Parallelism and Concurrency are two concepts in computer science that are often confused but represent different approaches to handling tasks. Let’s break down the differences:
1. Concurrency:
- Definition: Concurrency refers to the ability of a system to handle multiple tasks by interleaving their execution. The tasks can start, run, and complete in overlapping time periods.
- Execution: In concurrency, tasks may or may not run simultaneously. Instead, they are interleaved so that multiple tasks progress. The system switches between tasks (threads or processes) to give the appearance of parallel execution.
- Use Case: Useful when you have multiple tasks that can be performed independently but share a resource (e.g., CPU or I/O), such as in web servers or user interface applications.
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Example: In a single-core processor, concurrency allows multiple threads to run by switching between them frequently. Each thread may perform a part of its task before switching to another thread.
Analogy: Imagine you’re at a restaurant, and the waiter is taking orders from multiple tables. They don’t handle one table at a time from start to finish but instead switch between tables, taking and delivering orders. The waiter does one thing at a time but handles multiple tasks by alternating between them.
2. Parallelism:
- Definition: Parallelism refers to actually executing multiple tasks simultaneously. In parallelism, tasks are broken down into subtasks that can be executed at the same time on multiple processors or cores.
- Execution: Parallelism requires multiple physical processing units (like multiple cores in a CPU or different processors) to execute tasks at the same time.
- Use Case: Ideal for tasks that can be broken down into smaller independent units that can run at the same time, like scientific simulations, video processing, or distributed computing tasks.
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Example: On a multi-core processor, parallelism allows several threads to execute simultaneously on different cores.
Analogy: Imagine the same restaurant with multiple waiters. Each waiter serves one table at a time, but now multiple tables can be served simultaneously by different waiters.
Key Differences:
| Feature | Concurrency | Parallelism |
|---|---|---|
| Execution | Tasks are interleaved (may not run at the same time). | Tasks run simultaneously (at the same time). |
| Processor usage | Can work on a single processor (time-sharing). | Requires multiple processors or cores. |
| Goal | Handle multiple tasks by switching between them. | Speed up execution by dividing tasks. |
| Example | A web server handling multiple users’ requests by switching between them. | Dividing a big matrix computation across multiple cores. |
| Requirement | Works even with a single-core CPU. | Needs a multi-core or distributed system. |
Summary:
- Concurrency is about dealing with lots of things at once, while parallelism is about doing lots of things at once.
- Concurrency involves structuring the program to handle multiple tasks by switching between them (even on a single core), while parallelism involves running multiple tasks simultaneously on multiple cores.