I would probably recommend the art of multiprocessor programming (herlihy and shavitz) as a good starting point for concurrent programming. There is also " A primer on memory consistency and cache coherence" (Nagarajan et al) if you want to get more into the interaction between memory consistency and coherence.
That's a useful interpretation of the two terms, but it's far from universal and the two have often been used fairly interchangeably over the decades. It's been much more useful as a distinction when someone discussing it announces that that is how they're separating the two concepts, instead of trying to force other people to adopt that particular pair of definitions.
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.
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 would probably recommend the art of multiprocessor programming (herlihy and shavitz) as a good starting point for concurrent programming. There is also " A primer on memory consistency and cache coherence" (Nagarajan et al) if you want to get more into the interaction between memory consistency and coherence.
Should have been titled "Is Parallel Programming What Can You Hard, And, If So, Do About It?"
world! hello
I do agree with your comment.
not not
This review seems to equate parallelism and concurrency as the same thing and they are not.
As I understand it, the parallelism is about task execution and concurrency is about task structure. Or, as Rob Pike said:
"Concurrency is about dealing with lots of things at once. Parallelism is about doing lots of things at once."
He said that in his Concurrency is not Parallelism talk.
That's a useful interpretation of the two terms, but it's far from universal and the two have often been used fairly interchangeably over the decades. It's been much more useful as a distinction when someone discussing it announces that that is how they're separating the two concepts, instead of trying to force other people to adopt that particular pair of definitions.
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.
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.
Parallel programming is a great application for LLM correctness proofs in Lean.
You can't unit test your way out, but if you care about the code's correctness, today there's a way.
Mix of different types of tests helps.
Best examples are SQLite and Jepsen test suites for dbms engines.
https://jepsen.io/
Nope! Parallel programming is all yahoo, wee, look at that go!
Then comes the parallel debugging.
Pretty soon it's 15 years later, different person, yahoo-wee bro having long moved on.