It seems it's optimized for scenarios with high performance compute heavy code, and short critical sections.
These assumptions may let it win benchmarks, but don't cover the use cases of all users. To illustrate why this is bad, imagine if you have a Mutex protected resource that becomes available after 10us on average. This locks spins 10 times checking if it has become available )(likely <1us) then yields the thread. The OS (lets assume Linux) wont wake it up the thread until the next scheduler tick, and its under no obligation to do so even then (and has no idea it should). But even best-case, you're left waiting 10ms, which is a typical scheduler tick.
In contrast OS based solutions are expensive but not that expensive, let's say that add 1us to the wait. Then you would wait 11us for the resource.
A method call taking 10ms and one taking 15 us is a factor of 60x, which can potentially kill your performance.
You as the user of the library are implicitly buying into these assumptions which may not hold for your case.
There's also nothing in Rust that protects you from deadlocks with 100% certainty. You can fuzz them out, and use helpers, but you can do that in any language.
So you do need to be mindful of how your mutex works, if you want to build a system as good as the one it replaces.
How would you handle the archetypical example of a money transfer between two bank accounts, in which 100 units of money need to be subtracted from one account and atomically added to another account, after checking that the first account contains at least 100 units?
and the way to do it is to either have some kind single-point-of-control (designated actor or single-threaded executor) or mark the data (ie. use some concurrency control primitive either wrapping the data or in some dedicated place where the executors check [like JVM's safepoints])
using consistent hashing these hypothetical accounts could be allocated to actors and then each transaction is managed by the actor of the source (ie. where the money is sent from, where the check needs to happen), with their own durable WAL, and periodically these are aggregated
(or course then the locking is hidden in the maintenance of the hashring as eating philosophers are added/removed)
Distributing accounts among different actors, without two-phase commit or its moral equivalent, enables check kiting.
In your case, you could have a channel where the Receiver is the only part of the code that transfers anything. It'd receive a message Transfer { from: Account, to: Account, amount: Amount } and do the required work. Any other threads would therefore only have copies of the Sender handle. Concurrent sends would be serialized through the queue's buffering.
I'm not suggesting this is an ideal way of doing it
The actor model reaches its limits as soon as you need transactions involving two or more actors (for example, if you need to atomically operate on both the customers actor and the bank accounts actor). Then you can either pull all involved concerns into a single actor, effectively giving up on concurrency, or you can implement a locking protocol on top of the actor messages, which is just mutexes with extra steps.
No single concurrency primitive covers all use cases. I was addressing your misconceptions about mutex performance and overhead, not whether mutexes are the best solution to your particular problem.
> […] it has no way of getting notified when the mutex gets unblocked […] The OS (lets assume Linux) wont wake it up the thread until the next scheduler tick, and its under no obligation to do so even then (and has no idea it should).
You've misunderstood the parking_lot implementation. When thread B tries to lock a mutex that's currently locked by thread A, then, after spinning a few cycles, thread B "parks" itself, i.e., it asks the kernel to remove it from the Runnable task queue. On Linux, this is done using the futex syscall. When thread A unlocks the mutex, it detects that another thread is waiting on that mutex. Thread A takes one thread from the queue of waiting threads and "unparks" it, i.e., it asks the kernel to move it into the Runnable task queue. The kernel is notified immediately, and if there's a free CPU core available, will tend to dispatch the thread to that core. On a non-realtime OS, there's no guarantee how long it takes for an unblocked thread to be scheduled again, but that's the case for all concurrency primitives.
667 (a thousand 15μs calls take 15ms)
Uhh no, everyone in Linux userspace uses futexes these days to wait on a contended lock.
https://github.com/Amanieu/parking_lot/blob/03d36d62fecbd85c...
1) the mutex byte/word is more likely to be in the same cache line as the data you want to access anyway, and
2) different threads are more likely to write to mutex bytes/words in different cache lines, whereas in coarse-grained locking, different threads will fight for exclusive access over the cache line containing that one, global mutex.
@magicalhippo: Since I'm comment-rate-throttled, here's my answer to your question:
Typically, you'd artificially increase the size and alignment of the structure:
#[repr(align(64))]
struct Status {
counter: Mutex<u32>,
}
If you got small objects and sequential allocation, that's not a given in my experience.
Like in my example, the ints could be allocated one per thread to indicate some per thread status, and the main UI thread wants to read them every now and then hence they're protected by a mutex.
If they're allocated sequentially, the mutexes end up sharing cache lines and hence lead to effective contention, even though there's almost no "actual" contention.
Yes yes, for a single int you might want to use an atomic variable but this is just for demonstration purposes. I've seen this play out in real code several times, where instead of ints it was a couple of pointers say.
I don't know Rust though, so just curious.
And allocating the int contiguously might actually be the right solution is the cost of sporadic false sharing is less than the cost of wasting memory.
There's no silver bullet.