3 ms·

A distinction without meaning is kind of dumb. In popular parlance parallelism and concurrency are approximately the same thing.

Jtsummers 11y ago
You aren't wrong, but it does make it difficult to discuss with people. At least within the programming field it's useful to have two terms with distinct meanings to discuss the topic. Consider the terms accuracy and precision [0], where again the popular understanding is that they're (essentially) synonymous. But within the scientific and engineering world that uses the terms the distinction is critical. EDIT: Verification and validation [1] are another pair of terms where the distinction is important for industry, but popular understanding often mixes them up. [0] http://en.wikipedia.org/wiki/Accuracy_and_precision http://en.wikipedia.org/wiki/Accuracy_and_precision [1] http://en.wikipedia.org/wiki/Verification_and_validation http://en.wikipedia.org/wiki/Verification_and_validation
enneff 11y ago
When having a technical discussion we should interpret technical terms with their precise meaning. Rob Pike gave a good talk about this: http://blog.golang.org/concurrency-is-not-parallelism http://blog.golang.org/concurrency-is-not-parallelism
bkeroack 11y ago
It wasn't very long ago (before the multicore era) that essentially nothing running on personal computers was truly parallel. Yet multithreaded applications (concurrency) worked just fine.
Igglyboo 11y ago
It does have meaning. An obvious difference is that you can have concurrency with one processor but not parallelism requires at least two.
- chrisseaton 11y ago
  > parallelism requires at least two Interestingly there is one example of parallelism without concurrency and with just one processor and just one core - vector instructions. Although it would be a breathtakingly good compiler that could emit vector instructions to schedule two goroutines in parallel
jlouis 11y ago
The advantage of making the distinction is you can discuss the subtly different semantics which often arise in the area of parallel, concurrent and distributed computing. If you equate them, you can only discuss with limited level of detail, which makes it harder to understand the subtle differences coming from different computational models. For instance, the subtle difference arising from MIMD execution and SIMD execution. The former allows for concurrent execution, whereas the latter does not. However, both are parallel execution models. This isn't theoretical either as GPUs are strictly SIMD machines in their execution.
pron 11y ago
I agree that if you look at it simply as the number of cores used, then the distinction is not very interesting. But a better way to look at it is as follows: * Parallelism is a feature of the algorithm, chosen to make it run faster. E.g. you can use a parallel algorithm to factor a matrix. From a technical standpoint, the challenge of parallelism is how to deconstruct the problem so that each processing unit will be able to take on a share of the effort -- parallelism implies cooperation. * Concurrency is a feature of the problem. E.g. many users using Facebook at once. If at any one time you have 10K requests, then your concurrency level is 10K, no matter how many cores are used. The challenge of concurrency is how to allocate computing resources among the competing requests -- concurrency implies competition. Parallelism is orderly; concurrency is messy. Parallelism is a choice; concurrency isn't.

Jtsummers 11y ago

You aren't wrong, but it does make it difficult to discuss with people. At least within the programming field it's useful to have two terms with distinct meanings to discuss the topic. Consider the terms accuracy and precision [0], where again the popular understanding is that they're (essentially) synonymous. But within the scientific and engineering world that uses the terms the distinction is critical. EDIT: Verification and validation [1] are another pair of terms where the distinction is important for industry, but popular understanding often mixes them up. [0] http://en.wikipedia.org/wiki/Accuracy_and_precision http://en.wikipedia.org/wiki/Accuracy_and_precision [1] http://en.wikipedia.org/wiki/Verification_and_validation http://en.wikipedia.org/wiki/Verification_and_validation

enneff 11y ago

When having a technical discussion we should interpret technical terms with their precise meaning. Rob Pike gave a good talk about this: http://blog.golang.org/concurrency-is-not-parallelism http://blog.golang.org/concurrency-is-not-parallelism

bkeroack 11y ago

It wasn't very long ago (before the multicore era) that essentially nothing running on personal computers was truly parallel. Yet multithreaded applications (concurrency) worked just fine.

Igglyboo 11y ago

It does have meaning. An obvious difference is that you can have concurrency with one processor but not parallelism requires at least two.

chrisseaton 11y ago
> parallelism requires at least two Interestingly there is one example of parallelism without concurrency and with just one processor and just one core - vector instructions. Although it would be a breathtakingly good compiler that could emit vector instructions to schedule two goroutines in parallel

jlouis 11y ago

The advantage of making the distinction is you can discuss the subtly different semantics which often arise in the area of parallel, concurrent and distributed computing. If you equate them, you can only discuss with limited level of detail, which makes it harder to understand the subtle differences coming from different computational models. For instance, the subtle difference arising from MIMD execution and SIMD execution. The former allows for concurrent execution, whereas the latter does not. However, both are parallel execution models. This isn't theoretical either as GPUs are strictly SIMD machines in their execution.

pron 11y ago

I agree that if you look at it simply as the number of cores used, then the distinction is not very interesting. But a better way to look at it is as follows: * Parallelism is a feature of the algorithm, chosen to make it run faster. E.g. you can use a parallel algorithm to factor a matrix. From a technical standpoint, the challenge of parallelism is how to deconstruct the problem so that each processing unit will be able to take on a share of the effort -- parallelism implies cooperation. * Concurrency is a feature of the problem. E.g. many users using Facebook at once. If at any one time you have 10K requests, then your concurrency level is 10K, no matter how many cores are used. The challenge of concurrency is how to allocate computing resources among the competing requests -- concurrency implies competition. Parallelism is orderly; concurrency is messy. Parallelism is a choice; concurrency isn't.