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.
- Concurrency: Like a single cashier serving multiple customers by switching between them very quickly (switching between tasks)
- Parallelism: Like multiple cashiers serving multiple customers at the same time (tasks running truly at the same time)
Concurrency
Concurrency refers to handling multiple tasks by sharing a single processing resource, without executing them truly simultaneously. It improves system responsiveness by creating an illusion of parallelism.
- Tasks execute in overlapping time periods, making progress without waiting for others to complete.
- A processor switches between tasks (context switching), commonly used in systems requiring high responsiveness like handling multiple user requests.
Example: A single-core CPU running multiple threads: the CPU rapidly switches between threads so each makes progress.
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.
Note: 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 executing multiple tasks simultaneously using multiple processing units. It improves system throughput and computational speed by dividing work across processors.
- Tasks are split into smaller subtasks that run in parallel on separate cores or processors.
- Focuses on true simultaneous execution, commonly used in data processing and high-performance applications.
Example: A quad-core CPU running four threads: each thread assigned to a separate core and executed truly in parallel.
This diagram shows parallelism where a task is divided into smaller parts (P1 to P5) and each part is processed at the same time on different processors.
Instead of taking 5 minutes on a single processor, all parts run simultaneously and complete in just 1 minute.
Note: Parallelism increases speed by executing multiple tasks (CPU and I/O) at the same time across different processes.
Concurrency improves speed by overlapping I/O of one process with CPU work of another, without true simultaneous execution.
Concurrency Vs Parallelism
| Concurrency | Parallelism |
|---|---|
| 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. |