computation can occur when the multi-core processor works on several tasks concurrently while dividing them into subtasks to perform them in parallel execution.

What are the key differences between concurrency and parallelism?

The main difference is that concurrent computing deals with many tasks at the same time, while parallel computing does a lot of things simultaneously. The main differences between parallel and concurrent computing sessions are:

  • In concurrent computing sessions, several activities can be in progress, but the central processing unit might not be working on all of them at the same time. In parallel sessions, the central processing unit (CPU) takes care of several activities at once by dividing them into smaller subtasks.
  • In concurrent sessions, the CPU quickly switches between the tasks. During parallel processing, the multi-core CPU runs tasks at once.
  • A single CPU is used for concurrent sessions, while parallelism occurs when a multi-core CPU is in action.
  • Concurrency increases the quantity of work completed at a time, while parallelism speeds up the process by expanding bandwidth.

Concurrency can be combined with parallelism to ensure even better processing. You can also choose only one of them to apply to your web application or even neither. 

Plan name

Parallel

Non-parallel

Concurrent

Multiple tasks are carried out concurrently, while splitting each function into subtasks for parallel execution.

Multiple tasks are in progress, but not all of them are executed at the same time.

Non-concurrent

Several subtasks are performed on a multi-core processor simultaneously.

All the tasks are performed in a specific sequence.

Pros and cons of concurrency and parallelism

Like all the dope stuff in programming world, concurrency and parallelism also comes with some drawbacks:

Concurrency

+ Increases the number of tasks finished;

+ Increases resource utilization;

+ Reduces response time;

– Harder to spot computer programming error;

– Switching between apps causes additional performance overheads and complexity in operating systems;

– Running too many concurrent computing sessions might result in substantially decreased performance.

Parallelism

+ Improves the speed of tasks;

+ Solves big scale problems in a shorter time period;

+ Can reduce costs, since the tasks are completed in shorter time;

+ Optimizes multiple computing resources

— Parallel architecture is more difficult to understand;

— Requires more power due to multi-core CPUs being used;

– Harder to debug.

Concurrency and parallelism in scraping

Data gathering can be a time-consuming process, especially when the scraper is used for several scraping projects at the same time. Concurrency and parallelism could be possible solutions to lagging scraper problems caused by too many tasks processed simultaneously.

Scrapping delays usually happen during three scraping stages:

1) Gathering data from the target, 

2) Parsing

3) Data saving processes.

Especially if this sequence repeats many times.

If you are interested in search engine scraping, but don’t want to worry about the process, just grab our SERP Scraping API. With this solution, all you’ll need to do is configure the request correctly, and the rest is taken care of by us. You can forget about proxy management, scraping, and parsing because the data will be packed for you in a neat JSON format.

On the final note

The programming journey is smoother when you understand how concurrency and parallelism work and how to use them. And even if you feel that it isn’t stuff for you to dig deeper, you can always rely on us in your scraping journey. Just leave us a message and Smartproxy heroes will guide you anytime.

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Ella Moore

Ella’s here to help you untangle the anonymous world of residential proxies to make your virtual life make sense. She believes there’s nothing better than taking some time to share knowledge in this crazy fast-paced world.

Frequently asked questions

What are concurrency and parallelism in Python?

Python supports both concurrent computing and parallel programming for different use cases. Concurrency occurs in threading, coroutines, and async, while multiprocessing can support parallelism. 

Does parallelism imply concurrency?

Parallelism is built on dividing tasks into smaller subtasks that can be solved concurrently. Therefore, parallel communication always implies concurrent computing.

Can concurrency be achieved without parallelism?

Concurrency and parallelism are regarded as interchangeably in practice, especially in large-scale projects. Concurrency can work without parallelism when the progress is made in several different tasks concurrently, by constantly switching between tasks.