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Concurrency is how a program organizes multiple tasks so they can make progress during overlapping periods; parallelism is when multiple computations actually run at the same time. One cook switching between dishes illustrates concurrency. Two cooks working simultaneously illustrate parallelism—if the work and tools let them.
What concurrency means
Concurrency is a way of structuring work into independently progressing tasks. The tasks may take turns rather than run at the exact same moment. A single cook might start a pot simmering, chop vegetables while it cooks, then return to the pot when it needs attention. Several tasks are in progress, but the cook performs only one action at a time.
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In programming, a concurrent program similarly gives distinct tasks opportunities to make progress over overlapping periods. On a single processor, the system can switch among them. Concurrency is therefore possible without simultaneous execution.
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Parallelism means computations execute simultaneously. In the kitchen analogy, two cooks prepare different parts of a meal at once. Andrew Gerrand, writing on the Go Blog, puts 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: “Concurrency is not parallelism”
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The analogy is a useful starting point, not a complete model: real programs must also contend with scheduling, dependencies, shared resources, and the cost of coordinating tasks.
Concurrency and parallelism compared
| Question | Concurrency | Parallelism |
|---|---|---|
| What does it describe? | How independent tasks are structured and can make progress | Whether multiple computations execute simultaneously |
| Must work happen at the same instant? | No | Yes |
| Kitchen example | One cook switches among dishes as tasks wait or become ready | Multiple cooks work at once, when tasks and resources allow |
| What makes it possible? | Program design that allows tasks to progress independently | Execution resources and work that can use them at the same time |
| Performance caveat | Organizing tasks this way does not guarantee a speedup | Dependencies and coordination can limit or erase gains |
Go’s Effective Go documentation also warns against treating the terms as interchangeable: concurrency concerns program structure, while parallelism concerns executing calculations in parallel, often to use multiple CPUs.
Why more CPUs may not make a program faster
Extra processors help only when a program has independent work that can be divided among them. If one step must finish before the next can begin, adding CPUs does not make that sequential work happen simultaneously.
Even when work can be divided, tasks need to coordinate and sometimes communicate. Those costs use time and resources. If the overhead outweighs the useful work gained from simultaneous execution, using multiple operating-system threads can make a program slower rather than faster. The Go FAQ discusses these limits and asks directly, “Why doesn’t my program run faster with more CPUs?”
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What this means for Go programs
Go provides concurrency primitives, including goroutines, for structuring independently progressing work. Their presence does not by itself show that useful computations are running in parallel. As the Go FAQ explains, concurrency enables parallelism only when the underlying problem is intrinsically parallel.
When considering whether more CPUs can help, ask two questions:
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- Can the work be separated into computations that do not have to wait on one another?
- Will the time spent coordinating or communicating remain small relative to the useful work performed?
If the first answer is no, the work is fundamentally sequential. If the second is no, the overhead may consume the gains. Neither concurrency nor a higher CPU count guarantees a particular speedup.
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