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Comment by ahelwer | Hacker News Reader

That battle has unfortunately been lost and different sources give different definitions, often exactly swapped. This was discussed in one of the HN posts linked in the article: https://news.ycombinator.com/item?id=36318280

In the end I don't think it is too much of an issue. What confusion is really brought by conflating parallelism and concurrency? Sure, concurrent programs can be serialized onto a single core (that's how deterministic simulation testing implementations like Antithesis and record & replay implementations like Mozilla's rr operate). But there isn't some deep conceptual unlock you get by having a strict conceptual boundary between concurrency and parallelism.

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I think there is a deep conceptual unlock: concurrency is about semantics, whilst parallelism is an operational property. I use this distinction a lot in my own work. Concurrent programming primitives are inherently non-deterministic (and usually about handling non-deterministic events), on top of which we must then establish some kind of properties (sometimes determinism to some extent). Many interesting parallel operations are however completely deterministic, and the fact that they are parallel is a property of their assigned cost model (and hopefully implementation, in practice).

I agree that this distinction is hardly universal, but it seems to be growing increasingly established, and I think it is worth fighting for it.

I don't like the essential characteristic of concurrency being nondeterminism. its really that multiple processes are running concurrently. if we don't have serializing operations, we have arbitrary execution order. but if we do then we can introduce the necessary determinism while still being (largely) concurrent in evaluation. and if those logically concurrent processes are physically concurrent then we have parallelism. so the first is necessary but not sufficient for the latter.

so I find saying that we have one or the other to pretty misleading.

deterministic scheduling is possible, but most theoretical concurrency models assume non-determinism.

And even with deterministic scheduling, concurrency might be dictated by external stimuli (for example request arrival) that are not deterministic.

Im not saying arrival order isn't a key consequence of concurrency in many cases, it's just not the same as concurrency itself. I guess the point for me is that when we're programming or when we're using models, we define the partial ordering. so if an event arrives from outside and causes a message to be put in the queue, the read of that event by another thread is still after the external event.

so our job is really to kind of look at all the possible topological sorts of that 'after' ordering, and ensure that they are all correct, and if not, add additional edges by using locks or whatever mechanism.

kind of more interested are techniques like mvcc and crdt, which make _any_ causal ordering of events (topo sort) result in a meaningful answer.

but if you look at classical simd for example, we have concurrency (and parallelism) without additional constraints, because the threads are strongly synchronized at the hardware level.

I would say that classical simd with explicit vector registers is plain parallelism, not concurrency. You can build concurrent abstractions on top of it (ISPC, any of the high level GPU languages), but when you move beyond simple simd hardware and have multiple hardware threads executing simd groups on multiple cpus, possibly with the ability to migrate threads between groups, the strong synchronization is a bit lost. But I'll admit I'm not an expert.

It's really important to get people to recognize that concurrency can happen on a single core or a single task-switching thread. You don't necessarily need to split off parallelism to explain that, but it helps.

And it's worth talking about how you can have a single task run in a parallel way, for varying strictness of 'single'.

Coroutines and SIMD are far enough apart that their execution models should have different words.

Personally I think it's not a good idea to think too rigidly about and try to draw a huge distinction between the two. They're on a continuum and sometimes I'd say some things aren't even strictly speaking "between" them either. Sitting down and trying to classify code into "parallel" and "concurrent" is as likely to do harm as to do any good.

I firmly disagree, they are not a continuum, they are binary properties of what they describe. Code can have concurrency primitives, but parallelism primitives must necessarily come from the environment the code executes in, whether it's multiple code streams on a multicore processor or process parallelism provided by an operating system. Programs that are parallel are necessarily also concurrent (if they must communicate between parallel executions), but the inverse is not necessarily true.

If this distinction wasn't important, Python's infamous GIL would not be an issue.

I completely agree. Even in classic Python with GIL, the programmer would still have to understand concepts from concurrent programming. The asyncio package has to provide types such as Lock, Condition, Semaphore, even when it uses just one thread. The threading package on the other hand uses OS threads, and yet it provides its own version of types such as Lock, Condition, Semaphore, even if it is protected by the Python GIL. The GIL is preventing meaningful parallelism in Python, but it remains the programmer’s responsibility to use concurrency tools correctly.

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