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Concurrency is how a program organizes independent tasks so they can make progress during overlapping periods; parallelism is when computations actually run at the same time. One cook switching among dishes illustrates concurrency. Two cooks doing separate work simultaneously illustrate parallelism. A program can be concurrent without being parallel, and adding CPUs helps only when the work and its coordination costs allow useful parallel execution.
What concurrency and parallelism mean
Andrew Gerrand’s Go Blog draws the distinction this way: “In programming, concurrency is the composition of independently executing processes, while parallelism is the simultaneous execution of (possibly related) computations.” (Go Blog, January 16, 2013.)
Concurrency describes the structure of work: separate tasks can progress over overlapping periods. Parallelism describes execution: two or more computations run at the same time. The ideas are related, but they are not interchangeable.
How the kitchen analogy works
One cook: concurrency
Imagine one cook preparing several dishes. The cook starts something that needs time to simmer, moves to another task while it waits, then returns when it is ready. Several tasks are in progress, but the cook is not performing two actions at once. That is concurrency without parallelism.
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Two cooks: parallelism
Now imagine two cooks doing separate tasks at the same time. That is parallel execution, provided the work and available resources let them proceed independently. The analogy is useful for remembering the difference, but it is not a complete model of how computers schedule work or how tasks compete for resources.
How the distinction applies to programs
The Go documentation describes concurrency as structuring a program around independently executing components, and parallelism as executing calculations in parallel for efficiency on multiple CPUs (Effective Go). A concurrent program may take turns on a single processor; its tasks can still be structured to make progress independently even though they are not running simultaneously.
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Parallelism requires both execution capacity and work that can use it. Multiple CPUs cannot make intrinsically sequential work happen at once. Dependencies may require one step to finish before another can begin.
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Why more CPUs may not make a program faster
More execution resources help only if the problem can be divided into computations that can run independently. Even then, the work of coordinating and communicating between those computations consumes resources. If that overhead outweighs the useful work done concurrently, using multiple operating-system threads can make a program slower rather than faster.
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The Go FAQ addresses the question “Why doesn’t my program run faster with more CPUs?” Its practical answer is to consider whether the problem is intrinsically parallel and whether synchronization or communication costs are small enough for parallel execution to help (Go FAQ). The number of CPUs alone does not establish a speedup.
Quick comparison
| Question | Concurrency | Parallelism |
|---|---|---|
| What does it describe? | How independent tasks are structured and make progress | Whether multiple computations execute simultaneously |
| Must tasks run at the exact same time? | No | Yes |
| Kitchen example | One cook switches among dishes as work waits or becomes ready | Multiple cooks work at once when the work and resources permit |
| What does it depend on? | A design with independently progressing tasks | Execution resources and work that can use them |
| Performance caveat | Organizing work into tasks does not guarantee a speedup | Dependencies, synchronization, and communication can limit or erase gains |
What this means for Go
Go offers concurrency primitives, but using goroutines or another concurrent structure does not by itself show that useful computations are executing in parallel. Concurrency can enable parallelism when the problem can be divided appropriately; whether that improves performance depends on the workload and the cost of coordination.
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