Mutable shared state is a feature, not a bug.

It's true that it's easier to write correct async code using immutable shared data or unshared data.

However, it's very hard if not impossible to do fast and low memory concurrent algorithms without mutable shared state.

Idk, it's a general rule of thumb that the more mutable shared state an algorithm has, the worse it scales. So if you're trying to scale something to be concurrent, mutable shared state is an antipattern.

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PaulDavisThe1st 32 minutes ago | parent | next [-]

as noted someone else, it is lock contention that doesn't scale, not mutable shared state. lock-free data structures, patterns like RCU ... in many cases these will scale entirely appropriately to the case at hand. A lot of situations that require high-scale mutable shared state have an inherent asymmetry to the data usage (e.g. one consumer, many writers; many consumers; one writer) that nearly always allow a better pattern than "wrap it in a mutex".

	▲	loeg 15 minutes ago \| parent [-]
		No, it's the mutable shared state that is the problem. Lock contention is just downstream of the same problems as any other mutable shared state. > patterns like RCU RCU isn't mutable shared state! It's sharing immutable state! That's the whole paradigm.

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cogman10 3 hours ago | parent | prev [-]

It's lock contention that slows things down more than anything.

But it's really an 'it depends' situation.

The fastest algorithms will smartly divide up the shared data being operated on in a way that avoids contention. For example, if working on a matrix, then dividing that matrix into tiles that are concurrently processed.

	▲	loeg an hour ago \| parent [-]
		> It's lock contention that slows things down more than anything. It's all flavors of the same thing. Lock contention is slow because sharing mutable state between cores is slow. It's all ~MOESI. > The fastest algorithms will smartly divide up the shared data being operated on in a way that avoids contention. For example, if working on a matrix, then dividing that matrix into tiles that are concurrently processed. Yes. Aka shared nothing, or read-only shared state.