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Difference between Concurrency and Parallelism

Last Updated : 07 Aug, 2025
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Concurrency and Parallelism are foundational concepts in computer science, especially in multithreading and distributed systems. While they sound similar, they refer to different ways of managing multiple tasks. Understanding their distinction is crucial for designing efficient, scalable applications.

Concurrency: Like a single cashier serving multiple customers by switching between them very quickly.
Parallelism: Like multiple cashiers serving multiple customers at the same time.

Concurrency

Concurrency means dealing with multiple tasks at once, but not necessarily executing them simultaneously. Instead, tasks make progress by sharing time on the same processing resource. Concurrency is an approach that is used for decreasing the response time of the system by using the single processing unit. It creates the illusion of parallelism.

Key Characteristics:

  • Tasks start, run and complete in overlapping time periods.
  • A single processor may switch between tasks quickly (context switching), giving the illusion of simultaneous execution.
  • Common in environments where responsiveness is important (e.g., handling multiple user requests).
concurrency_in_os2
Concurrency

Here, first Task 1 is executing then it went to I/O stage, during this Task 2 starts executing and then it also went to I/O stage and then task 3 and so on. Finally task 1 finish its I/O stage and starts executing remaining part, Task 2 follows it and then Task 3.

Example: A single-core CPU running multiple threads: the CPU rapidly switches between threads so each makes progress.

Concurrency is achieved through the interleaving operation of processes on the central processing unit (CPU) or in other words by the context switching. It increases the amount of work finished at a time.

Parallelism

Parallelism refers to actually executing multiple tasks at the same time on multiple processing units. Here  tasks are divided into smaller sub-tasks that are processed seemingly simultaneously or parallel. It is used to increase the throughput and computational speed of the system by using multiple processors. 

Key Characteristics:

  • Tasks are divided into subtasks that run simultaneously on separate cores or processors.
  • Mainly focuses on performance improvements via true simultaneous execution.
  • Often used for data processing, scientific computation and high-performance applications.
Parallelism
Parallelism

Example: A quad-core CPU running four threads: each thread assigned to a separate core and executed truly in parallel.

Parallelism leads to overlapping of central processing units and input-output tasks in one process with the central processing unit and input-output tasks of another process. Whereas in concurrency the speed is increased by overlapping the input-output activities of one process with CPU process of another process. 

Concurrency v/s Parallelism

ConcurrencyParallelism
Concurrency is the task of running and managing the multiple computations at the same time.While parallelism is the task of running multiple computations simultaneously.
Concurrency is achieved through the interleaving operation of processes on the central processing unit(CPU) or in other words by the context switching.While it is achieved by through multiple central processing units(CPUs).
Concurrency can be done by using a single processing unit.While this can't be done by using a single processing unit. it needs multiple processing units.
Concurrency increases the amount of work finished at a time.While it improves the throughput and computational speed of the system.
Concurrency deals with a lot of things simultaneously.While it does a lot of things simultaneously.
Concurrency is the non-deterministic control flow approach.While it is deterministic control flow approach.
In concurrency debugging is very hard.While in this debugging is also hard but simple than concurrency.

Why Does This Matter?

Understanding concurrency vs. parallelism helps you:

  • Choose the right design for performance and scalability.
  • Avoid common pitfalls like race conditions.
  • Leverage system resources effectively.

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