Swift does not use virtual machine and garbage collection, it competes more to c++ and rust and if Apple is serious about pushing it cross platform that's definitely a welcome move, in fact, I can't wait even though I have never programmed in swift. the main point is that, it's memory safe, and seems much easier to code than rust.
Reference counting is garbage collection, and it performs significantly worse from a throughput perspective than tracing GC, which is possibly the most common metric for web server type workloads.
It really is not nitpicking, we should just really use tracing GC when we mean it.
There is kotlin native, which generates native code, using the same llvm that c++, rust and swift use. It doesn't have to use virtual machine, it is just one of targets.
not sure if it is 'production ready' and how does its performance/size go comparing to c++/rust/swift, in the end though, it's the ecosystem that matters.
Nope; no having to pause execution and clean up. Miguel de Icaza (the creator of Mono) explicitly mentions this as one of Swift's key strengths over GC languages like C# during a talk about Swift at GodotCon 2023: https://www.youtube.com/watch?v=tzt36EGKEZo&t=7s&pp=ygURc3dp...
Maybe he should then read a book on garbage collectors that all start with ref counting..
Also, is it “pause execution and clean up” together? As ref counting obviously has to clean up, that’s the whole point - and it actually does so by blocking the mutator thread (the actual program written by the user). Then we didn’t even get to the point where syncing counters across threads are possibly the slowest primitive operation a CPU can do, so if we can’t know that an object will only ever be accessed from a single thread, ref counting has plenty shortcomings. Oh also, nulling the counter in case of a big object graph will pause execution for considerable amount of time (particularly noticeable in case of a c++ program exiting which uses a bunch of shared ptrs)
Perhaps? Most scenarios that explicitly involve .NET's GC vs Swift's ARC display much better performance of the former, to the point where the fact that ARC does not have GC pauses does not help if the whole things is multiple times slower, in many ways it's like Go's """low-pause""" GC design discussions that completely ignore allocation throttling and write barrier cost.
Swift lacking proper performant GC is a disadvantage. Upcoming features solve it by likely enabling more scenarios to sidestep ARC, but their impact on the Swift as a whole, and user applications that use them, is yet to be seen.
It's important to always remember - there's no free lunch.
I'm sad that Miguel de Icaza seems to have a bone to pick with C# nowadays, but it's not surprising given Xamarin story.
> Perhaps? Most scenarios that explicitly involve .NET's GC vs Swift's ARC display much better performance of the former
By which you mean "less CPU cycles on a desktop machine with plenty of memory"?
That's not when ARC is more performant; it's better on smaller devices that are under memory pressure and have swapped out some of your memory. In which case you have to swap it back in to go scan for pointers. And if you're a low-priority daemon then you evict higher priority pages in the process.
Perhaps? You assume GC takes unreasonably more space. It's purely a function of a tradeoff between running it more frequently, tuning heap sizing algortithms, choosing to run them as part of allocation calls on the same thread, sacrificing throughput in the process. GC can be more compact than what you assume. Modern good GC implementation are precise and don't have to mark dead GC roots as live, even within a scope of a single method. .NET and I assume Java GC implementations work this way - that's what "precise" means in "precise tracing GC".
Man the term must have changed since I was in school; i thought garbage collection was a much more general concept than a specific tactic to achieve this end of automatic memory collection. Pity, it was a useful term.
It's worth noting many others also consider automatic reference counting to be a form of gc, albeit one with different strengths and weaknesses than stack- and heap-scanning varieties
Which is reference counting, not garbage collection. Ref counts free when count = 0. Garbage collection scans all object pointers and looks for loops / no missing pointers.
Reference counting is not tracing garbage collection. To also quote a Wikipedia Link:
„The main advantage of the reference counting over tracing garbage collection is that objects are reclaimed as soon as they can no longer be referenced, and in an incremental fashion, without long pauses for collection cycles and with clearly defined lifetime of every object.“
Of course reference counting is not tracing garbage collection. I never said it was. The comment I replied to claimed reference counting was not garbage collection at all and seemed to think tracing garbage collection was the only kind of garbage collection. Reference counting and tracing garbage collection are two different types of garbage collection.
binary-trees is almost completely dominated by the time spent in allocator code, and stresses its throughput. This benchmark showcases how big of a gap is between manual per-thread arenas, then tracing generational multi-heap GCs, then ARC and more specialized designs like Go GC. Honorable mention goes to BEAM which also showcases excellent throughput by having process-level independent GCs, in this case resembling the behavior of .NET's and OpenJDK GC implementations.
A tree is indeed a bad fit for RC; so is anything else where you have multiple references to something but know there is a single real owner.
I'd suggest keeping strong references to all the tree nodes in an array, then having everything within the tree be unowned. Basically fake arena allocation.
is a common way you see toy data structures code written, but it's inefficient (because pointer chasing is slow) and there's better patterns. If you use the arena method above, you could use indexes into the arena. If not, intrusive data structures (where the references are inside Node instead of inside Tree) are better.
Pointer chasing is irrelevant here. It takes <= 15% of the execution time, and CPUs have gotten good at it. If it takes more - it speaks more about the quality of the allocator which has poor memory locality. As noted in my previous comment, it is dominated by the time spent in the allocator/GC code.
The requirement to implement the same algorithm with the same data structure is what makes this benchmark interesting and informative. Don't tell me "allocate parent object A, allocate child objects C, D and E and assign them to A's fields, then allocate array F and G, and assign them to D's fields" isn't the bread and butter of all kinds of application logic, something that this benchmark stresses.
It has nowhere near the performance characteristics of those languages. It could, but it doesn’t. Look up a variety of language benchmarks. It’s typically ranked around Python/Javascript. You can get as fast as C but the code is very atypical.
Shows a huge difference, as expected for a typed memory-safe compiled language using LLVM versus an interpreted language with a global interpreter lock.
The thread you just posted has a bunch of posts indicating this was not the actually the same program in Python and Swift; further, the Swift version was written poorly. Plus, the graph in the final post shows whatever Swift version someone ran tests on as much faster than Python.
Edit: Kotlin is perfectly fine for _just_ web-servers, Vert.X is great. On systems programming, read below:
All JVM languages are not viable by definition for this domain. Object oriented and heavily abstracted nature of the underlying runtime implementations prevents their effective usage in systems programming due to lack of fast FFI, structs, particularly so of custom layout, and the historical aversion of the ecosystem to low-level features.
Kotlin native does not count because presently it has 0.1-0.001x performance of OpenJDK, it is that bad, and I assume is subject to the common subset of features that must also be expressible with JVM.
.NET, especially with compilation to native statically linked binaries (NativeAOT) is an option, and I believe, due to ecosystem maturity as well as very heavy focus on performance in all recent .NET versions as well as continued improvement of low-level features (portable SIMD, byref pointers with simple lifetime analysis, static linking with C/C++/Rust/etc.), it is a strong contender. Unlike Java, C# has great systems programming story, after all, it was influenced as much by C++ as it was by Java, sadly many only ever think about the latter.
However, I'm looking forward to Swift 6. Once it is out, I'd love to see it offer more opportunities at ensuring static dispatch and generic monomorphization (in .NET, generics with struct arguments are always monomorphized like in Rust, so you have tools for zero-cost abstractions) and happy paths allowing to bypass prohibitive cost of ARC with new annotations. By using LLVM, Swift has theoretically great performance ceiling, even if it does not deliver on it just yet, losing to C# by a good margin on the more complicated code due to ARC and dynamic dispatch. But because Apple seems to be invested in using it for tasks that will require addressing these shortcomings, it is pretty exciting to see where they will take it.
Isn't Kotlin based on JVM and Swift is natively compiled? That's a pretty significant difference and I'm not aware of any "to native" compiler for Kotlin like the NativeAOT approach exists for .NET...
There are in fact two "to native" compilers for Kotlin, the one for Kotlin only is called Kotlin Native but you can also use graalvm native-image to compile any JVM language to native.